GPS World

Category: Research & Development

  • Innovation: Attitude determination and RTK positioning using low-cost receivers

    Innovation: Attitude determination and RTK positioning using low-cost receivers

    Getting It Better

    Attitude Determination and RTK Positioning Using Multiple Low-Cost Receivers with Known Geometry

    By Xiao Hu, Paul Thevenon and Christophe Macabiau

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    DO YOU HAVE THE BEST POSITION AND ATTITUDE? No, I’m not talking about your personal life. I’m talking about the location and orientation of a vehicle. We all know that GNSS, perhaps augmented with additional sensors, can provide the position of a vehicle accurate at the few-meter level or so under good conditions when using only single-frequency pseudorange measurements. After all, this is how most consumer-grade receivers work, including those embedded in cell phones. Yes, we can sometimes do better by using dual-frequency measurements such as those obtained by newer cellphone models with dual-frequency receivers, but we need to use carrier-phase measurements to get a significant improvement in positioning accuracy.

    But this is not news. That carrier-phase measurements had the capability to provide up to centimeter-level accuracy was demonstrated in the early 1980s, when only a few prototype GPS satellites were in orbit. Surveyors and geodesists developed a relative or differential positioning technique using a reference station and one or more rover stations to determine the three-dimensional baselines connecting the stations. Knowing the coordinates of the base station, the coordinates of a rover station could be determined. This use of differential carrier-phase measurements was actually pioneered in the radio astronomy community through the development of interferometric techniques for improving the resolution of radio telescopes, which led to the invention of very long baseline (radio) interferometry (VLBI) in 1967. Geodesists realized that the astronomers’ technique could be used to obtain very precise (and accurate) baselines between radio telescopes, even if they were on different continents.

    The surveyors and geodesists performing the initial baseline determinations using GPS carrier-phase observations even developed ingenious techniques for resolving the integer ambiguities that bedevil carrier-phase measurements. Those techniques evolved into the real-time kinematic or RTK positioning capability widely used today, as well as attitude determination. It was in March 2001 that we featured an Innovation article on GPS attitude determination. As we said, “[GPS] is well known for its ability to determine a platform’s position and velocity with high accuracy. Less well known is the ability of GPS also to provide the orientation of the platform. Using three or more antennas feeding separate receivers, or separate channels in a single receiver, the baseline vectors connecting the antennas can be determined. The directions of these vectors determine the platform’s three-dimensional orientation… If only two antennas are used, then only two angles or directions of the platform can be determined, such as the azimuth or heading of the platform and its elevation angle or pitch.”

    While RTK positioning and attitude determination is readily done with high-end equipment, it is still a challenge to get good results for kinematic platforms with low-cost receivers. In this month’s column, we learn how a team of researchers in France is trying to do just that.

    Over the past decade, GNSS has been commonly used in various domains: automotive, aviation, marine, precision agriculture, geodesy and surveying, and so on. This includes autonomous driving, which has become more and more a central topic for the automobile industry, a kind of application for which information about precise position and attitude is essential. However, the accuracy and integrity that a low-cost GNSS receiver can provide in a restricted urban or indoor environment is far from satisfactory for applications where bounded decimeter or centimeter accuracy is envisioned.

    To reach this level of accuracy, techniques using raw carrier-phase measurements have been developed. Such measurements are more precise than pseudorange (code) measurements by a factor of about a hundred. Nevertheless, they are also less robust than code measurements and include a so-called integer ambiguity that requires implementation of an integer ambiguity resolution (IAR) process to use them for positioning. In some harsh environments, severe multipath and losses of lock of the receiver tracking loops create carrier cycle slips, which result in sudden changes of these ambiguities. If not detected, cycle slips create a bias in the carrier-phase measurement resulting in a reduction of position accuracy. Even if a cycle slip is detected, the IAR process has to be, at least partially, re-initialized, leading also to a loss of positioning accuracy. To increase confidence and to accelerate the IAR process by limiting the search space, restrictions can be established by using an array of two or more receivers with prior known and fixed geometry, which includes the lengths of the baseline vectors between the antennas of the receiver array and the orientation of these vectors.

    In recent years, several studies have focused on the use of an array of receivers for attitude determination. However, to the authors’ knowledge, very few research articles can be found that address the use of an array of receivers to improve the accuracy of positioning or for some steps of precise position computation for real-time kinematic (RTK) processing with vehicle attitude determination, such as cycle-slip detection or integer ambiguity resolution. The objective of the research reported in this article is to explore the possibility of achieving precise positioning with a low-cost architecture: using multiple low-cost receivers with known geometry to enable vehicle attitude determination and improved RTK performance.

    SYSTEM GEOMETRY AND CONFIGURATION

    With the intention of performing precise attitude estimation, we have adopted a dual antenna set-up, where two GNSS antennas with a known baseline length are mounted on a vehicle’s rooftop to get an attitude estimation. Furthermore, the absolute position accuracy is augmented using the RTK approach, in which the vehicle is positioned relative to a third receiver, whose position is static and known, used as a virtual reference station (VRS). By knowing the position of the VRS, the vehicle can be positioned absolutely, too. In the positioning algorithm, we strongly rely on carrier-phase positioning which, thanks to its low noise characteristics, may enable decimeter-level positioning. FIGURE 1 shows the typical geometry of our measurement set-up.

    FIGURE 1. Geometry of the model including the definition of the attitude of the vehicle.
    FIGURE 1. Geometry of the model including the definition of the attitude of the vehicle.

    According to Figure 1, the two GNSS antennas on the vehicle’s rooftop span the array antenna baseline b12, which one can resolve for the Euler attitude angles to get the vehicle’s orientation (heading and pitch ).
    For each time epoch, we estimate both the RTK position and receiver array attitude. The baseline b13 spanned between one vehicle antenna and the VRS antenna enables us to locate the position of the vehicle relative to the VRS.

    MATHEMATICAL MODELS

    The realization of GNSS navigation is typically based on a Kalman filter, which is the most popular choice given its optimality and simplicity of implementation. In our study, we developed a position and attitude determination algorithm based on an extended Kalman filter (EKF).

    State Transition Model. The state transition or state-space model describes how the states or parameters of the system vary over time based on a specific linear model. In our EKF modeling, the state vector includes five vehicle state parameters and 2 × (Nsat – 1) satellite state parameters: the 3D position of GNSS receiver 1 relative to GNSS receiver 3 (), the pitch angle of the vehicle (), the heading angle of the vehicle (), the DD integer ambiguities of the visible satellites seen by GNSS receiver pair 1-3, and the DD integer ambiguities of the visible satellites seen by receiver pair 2-3. Note that at a given epoch, we can consider different sets of satellites visible for the receiver pairs.

    Transition Model for Position- and Attitude-Related State Parameters. In our EKF modeling for the position- and attitude-related state parameters, we suppose that they follow a random walk model, meaning that the speed and the angular rate are a zero-mean Gaussian process.

    Transition Model for Satellite-Related State Parameters. The satellite-related parameters are all assumed to be constant over subsequent epochs with very small noise compared to the position- and attitude-related state parameters. The resulting state transition matrix is then given by an identity matrix and different values of process noise variance are added to complete the model.

    Measurement Model. The measurement model describes how the individual sensor measurements are related to system states. In general, for every epoch, the measurement vector, which contains all measured values, can be described as a function of the state vector combined with the measurement noise vector, which describes the expected Gaussian noise of every measured value with an associated measurement noise covariance matrix.

    In our model, the measurement vector comprises the following measured values: the double-difference (DD) pseudorange (code phase) measurement vector of receivers 1 and 3, the DD pseudorange measurement vector of receivers 2 and 3, the DD carrier-phase measurement vector of receivers 1 and 3, and the DD carrier-phase measurement vector of receivers 2 and 3. The DD measurements are obtained by differencing the single-difference (SD) measurements in the usual way.

    In this measurement model, the position of receiver 2 is expressed in terms of the position of receiver 1 and the baseline vector between the two receivers of the array, such that it contains the known array baseline length information and the attitude information that we want to estimate. The individual DD corrected pseudorange and carrier-phase measurements for our short baseline case (less than 3 kilometers) can be modeled using certain approximations such as ignoring the tiny differential atmospheric effects.

    To reflect the difference in precision between the pseudorange and carrier-phase measurements, a fixed weighting factor of 1/100 is applied to the pseudorange.

    An elevation-angle-dependent measurement noise variance between all satellites is defined to complete the measurement model, defining the measurement covariance matrix.

    The relationship between the state and measurement vector is obviously non-linear, thus we need to linearize this measurement function and obtain the measurement (Jacobian) matrix for use in the EKF, as usual. Our algorithm is described in more detail in the proceedings paper on which this article is based (see Further Reading).

    Two alternating steps, which are the state prediction step and the state update step, are then conducted to complete the proposed EKF algorithm.

    RTK PROCESSING

    We first carry out a cycle-slip detection and repair scheme based on multi-epoch measurements. In our work, we use the differential phases over time cycle-slip resolution method. It is based on the observation of the differential phases between two adjacent epochs, which should include the actual jump in the ambiguity if there is one, plus some clock errors and the remaining noise term.

    Except for the ambiguity term, all terms contributing to the time-differenced phases change slowly. Any cycle slips will lead to a sudden jump in the time difference of the phases. Based on the past observation of differential phase measurements, a prediction of the current differenced data can be obtained by polynomial extrapolation or interpolation. The residual between the prediction and the observation can then be used as a detector metric, to be compared to the detection threshold to decide whether there are any cycle slips.

    After the cycle-slip detection process, a cycle-slip validation and size determination process is conducted to verify the determined sizes of the cycle slips. Cycle slips can be repaired using integer vector estimation similar to ambiguity resolution in the position domain.

    After repairing the existing cycle slips, the RTK processing begins. From the previously described EKF process, we first obtain a float estimation of the DD integer ambiguity. The accuracy of the position state estimate is further improved by fixing the DD ambiguities to integer numbers by using the well-known LAMBDA algorithm.

    The integer candidates are selected based on the sum of squared errors to get a fixed solution. The candidate with the lowest error norm is chosen once the ratio of the maximum a posteriori error norm between the second-best candidate and the best candidate is bigger than a threshold. It is a pre-defined critical value that the squared norm of ambiguity residuals of the best and second-best candidates should exceed to validate the integer estimation. In our work, we take an empirical fixed value of 3.0.

    Once the IAR process is declared successful, a new position is computed using the DD carrier phase measurements corrected by the validated DD integer ambiguities. This final position is a fixed solution. If the IAR process is not declared successful, the final position is kept as the float solution.

    SET-UP AND SCENARIOS

    In this section, we discuss the verification of our precise position and attitude determination algorithm with real measurements from two low-cost GNSS receivers and one high-end GNSS receiver.

    Data Collection. To investigate the feasibility of our proposed precise positioning and attitude determination algorithm, we set up a measurement campaign using four low-cost GNSS patch antennas and took measurements by recording single-frequency (L1) GPS pseudorange and carrier-phase measurements simultaneously at a 1-Hz rate. We put the patch antennas at various distances from each other, ranging from 60 centimeters to about 2.0 meters. In addition, we made the measurements in several different environments including an urban environment, a suburban environment, and an open-sky environment.

    FIGURE 2 shows a typical set-up of the GNSS antennas for one of the measurement sessions. According to Figure 1, any two GNSS antennas on the vehicle’s rooftop span the vehicle antenna baseline, which we can resolve for the pitch and heading attitude angles to get the vehicle’s orientation. Additionally, the baseline spanned between one vehicle antenna, and the VRS antenna is able to position the vehicle relative to the latter. If the VRS antenna location is known, the absolute position of the vehicle can be determined.

    FIGURE 2. Real data collection set-up: Four GNSS U-blox antennas and one NovAtel SPAN receiver antenna on the vehicle rooftop.
    FIGURE 2. Real data collection set-up: Four GNSS U-blox antennas and one NovAtel SPAN receiver antenna on the vehicle rooftop.

    The measurement test was performed with a vehicle on which the following hardware was mounted:

    • 4 low-cost GPS receivers with 1 Hz data rate
    • 4 L1 patch antennas mounted on the roof of the vehicle along its longitudinal axis
    • 1 high-end receiver on the roof of a building as the reference station for RTK processing
    • 1 high-end receiver tightly coupled with a tactical-grade inertial measurement unit with an antenna on the vehicle to provide its reference position and attitude.

    Data Collection Scenarios. We collected three sets of data in different environments for the analyses described in this article.

    Open-Sky Environment. To get an open sky and stable environment, the first measurement session took place on the football field of the École Nationale de l’Aviation Civile (ENAC) in Toulouse. As shown in FIGURE 3, the two receivers are static and their positions are fixed on the football field with favorable satellite visibility. This scenario was mainly used for validation of the algorithm implementation and to provide a reference for our multi-receiver system performance.

    FIGURE 3. Receiver fixed position for dataset 1.
    FIGURE 3. Receiver fixed position for dataset 1.

    Suburban Environment. The second measurement session took place on the ENAC campus. The true trajectory provided by the high-end equipment and the corresponding satellite visibility during the data collection are shown in FIGURE 4.

    FIGURE 4. Trajectory and corresponding satellite visibility for dataset 2.
    FIGURE 4. Trajectory and corresponding satellite visibility for dataset 2.

    Urban Environment. The third measurement session was performed when the vehicle was driven from ENAC to Toulouse’s city center. The whole trajectory in Google Earth and the corresponding satellite visibility during the data collection are shown in FIGURE 5.

    FIGURE 5. Trajectory and corresponding satellite visibility for dataset 3.
    FIGURE 5. Trajectory and corresponding satellite visibility for dataset 3.

    EXPERIMENTAL RESULTS AND DISCUSSION

    The datasets we collected allow us to investigate certain aspects of the simulations of our previous work. With our data collection approach, we could vary the distance between the two rover antennas, referred to as the array baseline length elsewhere in this article, as well as the type of environment. In this section, we address the following three points regarding the impact of these two aspects (array baseline and type of environment):

    • correlation of measurement error in the measurements collected by an array of receivers
    • improvement of cycle-slip detection and repair
    • improvement of positioning and attitude accuracy and ambiguity-fixed solution availability.

    Experimental Results on the Correlation of Measurement Errors. As the measurements come from signals received by the closely placed antennas, it is safe to consider a certain level of correlation between these measurements. For example, the multipath error from the same satellite may be similar in the measurements recorded by the receivers connected to the two closely-mounted antennas.

    By removing the corresponding geometric distance term from the DD pseudorange and carrier-phase observations, the correlation coefficient for the DD pseudorange and carrier-phase observations measured by the different antennas on the same satellite pair can be computed.

    We found that the DD phase measurement errors are very correlated, and there is also a medium degree of correlation between the pseudorange measurements. We speculate that this might be due to the characteristic of the GNSS DD measurements.

    To make our model closer to the real situation and thereby improve the EKF performance, we updated the observation covariance matrix in the EKF model by replacing the diagonal matrix with one including non-diagonal terms based on the correlation coefficient values we found.

    Experimental Results on the Cycle-Slip Detection Performance. We carried out a number of tests on our single-frequency method, which has many limitations. Due to the imperfect detection of cycle slips, a larger ambiguity standard deviation value of 0.1 cycle was chosen to account for possible undetected cycle slips. A slight improvement in positioning results was achieved compared with a smaller value of 0.01.

    Typical Positioning and Attitude Determination Performance. We found that the dual-receiver system performs better than the single-receiver situation and provides better solution availability thanks to the doubled observations redundancy.

    FIGURE 6 illustrates the 3D RTK positioning estimation error for our multi-receiver method. As one can notice, the algorithm succeeded in outputting a positioning result with an accuracy of about 0.5 meters for the horizontal coordinates, which is acceptable for a harsh environment.

    FIGURE 6. 3D RTK positioning estimation error illustration.
    FIGURE 6. 3D RTK positioning estimation error illustration.

    FIGURE 7 shows the estimation of the pitch and heading angles of the vehicle for dataset 1. One can see from the figure that the error between the estimated result and the true value is extremely small (less than 2 degrees), which can provide us with a relatively accurate vehicle posture by using our proposed method.

    FIGURE 7. Illustration of vehicle attitude estimation for dataset 1.
    FIGURE 7. Illustration of vehicle attitude estimation for dataset 1.

    Experimental Results of the RTK Performance. We found that for our open-sky data, the dual-receiver array system provides better performance than the single-receiver RTK solution, thus demonstrating the usefulness of such an approach.

    The results from the analysis of the datasets from the suburban and urban test drives gave us some useful information on the robustness of the multi-receiver RTK system in harsh environments.

    As previously mentioned, the suburban data was collected when the vehicle was driven on the ENAC campus. The reference trajectory was provided with centimeter-level accuracy. The maximum standard deviation values, up to 10 centimeters, occur at around the 500-epoch mark, which corresponds to the zone having a minimum number of visible satellites. Generally, however, the environment is quite favorable with at least eight satellites in view for most of the time.

    We have compared the results from the single-receiver system and our multi-receiver system in terms of the ambiguity fix success rate, the horizontal positioning (east and north directions), error statistics (mean, standard deviation, and 95% error bound), and array attitude error statistics for all three data collections. As an example, TABLE 1 gives the performance comparison between our dual-receiver system and the single-receiver system in the suburban environment and for different array baseline lengths. A better accuracy result is obtained in the dual-receiver situation.

    Table 1. Performance comparison for different array baselines for data collection 2 – Suburban.
    Table 1. Performance comparison for different array baselines for data collection 2 – Suburban.

    Moreover, there is no huge difference in the accuracy of the positioning results between the different dual-receiver variations. However, as expected, the attitude accuracy does improve slightly as the array baseline length increases.

    The high-end GNSS receiver with IMU provided a decimeter-level trajectory accuracy in the urban scenario. The number of visible satellites was much lower than for the other two environments. A clear uncertainty increase in the position solution was observed in the reference result during the trajectory section where the number of satellites was fewer than six. We also obtained satisfactory results from our urban scenario test. We can conclude that the use of an array of receivers with known geometry to improve RTK performance is feasible and effective.

    CONCLUSION

    In this article, we have presented a method that includes an array of receivers with known geometry to enable vehicle attitude determination and enhanced RTK performance in different environments. Taking advantage of the attitude information and the known geometry of the array of receivers, we are able to improve some of the steps of precise position computation.

    We demonstrated through real data processing results that our multi-receiver RTK system is more robust to degraded satellite geometry, in terms of ambiguity fixing rate, and obtains a better position accuracy under the same conditions when compared with the single-receiver system.

    ACKNOWLEDGMENTS

    The research described in this article was supported by the China Scholarship Council. The article is based on the paper “Attitude Determination and RTK Performances Amelioration Using Multiple Low-Cost Receivers with Known Geometry” presented at the virtual 2021 International Technical Meeting of The Institute of Navigation, Jan. 25–28, 2021.

    MANUFACTURERS

    Our equipment included four U-blox (www.ublox.com) F9P GNSS receivers fed by U-blox ANN-MB series multi-band patch antennas, a Hexagon | NovAtel (www.novatel.com) ProPak6 SPAN GNSS receiver with integrated tactical-grade IMU and a NovAtel Pinwheel GPS-702-GG antenna, and a Septentrio (www.septentrio.com) AsteRx-U GNSS receiver with a Hexagon | Leica (leica-geosystems.com) AR20 choke ring antenna as the base station for RTK processing.

    XIAO HU is a Ph.D. student in the Signal Processing and Navigation (SIGNAV) research group of the TELECOM laboratory at École Nationale de l’Aviation Civile (ENAC) in Toulouse, France.

    PAUL THEVENON is an assistant professor at ENAC.

    CHRISTOPHE MACABIAU is the head of ENAC’s TELECOM team, which includes research groups in signal processing and navigation, electromagnetics, and data communication networks.

     

    FURTHER READING

    (Coming soon)

  • BAE Systems to open Iowa facility for mission-critical GPS work

    BAE Systems to open Iowa facility for mission-critical GPS work

    Illustration: BAE Systems
    Illustration: BAE Systems

    BAE Systems is investing more than $100 million to build a state-of-the-art facility in Cedar Rapids, Iowa, expected to be completed in 2022. The facility will support the company’s newly acquired Navigation & Sensor Systems business, which makes mission-critical military GPS products.

    The new building will bring the company’s local design and production employees from multiple locations into a single center of excellence with modern manufacturing, engineering and office space.

    “Our world-class military GPS business is built on the rich talent pool in Greater Cedar Rapids,” said John Watkins, vice president and general manager of Precision Strike & Sensing Solutions at BAE Systems. “This investment will provide our high-tech engineering and manufacturing experts with a world-class workspace and the tools to enhance operational excellence.”
    The facility will improve operational efficiency, optimize production, and enhance the company’s ability to deliver high-quality military GPS products to the warfighter.

    The 278,000-square-foot research and development center will be located on a 32-acre site. The building will include a large factory; several hundred offices, workstations, and flexible work spaces; and classified and unclassified labs. The building was designed for growth, with the ability to add 50,000 square feet of additional space.

  • Innovation: Improved navigation through GNSS outages

    Innovation: Improved navigation through GNSS outages

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    Fusing Automotive Radar and OBD-II Speed Measurements with Fuzzy Logic

    SYN·ER·GY /ˈsinərjē/ noun: the interaction or cooperation of two or more organizations, substances, or other agents to produce a combined effect greater than the sum of their separate effects; from the Greek, “working together.” That is how the Oxford Dictionary defines this useful property that we often apply to business activities and other human interactions. But it can just as well describe the basis of an apparatus such as a navigation system that consists of several devices working together to produce a safer and more accurate result.

    We all know that GPS or any GNSS for that matter doesn’t work everywhere all the time. For example, in built-up areas, signals can be blocked and reflected by buildings leading to positioning errors or complete outages. That is why it is quite common nowadays to combine a GNSS receiver together with an inertial measurement unit or IMU (often in the same package) to produce a more reliable solution for continuous navigation. But IMUs drift and so during an extended GNSS outage, the fidelity of the position reported by the GNSS plus IMU system will degrade with time. And so additional sensors must be added to the mix to improve the reliability of the navigation system. LiDAR, cameras, altimeters and so on have all been used severally or individually to augment the basic GNSS plus IMU combination. Self-driving cars, for example, use multiple sensors to provide safe navigation under specific conditions. Such specialized systems are quite expensive and so we might ask: Can the basic combination of GNSS and an IMU (or some of its components) be augmented by measurements already available in most vehicles or provided easily and inexpensively by equipment add-ons?

    Yes. One measurement that helps is the forward speed of the vehicle. This is available from the vehicle’s on-board diagnostics computer system that tracks and regulates a car’s performance. Car manufacturers have adopted a standard for reporting data, the latest version of which is OBD-II. It is easy to interface to the OBD-II connector in a vehicle and extract the speed measurements – the same measurements displayed by the vehicle’s speedometer. Another potential source of speed measurements is the radar in most modern vehicles used for adaptive cruise control. That measurement is hard to acquire and has other limitations. But the idea to use radar as an input to a navigation system is a good one and easily obtained and installed radar units can be used instead.

    But how do you optimally combine all of these sensor readings to produce reliable navigation? In the Innovation article this month, we take a look at how fuzzy logic can be used to get a reliable speed estimate, how that can be combined with accelerometer and gyroscope measurements to get position, velocity and attitude of a vehicle and, lastly, how that can be combined with GPS-derived position and velocity in an extended Kalman filter to produce an integrated navigation solution. Now that’s synergy.

    Abosekeen

    Standard land vehicles and self-driving cars have acquired precise navigation solutions to improve safety and assist drivers. GNSS is used as the primary source of the navigation solution for such applications. However, when driving in environments such as urban canyons, tunnels, or under bridges, GNSS signal reception deteriorates. Worse, it may suffer from a full outage. Because of this, we need a supplemental or backup system, such as an inertial navigation system (INS). The INS provides a complete navigation solution, and it is not affected by signal deterioration or jamming. GNSS/INS integration can achieve better accuracy than GNSS alone. However, such efficiency cannot be maintained during extended GNSS outages, especially with low-cost and commercial-grade inertial sensors for the INS. This drawback principally occurs because the INS solution suffers from accumulated error growth over time. This error causes path or trajectory drift, which becomes significant in the long term.

    The fusion between an INS and a GNSS-based system provides a more robust solution than each system alone. In particular, INS/GNSS integration requires both systems to provide the vehicle with an accurate solution. However, when the vehicle is in challenging environments, the GNSS receiver cannot successfully update the integration filter, leaving the INS as the only source for the solution. When a GNSS outage is prolonged in some extreme situations, the solution quality deteriorates rapidly from INS drift. In particular, when using a micro-electromechanical system (MEMS) based inertial measurement unit (IMU), the drift rate significantly increases.

    Several approaches have been introduced to overcome such drawbacks. Our reduced inertial sensor system (RISS) concept can be a replacement for the INS in land vehicle and ground robot applications. RISS can provide a complete navigation solution with fewer sensors than a standard INS. It is easily implemented for common land or self-driving vehicle navigation because it uses the vehicle’s on-board diagnostics standard II (OBD-II) device to determine the vehicle’s forward speed. INS requires two integration steps for positioning, but using the OBD-II speed measurements in the RISS mechanization requires only one.  This reduction reduces the drift rate because it limits error accumulation from the integration process.

    RISS depends mainly on OBD-II speed measurements to provide the land vehicle forward velocity. Unfortunately, these speed measurements are vehicle-specification dependent. Furthermore, these speed measurements are vulnerable to several types of error sources that can be categorized as deterministic (systematic) and non-deterministic (non-systematic). Deterministic errors come from wheel-diameter changes due to variations in temperature, pressure, tread wear, speed, unequal wheel diameters between the different wheels, inefficient wheelbase (track width), limited resolution and sample rate of the wheel encoders. Non-deterministic error sources include wheel slips, uneven road surfaces and skidding. Both groups of error sources negatively affect the velocity, traveled distance and heading estimations using the speed measurements from the OBD-II device.

    Accordingly, we have made several RISS modifications to enhance performance, such as integration with a GPS receiver by enhancing the system design matrix for the integration filter. Moreover, an azimuth measurement update from magnetometers was added to the RISS/GPS integrated navigation system to provide azimuth updates during GPS outage periods, so the system can ensure more reliable positioning accuracy in challenging GNSS environments. Furthermore, we introduced a radar-based RISS to overcome OBD-II speed measurement errors. With this system, we demonstrated the superiority of using a frequency modulated continuous wave (FMCW) radar as a speed source instead of the one based on the OBD-II device. Automotive adaptive cruise control (ACC) mainly uses the Doppler measuring technique to measure the target’s (the vehicle ahead’s) relative distance and velocity. The primary radar unit’s radiation pattern is supposed to be a narrow beam to avoid other moving objects. Unfortunately, clutter affects forward-looking radar-collected data. Besides, extracting the onboard vehicle’s speed is difficult primarily because of the radar installation position.

    We improved the use of ACC by modeling the linear and non-linear error components with Fast Orthogonal Search as a non-linear system identifier. This provided a more precise solution during outages extending from 60 seconds to 10 minutes. Furthermore, vehicle positioning using ACC was enhanced by extracting the primary and target vehicles’ relative distances under specific rules in urban canyons. These results encouraged us to introduce a fusion between the RISS and ACC, developing a more robust navigation system that relies on more than one sensor type.

    In this article, we propose a smart fusion technique to produce more accurate velocity information from both the Doppler radar and the OBD-II speed measurements. Our new RISS mechanization for land vehicle navigation uses the fused speed from the radar and the OBD-II device with a vertical gyroscope and two transversal accelerometers.

    3D-RISS MECHANIZATION

    Our approach relies on a RISS incorporating a single-axis gyroscope, accelerometers, and speed measurements. Two accelerometers are used to estimate the pitch and roll angles instead of using two additional gyroscopes. Speed from the OBD-II device and heading information from the gyroscope aligned with the vehicle’s vertical axis enables the calculation of velocity, as shown in FIGURE 1. Calculating pitch and roll from accelerometers rather than gyroscopes retains RISS’s low cost while avoiding the gyroscope’s underpinning integration of velocity and position errors. When pitch and roll are calculated from accelerometers, the first integration of the gyroscope to obtain pitch and roll is eliminated, and thus the error in pitch and roll is not proportional to time integration.

    FIGURE 1. Block diagram of speed measurements from the OBD-II device and RISS mechanization. (Image: Authors)
    FIGURE 1. Block diagram of speed measurements from the OBD-II device and RISS mechanization. (Image: Authors)

    ACC-RADAR-BASED RISS

    The radar-based RISS mechanization can provide a complete navigation solution (including 3D position, velocity and attitude) using a reduced number of sensors compared to the classic INS. It consists of longitudinal and transversal accelerometers, one vertical gyroscope and one radar unit (see FIGURE 2). In this mechanization, the OBD-II-device-related measurements are replaced by those extracted from the FMCW radar.

    FIGURE 2. Radar-based RISS/GPS integrated navigation system block diagram. (Image: Authors)
    FIGURE 2. Radar-based RISS/GPS integrated navigation system block diagram. (Image: Authors)

    MULTI-SENSOR DATA FUSION

    Data fusion is the process of combining data from multiple sensors and related information to achieve more specific inferences than can be achieved by using a single, independent sensor. Fusion processes are often categorized into three modes — low, intermediate and high-level fusion:

    • Data level combines several sources of the same type of raw preprocessed data to produce a new data set expected to be more informative and useful than the inputs.
    • Feature level combines features such as edges, lines, corners, textures or positions into a feature map used for the segmentation of images, detection of objects, and so on.
    • Decision level combines decisions from several expert modes. Methods of decision fusion are voting, fuzzy logic and statistical methods.

    Various approaches for multi-sensor data fusion including weighted average, Bayesian estimators, adaptive observers, algebraic functions, fuzzy logic, neural network, soft computing, non-linear system fusion, and Kalman. Drawbacks of these methods include:

    • the necessity of adding new sensors to the system.
    • use of linear estimation models that need previous knowledge of signal statistics.
    • the presence of more than one faulty signal — an essential limitation of the performance. 
    • the need to understand the behavior of the system to generate governing rules.

    We used a data-clustering approach, which divides the data from a particular set into subsets (clusters) based on similarity. It could be defined as a reorganizing process for the dataset.

    Fuzzy C-means (FCM) Algorithm. The FCM clustering algorithm represents the “fuzzify” step in the fuzzy system and is based on the minimization of an objective function called the C-means functional. The FCM algorithm (FIGURE 3) computes the standard Euclidean distance norm, which induces hyperspherical clusters. Hence it can only detect clusters with the same shape and orientation because the common choice of the norm-inducing matrix is the identity matrix. Three parameters in this algorithm have to be determined at the beginning: the number of clusters, the weighting parameter representing the system’s fuzziness, and the ending threshold, respectively.

    FIGURE 3. FCM flowchart. (Image: Authors)
    FIGURE 3. FCM flowchart. (Image: Authors)

    Cluster Number Selection. The FCM algorithm required predefining the number of clusters (Figure 3). This number can be entered randomly, taking iterations and time to converge to the best number, or be calculated. Many methods could be used, such as the validation parameters but only in an offline mode, or by using the data distribution itself and calculating the probability density function (PDF) by first calculating the data’s kernel and then calculating the PDF. This process can be done using the smooth kernel density estimator (SKDE), which is a powerful real-time approach. The main idea is that the measurements values drift in two directions around the acceptable region of measurements (see FIGURE 4). The number of clusters has to be determined in every instance of measurement. From the same figure, the partitions may be three if the drift was in two directions from the accepted region or may be two partitions if the drift at any instance were to the left or to the right direction (one direction drift).

    FIGURE 4. Measured data partioning. (Image: Authors)
    FIGURE 4. Measured data partioning. (Image: Authors)

    Subsequently, the number of clusters is determined according to the following two rules, based on the kernel estimator’s maximum peak location: If the maximum peak of the SKDE is left- or right-skewed, then the number of partitions is two; if the maximum peak of the SKDE is centered, then there are three.

    METHODOLOGY

    The methodology of the implementation of our approach is divided into two parts. The first part utilizes the FCM explained in the previous sections to produce a fused vehicle forward speed from the radar and the OBD-II device. The second part uses the fused speed in the INS mechanization instead of using one sensor only. Further, the mechanization output is integrated with the GPS receiver to establish a more accurate navigation system.

    Sensor Fusion using Fuzzy Clustering. The data-fusion technique using the fuzzy clustering algorithm (FIGURE 5) consists of five main parts:

    • collecting data from the environment by using multiple sensors.
    • grouping the collected data by using the FCM algorithm in cluster form (“fuzzification”).
    • applying the fuzzy clipping rule using a cutting tool (fuzzy process).
    • making use of the clipping-rule properties to perform the fusion mechanism (additional process).
    • using the mean of the minimum to estimate the fusion output (“de-fuzzification”).
    FIGURE 5. Sensor data fusion mechanization. (Image: Authors)
    FIGURE 5. Sensor data fusion mechanization. (Image: Authors)

    The first part is concerned with setting the sensors for measuring a particular phenomenon from the environment. The second part is to “fuzzify” these measured data, using the FCM to separate the sensors’ data to a certain number of clusters with membership matrix and cluster centers. The fuzzy process deals with the output clusters and membership functions through a fuzzy process called the fuzzy clipping rule. This rule divides the membership function into two regions: the upper region of the cutting threshold, which is clipped and is useless in the fuzzy environment, and the lower region from the cutting threshold, which is the useful region in the fuzzy environment.

    Additional processes are applied to benefit from the previous stage — the existence of two regions, one useful, and the other not. This process aims to distinguish between the membership’s functions of the clusters. This could be achieved by generating a binary code that represents the membership function of the clusters. This binary code is generated by comparing the membership function with the threshold value. After the clustering process, each cluster membership function is represented as a binary code. The creation of this code depends upon the membership functions for the clusters and a variable threshold level.

    The defuzzification part aims to extract the suitable value and in the same units as those of the measurements. This part produces the fusion output. This output comes from the minimum binary code, which denotes the selected suitable cluster membership function. This cluster contains the optimum solution. This solution or the fusion process output is determined by the centroid of the selected membership function.

    Fusion-Radar-RISS/GNSS Integrated Navigation System. In this part of our technique, the fusion algorithm’s output is used in producing a full navigation solution as a control input of the RISS mechanization. This solution is subsequently integrated with the GPS receiver in a loosely coupled scheme using an extended Kalman filter (EKF). The overall proposed integrated navigation system is shown in FIGURE 6.

    FIGURE 6. Block diagram of fused radar-RISS/GPS integrated navigation system. (Image: Authors)
    FIGURE 6. Block diagram of fused radar-RISS/GPS integrated navigation system. (Image: Authors)

    EXPERIMENTAL WORK

    We carried out the experimental work to verify the proposed navigation system’s effectiveness by traveling real road trajectories. The testbed equipment was mounted inside and outside the test van.

    The interior testbed coincides with the van axes. It was rigidly and firmly fixed in the rear seat location using a standard seat chassis. For inertial sensors, we used both a low-cost MEMS IMU and a tactical-grade IMU. The specifications of these units are shown in TABLE 1.

    TABLE 1. Performance characteristics of IMUs.
    TABLE 1. Performance characteristics of IMUs.

    We used a dual-frequency GPS receiver with an output rate of 1 Hz. The tactical-grade IMU includes three fiber-optic gyroscopes and three MEMS accelerometers. The tactical-grade IMU and the GPS receiver were integrated using an off-the-shelf assembly developed by the manufacturer to provide a fully integrated, tightly coupled GNSS/IMU system that delivers a highly accurate 3D navigation solution. This tightly coupled integrated system from the manufacturer is used as a reference to compare the performance and the effectiveness of our proposed methods.

    The FMCW radar development kit from the manufacturer was mounted on the front bumper. The unit’s working frequency is 24.5 GHz with a maximum frequency span of 1.5 GHz, a maximum update rate of 10 Hz, a maximum detectable speed of 215 kilometers/hour, and a 3 dB-beamwidth angle of 8.5°. The chirp frequency spans were adjusted to be 0.125 GHz. The maximum coverage range was 30 meters, and the minimum was 0.5 meters.

    RESULTS AND DISCUSSION

    We conducted a road test with the proposed approach in the downtown area of Kingston, Ontario, Canada, in August 2017.

    The trajectory followed is shown in FIGURE 7 projected on a Google map with the approximate locations of the outages. The reference is plotted in red, and the black arrows mark the direction of motion.

    FIGURE 7. Road test trajectory with ovals indicating the approximate locations of GPS outages. (Image: Author)
    FIGURE 7. Road test trajectory with ovals indicating the approximate locations of GPS outages. (Image: Author)

    Performance Evaluation. The proposed system performance was tested over six simulated outages. The outages have been selected to contain several dynamics such as turns, consecutive turns, stopping, crossing intersections, and straight driving. Furthermore, the outages occurred at different speed levels. The proposed system performance was compared to the traditional RISS/GPS and Radar/RISS/GPS integrated navigation system. The comparison criteria are 2D-position root-mean-square error (RMSE) and the maximum errors.

    We compared our results using the radar-only versus OBD-II device test. TABLE 2 shows the RMSE of the 2D-position from the three systems in meters. Notice that the proposed system’s performance is better than the other two systems during four of the six outages. This result was achieved using the smart fusion technique to fuse the FMCW radar and the OBD-II speed measurements. Accordingly, the obtained speed is positively affecting the overall system performance.

    TABLE 2. 2D-Position RMS-error for the low-cost INS unit during outages.
    TABLE 2. 2D-Position RMS-error for the low-cost INS unit during outages.

    The average 2D-position RMSE reached 18.24 meters when using the OBD-II speed measurements only and 9.5 meters when using the radar only. On the other hand, the RMSE reached 9.4 meters when using the fusion between the two systems. The improvement percentage was 48.4% when applying the proposed integrated navigation system and 47.8% when using the radar-based system. The results show that the proposed system outperformed the other systems in outages 2, 3, 5 and 6 but did not do better than the radar-based system in outages 1 and 4. We highlight three outages.

    The first outage had two left turns after a stop sign over a slippery road. This outage lasted for only 50 seconds, but the system’s behavior was due to wrong measurements combined with a complicated driving scenario when using the traditional RISS/GPS. On the other hand, the radar-based RISS/GPS produces a better solution because of having better velocity measurements in the mechanization, which provides the navigation filter with a better navigation solution. The proposed system limits the drift to around 16.7 meters, while the traditional system had a 68.7-meter drift in its solution.

    The proposed system based on the fusion between both speed sensors — OBD-II and radar — could not compete with the radar because of the enormous gap between the two sensors and the lack of extra sensors. Despite that, the system produced a solution with 2D-RMSE of 22 meters, which is also better than the traditional RISS based on the OBD-II device and close to the results from fusing the radar. This problem can be solved by using an extra radar unit, typically installed with an ACC system. The system usually uses six radar units, two in the front and four at the vehicle’s corners.

    The second outage duration was 80 seconds and contained two consecutive turns, right then left. The radar-based system reduced the solution drift from 28.13 to 23.58 meters. In contrast to the previous outage, the proposed system reduced the 2D-position maximum error to 14.2 meters. The proposed system’s result is superior to the radar-based system, which performed better in the previous outage because the OBD-II and radar measurements gap is not as large as the previous outage. The dynamics, the average speed and the road surface differ from the first outage.

    The third outage was chosen to be a slight turn and mostly straight driving with an average speed of 60 kilometers/hour. This outage lasted for 110 seconds, and the proposed system holds the solution error growth down to 8.9 meters. The traditional system had a higher error growth rate and held it to 20.6 meters, and the radar-based system error reached 14.92 meters. This outage contained fewer dynamics when compared to other outages. Moreover, the slippage and false counting by the OBD-II device was not as considerable as in the first outage.

    CONCLUSIONS AND FUTURE WORK

    The performance of using a multi-sensor data-fusion technique based on fuzzy clustering successfully fuses the data measured by both the radar and the OBD-II device to produce a more robust forward speed of a moving land vehicle. The proposed system performance tested during six simulated GPS outages containing various dynamics significantly improved the overall navigation system, especially when the GPS signals were blocked. Finally, the fusion between multiple sensors leads to better performance if there are enough sensors or a fault-detection system to prevent the faulty sensor from biasing the fusion results. Moreover, the results demonstrate the superiority of the proposed fused radar RISS/GPS over each system alone.

    As an extension to work reported here, we plan to apply our approach with an extra number of sensors to avoid the kind of drift that happened in outage number one. In addition, we suggest that a sensor fault-detection smart algorithm be added to the system to detect and control faulty sensors.

    ACKNOWLEDGMENT

    This article is based on the paper “Enhanced Land Vehicle Navigation by Fusing Automotive Radar and Speedometer Data” presented at ION GNSS+ 2020 Virtual, the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Sept. 21–25, 2020.

    MANUFACTURERS

    Our testbed used a Crossbow (now Moog Crossbow, www.moog.com) MEMS-grade XBOW IMU300CC IMU and a NovAtel/Hexagon (www.novatel.com) IMU-CPT tactical-grade IMU. We also used a SPAN-OEM4 or SPAN-SE NovAtel/Hexagon dual-frequency GNSS receiver. The radar development kit used is a Sivers IMA (now Sivers Semiconductors, sivers-semiconductors.com) RK1001K/00.

    ASHRAF ABOSEKEEN is a lecturer in the Department of Avionics Engineering, Military Technical College, Cairo, Egypt. He received a B.Sc. and M.Sc. in electrical engineering from the Military Technical College in 2004 and 2012, respectively. He received his Ph.D. from the Department of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario, Canada, in 2018.

    UMAR IQBAL is an assistant clinical professor in the Department of Electrical and Computer Engineering, Mississippi State University. He completed his Ph.D. in electrical and computer engineering at Queen’s University in 2012.

    ABOELMAGB NORELDIN is a professor in the Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, Ontario with a cross-appointment at both the School of Computing and the Department of Electrical and Computer Engineering, Queen’s University.

  • Innovation: A multi-sensor navigation system for outdoors and indoors

    Innovation: A multi-sensor navigation system for outdoors and indoors

    Getting the Best in Both Worlds

    By Karsten Mueller, Jamal Atman, Nikolai Kronenwett and Gert F. Trommer

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    IT DOESN’T WORK EVERYWHERE. GPS, that is. Unlike many radio broadcasts and the transmissions from nearby cell-phone towers, the signals from GPS satellites are too weak to be reliably received indoors. They don’t make it into tunnels either. And even outdoors, the signals can be blocked by tall buildings and mountains. This is why the Japanese developed the Quasi-Zenith Satellite System — to provide supplementary signals when an insufficient number of GPS signals are available in the concrete canyons of Tokyo and other high-density cities. Even if a GPS signal can be received, it might be contaminated with multipath interference resulting in a degraded position solution.

    While GPS signals can be piped indoors from an antenna on the top of a building and reradiated, a GPS receiver will give its position as that of the rooftop antenna and not where it is in the building. While this might be useful for establishing the approximate whereabouts of the receiver when it’s on a bus in an underground terminal, for example, and allows the receiver to continue to receive up-to-date navigation messages providing a quick time-to-first-fix when it leaves the terminal, it’s far from satisfactory as a general indoor navigation solution.

    While there are some improvements in signal reception in degraded environments with modernized signals from GPS and the other GNSS constellations, in many instances where we don’t have an unobstructed line-of-sight view of the satellites, GPS alone won’t cut it. Thankfully, other navigation sensors can be used to supplement or replace GNSS when the going gets tough for GPS alone. These include, among others, inertial measurement units, digital compasses, barometric pressure sensors, cameras and laser rangefinders.

    But, even with these, is one better than another in all situations, or do they each have benefits and drawbacks just like GNSS? Would a system composed of multiple sensors be best? Such considerations are important if trying to develop a navigation system that can work well in most any environment both outdoors and indoors and transition gracefully when moving from one type of environment to another. This is the problem that confronted a team of researchers from Germany’s Karlsruhe Institute of Technology when designing a navigation system to allow a micro aerial vehicle to operate continuously and autonomously in almost any environment. In this issue’s “Innovation” column, we learn how they went about it and how well the system worked.

    Today, micro aerial vehicles (MAVs) are widely used in outdoor environments. The navigation solution of commercially available products typically relies on the availability and accuracy of GNSS. To expand the field of application of MAVs to autonomous operation in indoor environments, an accurate navigation solution is necessary. Possible scenarios include the support of rescue forces, surveillance tasks and inspection missions. Different algorithms using camera or laser rangefinder measurements for indoor navigation can provide accurate results.

    However, application of these algorithms is typically limited to indoor scenarios and will not provide accurate results in outdoor environments. Another drawback of these approaches is that absolute positioning is not achieved. Hence, we sought a navigation system for outdoor and indoor environments that combines the beneficial properties of outdoor and indoor navigation systems. Such a navigation system should provide an accurate navigation solution both outdoors and indoors, as well as during transition phases from outdoor to indoor and vice versa.

    THE PROBLEM

    Several challenges arise when combining multiple sensors in a single navigation system due to specific sensor characteristics. While an accurate navigation solution is obtained by an inertial navigation system with GNSS aiding in open-sky environments, urban canyons and indoor environments degrade the quality of GNSS signals or lead to GNSS outages such that no accurate navigation solution is available.

    On the other hand, laser rangefinder measurements allow for the generation of accurate relative measurements indoors. However, due to the limited range of the laser rangefinder, no or only a few measurements are available outdoors away from buildings. Obviously, it is best to exploit the complementary characteristics of both sensors. To avoid losing information, hard switching between two different navigation systems is undesirable. Hence, two main challenges arise:

    • Accurate time synchronization is necessary when processing measurements from different sensors.
    • A method has to be developed for the decision on whether a measurement should be processed or rejected.

    Moreover, for aerial vehicles, two more requirements must be met:

    • Estimation of the 3D position and attitude instead of only the 2D position and heading as provided by 2D simultaneous localization and mapping (SLAM) approaches.
    • Estimation of the vehicle’s velocity and inertial measurement unit (IMU) biases.

    Our goal was to develop a navigation system that provides an accurate navigation solution for large-scale environments. The navigation system needed to provide a frequent navigation solution at the update rate of the IMU with very short delays. The framework needed to seamlessly integrate GNSS and other sensors such as a laser rangefinder or cameras. Additionally, the approach could not be limited to a specific sensor setup except for a mandatory GPS receiver, necessary for absolute positioning.

    The results presented in the literature often do not include large-scale, realistic environments. Some investigators only consider short indoor sequences, while others ignore challenging GNSS conditions. In contrast, the focus of our approach is on rejecting outlier measurements in transition zones such as urban-canyon environments occurring between outdoor open sky and indoor environments. The choice of the navigation system architecture depends on the requirements of a specific platform. In the case of a quadrotor helicopter (see FIGURE 1), a high update rate is necessary for vehicle guidance and control. Therefore, we chose a Kalman-filter-based approach because it has the advantage over pure SLAM approaches when providing a navigation solution at a high update rate is required.

    FIGURE 1. Components of the quadrotor helicopter. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
    FIGURE 1. Components of the quadrotor helicopter. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)

    SYSTEM OVERVIEW

    We attached several sensors and two processing platforms to the quadrotor helicopter used in our work. A microcontroller sensor board reads the sensor values from the IMU, digital compass, air pressure sensor and a GPS-only GNSS module. Timestamps are generated for each sensor data type so that accurate synchronization is provided even when delays occur, such as when processing the sensor data. The IMU is mounted close to the center of the vehicle. The air pressure sensor is directly attached to the sensor board, while the three-axis digital compass is attached to the quadrotor’s landing skid to avoid interfering magnetic fields from power electronics. The GPS receiver provides pseudorange and Doppler measurements at a rate of 10 Hz. Moreover, ephemeris data for each satellite and Klobuchar ionospheric parameters are recorded to correct the measurements. Each second, a time pulse is generated by the receiver to precisely determine the time when GPS measurements were taken. Additionally, the time pulse is used to estimate the drift of the real-time clock (RTC) on the sensor board and, therefore, to provide more accurate timestamps.

    A two-dimensional laser rangefinder is mounted on top of the helicopter. Distance and angular information of objects within a scan angle of 270° is provided by this sensor. The maximum range is 30 meters. Time synchronization is achieved through a pulse registered by the microcontroller sensor board before every scan. The body of the laser rangefinder is shielded using copper foil to reduce interference with signals received by the GPS antenna. A trigger signal is sent to the camera mounted at the front of the helicopter to provide time synchronization. However, the camera was not used for the results presented in this article. An overview of the sensor setup and time synchronization is depicted in FIGURE 2.

    The camera and laser rangefinder data is sent via USB to a powerful computing platform attached to the bottom carbon-fiber sheet. Time synchronization information and additional sensor data is sent from the microcontroller sensor board to the computer for processing the sensor data and calculating the navigation solution.

    FIGURE 2. Block diagram showing signal flows among system hardware components. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
    FIGURE 2. Block diagram showing signal flows among system hardware components. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)

    NAVIGATION SYSTEM

    The navigation system presented in this article was developed to provide a navigation solution in both outdoor and indoor environments. Therefore, processing GPS position and velocity estimations must be possible, as well as handling of relative position and heading angle changes resulting from the laser rangefinder scans. Challenges arise due to the different time delays as illustrated in FIGURE 3. IMU measurements are available at a high frequency. Messages with the trigger timestamps are sent from the sensor board to the computer to provide information about when a GPS or laser measurement was taken.

    FIGURE 3 Time sequencing of measurements and calculations. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
    FIGURE 3 Time sequencing of measurements and calculations. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)

    The corresponding measurements are available with significant delays. Since GPS pseudorange and Doppler measurements are not immediately available and processing requires additional time, the typical delay between the point in time when the measurement was taken by the receiver and the time when the estimated position and velocity are available to the navigation filter is between 70 and 90 milliseconds. Even longer delays occur when processing laser rangefinder data. After processing the laser scans, the horizontal position changes and yaw angle changes (in this article, denoted as two-dimensional pose change measurements) are available for analysis. However, these changes are relative to a point in time in the past. Moreover, due to the processing, additional delay occurs and synchronization with the correct laser rangefinder trigger signal is required. The requirement to process measurements with a temporal overlap causes additional complexity, such as having several GPS measurements that are taken in the time period covered by a pose change measurement.

    Error-State Kalman Filter with Stochastic Cloning. An error-state Kalman filter with 16 states estimates the vehicle’s 3D position, 3D velocity, attitude, accelerometer and gyroscope biases, and the bias for the barometric altimeter. The prediction step of the filter consists of integrating the specific force and angular rate measurements of the IMU. Measurements of the air pressure sensor and the digital compass have negligible delays, so these measurements are processed in the Kalman filter update step without compensating for delays. As we mentioned, the assumption of insignificant delays does not hold for GPS measurements and pose change measurements. Thus, we implemented stochastic cloning to overcome errors that would be introduced by delays. The idea of stochastic cloning is to augment the state vector and covariance matrix by copies of the state and covariance estimates at a specific point in time. As the augmented covariance matrix contains cross-correlation terms between the state at a previous time instance and the current state, processing of delayed measurements corrects the current state and covariance estimations.

    Processing GPS Measurements. The first step when processing GPS measurements is to clone the current filter state. As outlined in the section “System Overview,” the time pulse generated by the receiver is used to determine the time when a measurement is taken. Once the pseudorange measurements are available, corrections are calculated. A weighted least-squares estimation is used to calculate position and velocity. The weight for each pseudorange measurement is the inverse of the estimated variance, which is calculated depending on the carrier-to-noise-density ratio. Weights for Doppler measurements are calculated similarly.

    To reduce the errors introduced by satellite signals of low quality, a minimum carrier-to-noise-density ratio of 33 dB-Hz and a minimum elevation angle of 15° are required for the satellite signals. In addition to position and velocity, valuable information is drawn from the estimation: The variance of the calculated position is chosen to be proportional to the weighted root mean square value of the residuals and the position dilution of precision (PDOP). The velocity variance is calculated similarly. In case only four satellites are used, the variance is only proportional to the PDOP as no residuals are available. The position and velocity estimates are processed by the Kalman filter using these estimated variances. Moreover, before the filter update step is executed, the Mahalanobis distance for each measurement is calculated and outliers removed.

    Additionally, measurements are not processed if their variance is above a threshold. This typically occurs in the vicinity of buildings as non-line-of-sight signals are tracked by the receiver and, therefore, processing these measurements is not desired.

    Laser Rangefinder Processing. As described in the previous section, stochastic cloning is used to treat delayed pose change measurements. To process a measurement, two cloned states are necessary.

    A pose change measurement consists of a relative translation and a rotation, both given in coordinates of the body-stabilized frame, which is identical to the body frame but compensated for roll and pitch angles. Hence, the x and y axes of the body-stabilized frame are parallel to the ground. Several methods could be used for generating pose-change measurements, such as camera-based approaches, laser rangefinder approaches or hybrid approaches. In our work, Cartographer, a laser SLAM approach, is used to obtain horizontal position and yaw angle changes. However, the SLAM module could be easily replaced by other laser SLAM approaches.

    As laser SLAM approaches build an incremental map, the laser’s pose is given with respect to the map frame. Therefore, the translational and rotational components of the pose-change measurement must be transformed from the map frame to the body-stabilized frame before being processed by the Kalman filter. Different options are possible when choosing the first point in time for a relative measurement (the second point in time is determined by the most recent laser measurement).

    We decided to use a keyframe-based aiding technique. A keyframe is defined and the filter state is cloned accordingly. After the processing of a laser measurement by the SLAM algorithm, pose estimations given in map coordinates are transformed to pose change measurements relative to this keyframe. The keyframe is changed depending on the filter status information as outlined in the section “Using the Filter Status Information” of this article. Additionally, the keyframe is changed if the difference between consecutive pose estimations exceeds a threshold. This indicates an erroneous pose estimation by the SLAM module as only small pose changes are expected due to the high update rate of laser scans and the limited velocity of the vehicle. As a result, the influence of errors in the SLAM module on the navigation solution provided by the Kalman filter is reduced.

    FILTER STATUS

    Above, we described how relative and absolute delayed measurements are processed in an error-state Kalman filter. However, simply processing all available measurements will not lead to the best performance of the filter. For example, the laser SLAM algorithm might not provide accurate and reliable results in open-sky environments free from human-made structures, as mainly vegetation is detected by the laser rangefinder.

    To derive a metric for the decision on the necessity of integrating additional relative measurements, we provide a classification scheme based on GPS measurements. The advantage of using only GPS measurements for the filter status determination is the versatility of the approach: A GPS module will be available on almost every platform. The laser rangefinder, however, could be replaced by a camera without modifications in the classification scheme.

    Clearly, processing GPS in indoor environments is not an option as no measurements are available. On the contrary, in outdoor open-sky environments, a sensor setup comprising GPS, IMU, digital compass and air pressure sensor results in an accurate navigation solution. Therefore, the interaction of different sensors in transition phases and urban-canyon environments is the most critical part for an accurate navigation solution in large-scale environments. The following paragraphs introduce the classification of single GPS position measurements and the determination of filter status based on the GPS classification.

    Classification of Single GPS Position Measurements. The first step for the filter status determination is the classification of single GPS position measurements. The categories for a measurement are very good, good, medium and poor. Two parameters are used for the classification: the number of satellites used for the position calculation and the estimated variance. For a very good measurement, at least six satellites are required; for a good measurement, at least five satellites are necessary. Moreover, thresholds for the estimated position variance are applied. As the variance is proportional to the PDOP and the root mean square of the weighted residuals, this means that a very good or good position measurement must offer a good satellite constellation and small residuals.

    Filter Status Determination. The classification of GPS position measurements is used to calculate a filter status. First, a sum over a time interval of one second is computed. The number of positions classified as very good are multiplied by a factor of four, good positions count twice, and the number of medium positions added without a multiplicative factor. In our setup, 10 position measurements are available in one second. The final filter status is determined using two thresholds. If the sum is at least 20, the filter status is “Good GPS.” This means that five measurements classified as being very good or all 10 measurements classified as being good would be sufficient for this status.

    The “Medium GPS” status is achieved with a sum between 10 and 20. If no valid GPS measurements have been available over the last five seconds, an additional indoor flag is set, and it is assumed that the vehicle is now indoors. As soon as GPS position measurements become available again, the filter status is re-calculated. The parameters for the filter status are determined empirically and provide robust results for a large variety of scenarios. However, minor changes of the parameter set to classify single measurements might be necessary in case a different GNSS hardware setup is used.

    The resulting filter status for an example trajectory is shown in FIGURE 4. As expected, GPS is good in the western part of the trajectory, and the status quickly deteriorates to poor GPS between the high-rise buildings. Just before entering the building, the status changes to “Indoor.” After leaving the building and moving north, the filter status changes mainly between good and medium GPS as signals are blocked due to buildings or mitigated due to foliage. The end of the trajectory in the eastern part offers better GPS conditions since the surrounding buildings are smaller and the status changes to “Good GPS.”

    FIGURE 4. The filter status changes from “Good GPS” to “Poor GPS” in the vicinity of high buildings and provides important information on how accurately the filter is aided by processing GPS measurements. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
    FIGURE 4. The filter status changes from “Good GPS” to “Poor GPS” in the vicinity of high buildings and provides important information on how accurately the filter is aided by processing GPS measurements. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)

    Using the Filter Status Information. The filter status provides valuable information when combining GPS and relative measurements. As outlined in previous sections, the filter status “Good GPS” occurs in open-sky environments where processing of additional relative measurements does not improve the navigation solution. Since the laser SLAM solution might be corrupted in areas without a sufficient number of human-made structures, relative measurements are not processed while the filter status is “Good GPS.” Additionally, the keyframe is changed in short time intervals during this status. The reasoning behind this decision is that it is desired to have a good estimation of the absolute position and orientation with a low uncertainty at the time a keyframe is chosen.

    During a period with “Good GPS” conditions, position estimation typically becomes gradually better. For the same reason, it is best to retain a keyframe for a long time when the filter status is “Poor GPS” or “Indoor.” In these scenarios the laser SLAM algorithm provides accurate results as the environment mostly consists of human-made structures. A drawback inside buildings is that the Earth’s magnetic field might become distorted, for example close to elevators. Hence, magnetometer measurements are not processed when the “Indoor” flag is set. If the status “Medium GPS” is set, GPS and relative measurements should be weighted equally. The keyframe is retained until a predefined maximum age is reached or inconsistencies in the SLAM solution are detected.

    In contrast to the “Poor GPS” case, the integration of relative measurements is more pessimistic, and the variance is chosen in the range of the typical GPS accuracy. This takes into account that a very accurate laser SLAM solution is not assured. However, the processing of relative measurements improves position accuracy and avoids the growth of filter state covariance, which is beneficial for rejecting faulty measurements. Independent of the filter status, GPS measurements fulfilling the Mahalanobis distance threshold criterion are processed.

    RESULTS

    The results of three trajectories recorded at the campus of the Karlsruhe Institute of Technology are presented in this section. All trajectories cover outdoor environments with good GPS signal reception as well as urban-canyon and indoor sections. Since flying these challenging trajectories was not possible due to legal reasons and due to small doors that had to be passed through, the quadrotor helicopter was manually carried.

    The first trajectory shown in FIGURE 5 starts in an open-sky environment. At position 1, the footpath goes between two 40-meter buildings. Hence, GPS satellite signals are blocked and non-line-of-sight signals are tracked by the receiver that increasingly deteriorate GPS positon and velocity accuracy. The indoor section starts at position 2. After 30 seconds of indoor navigation, the trajectory continues north on the sidewalk. On this section, numbered 4 in Figure 5, a six-story building on the left side and a nearby building on the right side cause medium to poor GPS conditions as was shown in Figure 4. Despite the difficult conditions, the trajectory follows the footpath correctly. Of course, as no GPS correction service or satellite-based augmentation system is used, sub-meter level accuracy is not achieved. At position 2, the trajectory passes along stairs.

    FIGURE 5. Trajectory 1 featuring two high buildings of 42-meter height between positions 1 and 2 in the center of the image. After an indoor section the building is left at position 3. The total time of the trajectory is 394 seconds. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
    FIGURE 5. Trajectory 1 featuring two high buildings of 42-meter height between positions 1 and 2 in the center of the image. After an indoor section the building is left at position 3. The total time of the trajectory is 394 seconds. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)

    Therefore, accuracy in the north direction is very good. In the east direction, however, the error is larger as the trajectory should be farther east within the building. This error remains throughout the indoor section until position 3, as no GPS position measurement is processed to correct for the error. After leaving the building, the error in the east direction becomes smaller by processing accurate GPS position measurements. After heading north on the sidewalk, the error is within the expected accuracy bounds specified by the GPS position accuracy. The smoothness of the trajectory after leaving the building shows that the rejection of GPS position outliers leads to a consistent navigation solution.

    The second trajectory is the longest of the three trajectories, covering 400 meters in 9 minutes. The first difficult section is denoted by position 1 in FIGURE 6, when the vehicle moves between two buildings. The walls of the right building are covered by metal plates. It looks like the trajectory is very close to the edge of the right building. However, this effect is from the perspective view of the building in the georeferenced image. We passed below a canopy at position 2 and entered a building at position 3. An accurate position solution is available during the long indoor section with multiple turns. The total time spent indoors was 112 seconds. GPS position measurements becoming available after leaving the building at position 4 improve the accuracy of the navigation solution. However, due to the high accuracy of the position estimation before leaving the building, only small filter innovations occur. The trajectory ends on the sidewalk near the building identified as number 5.

    FIGURE 6. Trajectory 2 with a total duration of 9 minutes. An accurate position estimation is obtained during the segment with poor GPS signal reception between positions 1 and 2 and during the indoor section between positions 3 and 4. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
    FIGURE 6. Trajectory 2 with a total duration of 9 minutes. An accurate position estimation is obtained during the segment with poor GPS signal reception between positions 1 and 2 and during the indoor section between positions 3 and 4. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)

    Trajectory three, shown in FIGURE 7, is the most challenging, with position errors exceeding those of the previous two trajectories. Already at the start of the trajectory, only six GPS satellites can be used for calculating position and velocity estimates. It is several meters until an accurate position estimate is available at position 1. Between positions 2 and 3, a section with buildings up to 56 meters tall results in no accurate GPS position fixes being available for more than 30 seconds. In this section, the computed trajectory clearly is several meters too far north. Additionally, at position 2 the heading change is smaller than 90 degrees, which results in additional drift. Before entering the building at position 3, GPS position measurements become available and the position is corrected, reducing the error in the north. After 57 seconds indoors, we exited the building at position 4. The position solution is still too far north, but is corrected by additional measurements so that good accuracy is achieved when walking on the sidewalk. The trajectory ends at its start position.

    FIGURE 7. Trajectory 3. Poor GPS conditions due to a building of 56-meter height near the north part of the trajectory cause position errors. At position 3 accurate GPS measurements are available and correct the position such that an accurate navigation solution is obtained during the indoor part part of the trajectory. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)
    FIGURE 7. Trajectory 3. Poor GPS conditions due to a building of 56-meter height near the north part of the trajectory cause position errors. At position 3 accurate GPS measurements are available and correct the position such that an accurate navigation solution is obtained during the indoor part part of the trajectory. (Photo: K. Mueller, J. Atman, N. Kronenwett & G.F. Trommer)

    CONCLUSION

    The navigation system presented in this article fuses GPS measurements and relative pose change measurements to provide an accurate navigation solution in both outdoor and indoor scenarios. We show that position errors are small even for challenging scenarios with high buildings and poor GPS signal reception. Currently, the accuracy in outdoor environments is limited by GPS accuracy. Further improvements are expected by including additional GNSS such as GLONASS or Galileo to obtain better satellite geometry, especially in urban-canyon scenarios.

    MANUFACTURERS

    We used a u-blox LEA-M8T GPS receiver, an Analog Devices ADIS 16448 IMU, a Freescale (now, NXP Semiconductors) MP3H6115A air pressure sensor, a Honeywell HMC5843 digital compass, an Hokuyo UTM-30LX laser rangefinder, an IDS UI-3260CP-C-HQ camera, and an Intel Next Unit of Computing (NUC) platform. We constructed the quadrotor helicopter ourselves. The motors, motor controllers and landing skid are from MikroKopter, while the carbon fiber sheets and the sensor board PCB are our own design. We used a Pixhawk 4 flight controller from Pixhawk.

    ACKNOWLEDGMENTS

    The authors acknowledge financial support from the Federal Ministry of Transport and Digital Infrastructure of Germany in the framework of mFUND. We also thank the City of Karlsruhe for providing the georeferenced orthophotos. The datasets used for the results presented in this article are available on our project website. This article is based on the paper “A Multi-Sensor Navigation System for Outdoor and Indoor Environments” presented at ION ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–25, 2020.

    KARSTEN MUELLER received an M.Sc. from the Karlsruhe Institute of Technology (KIT), Germany, in 2015, after which he started research as a Ph.D. candidate in KIT’s Institute of Systems Optimization.

    JAMAL ATMAN received an M.Sc. in electrical engineering and information technology from KIT in 2015. He is a research engineer in KIT’s Institute of Systems Optimization.

    NIKOLAI KRONENWETT received an M.Sc. degree in electrical engineering and information technology from KIT in 2015. He is a Ph.D. candidate in KIT’s Institute of Systems Optimization.

    GERT F. TROMMER received Dipl.-Ing. and Dr.-Ing. degrees in electrical engineering from the Technical University of Munich, Germany. He is a professor in KIT’s Institute of Systems Optimization.

    FURTHER READING

    • Authors’ Conference Paper

    “A Multi-Sensor Navigation System for Outdoor and Indoor Environments” by K. Mueller, J. Atman, N. Kronenwett and G.F. Trommer in Proceedings of ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–24, 2020, pp. 612–625. https://doi.org/10.33012/2020.17165.

    • Camera and Laser Rangefinder Navigation

    “Navigation Aiding by a Hybrid Laser-Camera Motion Estimator for Micro Aerial Vehicles” by J. Atman, M. Popp, J. Ruppelt and G.F. Trommer in Sensors, Vol. 16, No. 9, 2016. https://doi.org/10.3390/s16091516.

    “Vision-Based State Estimation and Trajectory Control Towards High-Speed Flight with a Quadrotor” by S. Shen, Y. Mulgaonkar, N. Michael and V. Kumar in Proceedings of Robotics: Science and Systems IX, Berlin, Germany, June 24–28, 2013. https://doi.org/10.15607/RSS.2013.IX.032.

    “Laser Range Finder Aided Indoor Navigation for a Micro Aerial Vehicle” by P. Crocoll, J. Seibold, M. Popp and G.F. Trommer in European Journal of Navigation, Vol. 11, No. 1, pp. 4–14, 2013.

    • Keyframe-Based Navigation

    “Relative Navigation: A Keyframe-Based Approach for Observable GPS-Degraded Navigation” by D.O. Wheeler, D.P. Koch, J.S. Jackson, T.W. McLain and R.W. Beard in IEEE Control Systems Magazine, Vol. 38, No. 4, 2018, pp. 30–48. https://doi.org/10.1109/MCS.2018.2830079.

    • Integrated Navigation

    “3D Multi-Copter Navigation and Mapping Using GPS, Inertial, and LiDAR” by E.T. Dill and M. Uijt de Haag in NAVIGATION: Journal of The Institute of Navigation, Vol. 63, No. 2, Summer 2016, pp. 205–220. https://doi.org/10.1002/navi.134.

    “INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm” by Y. Gao, S. Liu, M.M. Atia and A. Noureldin in Sensors, Vol. 15, No. 9, 2015, pp. 23286–23302. https://doi.org/10.3390/s150923286.

    “Toward a Unified PNT — Part 1; Complexity and Context: Key Challenges of Multisensor Positioning” by P.D. Groves, L. Wang, D. Walter, H. Martin and K. Voutsis in GPS World, Vol. 25, No. 10, October 2014, pp. 18, 27–34, 49.

    “Toward a Unified PNT — Part 2; Ambiguity and Environmental Data: Two Further Key Challenges of Multisensor Positioning” by P.D. Groves, L. Wang, D. Walter and Z. Jiang in GPS World, Vol. 25, No. 11, November 2014, pp. 18, 27-35.

    Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, 2nd edition, by P.D. Groves. Published by Artech House, Boston, Massachusetts, 2013.

    • Stochastic Cloning

    “Stochastic Cloning: A Generalized Framework for Processing Relative State Measurements” by S.I. Roumeliotis and J. W. Burdick in Proceedings of 2002 IEEE International Conference on Robotics and Automation, Washington, DC, May 11–15, 2002, pp. 1788–1795. https://doi.org/10.1109/ROBOT.2002.1014801.

  • Innovation: Design and performance of a novel GNSS antenna for rover applications

    Innovation: Design and performance of a novel GNSS antenna for rover applications

    Smaller and Better

    By Reza Movahedinia, Julien Hautcoeur, Gyles Panther and Ken MacLeod

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    THE ANTENNA. This crucial component of any radio transmitting or receiving system has a history that actually predates the invention of radio itself. The first antennas were used by Princeton professor Joseph Henry (after whom the unit of inductance is named) to demonstrate the magnetization of needles by a spark generator. But it was the experiments of Heinrich Hertz in Germany in 1887 that initiated the development of radio transmitters and receivers and the antennas necessary for launching and capturing electromagnetic waves for practical purposes. It was Hertz who pioneered the use of tuned dipole and loop antennas–basic antenna structures we still use today. As communication systems evolved using different parts of the radio spectrum from very low frequencies, through medium-wave frequencies, to high frequencies (shortwave), and to very high frequencies and ultra-high frequencies, and beyond, so did their antennas.

    There have been significant advances in the design of antennas over the years to improve their bandwidth, beamwidth, efficiency and other parameters. In fact, antenna development, going all the way back to the first antennas, has been one of continuous innovation.

    GNSS antennas are no different. The antennas for the first civil GPS receivers were bulky affairs. Researchers at the Massachusetts Institute of Technology initially introduced the Macrometer V-1000 in 1982, and Litton Aero Service subsequently commercialized it. It used a crossed-dipole antenna element on a 1-meter square aluminum panel and weighed 18 kilograms. The Jet Propulsion Laboratory’s demonstration GPS receiver, unveiled around the same time, used a small steerable parabolic dish that had to be sequentially pointed at GPS satellites. Both of these antennas gave way to more practical designs. Also introduced in 1982 was the Texas Instruments TI 4100, also known as the Navstar Navigator. This dual-frequency receiver used a conical spiral antenna to provide the wide bandwidth needed to cover both the L1 and L2 frequencies used by GPS.

    Subsequently, in the mid- to late-1980s, GPS and GLONASS antennas using microstrip patches were introduced for both single- and dual-frequency signal reception. The basic designs introduced then are still with us and are used for single- and multiple-frequency GNSS receivers. Miniature versions are used in some mass-market handheld receivers and for receivers in drone flight control systems. Patch antennas have also been used as elements in survey-grade antennas. A number of other GNSS antenna topologies have been developed including helices and planar spiral designs. Antennas designed for high-precision applications often integrate a ground-plane structure of some kind into the structure such as choke rings.

    You might think after more than 30 years of GNSS technology development, that there is nothing new to be expected in GNSS antenna development. You would be wrong. In this GPS World 30th anniversary issue Innovation column, we look at the design and performance of an antenna that offers high performance even in challenging environments in a relatively small package. It is appropriate that it is unveiled in this column. After all, Webster’s Dictionary has defined innovation as “the act of innovating or effecting a change in the established order; introduction of something new.” This antenna might very well be a game changer.

    Global navigation satellite systems (GNSS) have continued to evolve and have become critical infrastructure for all of society. Starting with the awesome engineering feat of the U.S. Global Positioning System and then the more recently developed constellations from other nations, we now have available refined signal structures with ever-improving positioning, navigation and timing accuracy.

    Expanding use cases has led to the design of GNSS antennas optimized for many different applications. However, new antenna design commonly requires more than simple modifications to existing GPS antenna technologies. Design agility is needed to meet requirements such as wider bandwidth, sculpted radiation patterns (we frequently talk about radiation characteristics even for a receiving antenna assuming antenna reciprocity), optimized/reduced size, better efficiency, lower noise figure, or improvements in the more esoteric parameters such as axial ratio (AR) and phase-center variation (PCV). Nothing changes the widely unappreciated fact that the antenna is the most critical element in precision GNSS systems.

    In this article, we report on the research and commercial development of a high-performance GNSS antenna by Tallysman, designated “VeroStar.” The VeroStar sets a new performance standard for an antenna of this type and supports reception of the full GNSS spectrum (all constellations and signals) plus L-band correction services. The antenna combines exceptional low-elevation angle satellite tracking with a very high-efficiency radiating element. Precision manufacturing provides a stable phase-center offset (PCO) and low PCV from unit to unit. The performance, compact size and light weight of the VeroStar antenna element make it a good candidate for modern rover and many other mobile GNSS applications.

    DESIGN OBJECTIVES

    The design of an improved, high-level GNSS antenna requires consideration of characteristics such as low-elevation angle tracking ability, minimal PCV, antenna efficiency and impedance, axial ratio and up-down ratio (UDR), antenna bandwidth, light weight, and a compact and robust form factor.

    Low-Elevation Angle Tracking. Today’s professional GNSS users have widely adopted the use of precise point positioning (PPP) including satellite broadcast of the PPP correction data. PPP correction data is broadcast from geostationary satellites, which generally hover at low-elevation angles for many densely populated regions such as Europe and much of North America. The link margin of L-band signals is typically minimal, so that improved gain at these elevation angles is an important attribute. This issue is exacerbated at satellite beam edges and northern latitudes where the link margin is further challenged — a difference of just 1 dB in antenna gain or antenna noise figure can make a big difference in correction availability. A key design parameter in this respect is the antenna G/T, being the ratio, expressed in dB per kelvin, of the antenna element gain divided by the receiver system noise temperature, typically determined by the antenna noise figure. The G/T objective for this antenna was –25.5 dB/K at a 10-degree elevation angle.

    The gain of most GNSS antenna elements, such as patches and crossed dipoles, rolls off rapidly as the elevation angle decreases toward the horizon. The polarization also becomes linear (rather than circularly polarized) at the lower elevation angles, due to the existence of a ground plane, necessary to increase gain in the hemisphere above the antenna. Improved gain close to the horizon also increases the ability of the receiver to track low-elevation-angle satellites with a concomitant improvement in the dilution of precision parameters (DOPs; a series of metrics related to pseudorange measurement precision).

    Most of the commercially available GNSS rover antennas have a peak gain at zenith of about 3.5 dBic to 5 dBic with a roll-off at the horizon of 10–12 dB (dBic refers to the antenna gain referenced to a hypothetical isotropic circularly polarized antenna). Typically, this provides an antenna gain at the horizon, at best, of about –5 dBic, which is insufficient for optimized L-band correction usage. In some studies, different antenna types such as helical elements have been proposed to overcome this issue. However, their cylindrical shape and longer length makes them unsuitable for many rover applications. Furthermore, the helix suffers from back lobes that can make the antenna more susceptible to reception of multipath signals from below the upper hemisphere of the antenna.

    In the VeroStar design, we used wide-bandwidth radiating elements (referred to here as “petals”) that surround a distributed feed network. The petal design is important to achieve superior right-hand circularly polarized (RHCP) gain at low-elevation angles.

    Tight Phase-Center Variation. The phase center of an ideal antenna is a notional point in space at which all signals are received or transmitted from, independent of the frequency or elevation or azimuth angle of the signal incidence. The phase centers of real-life antennas are less tidy, and the PCV is a measure of the variation of the “zero” phase point as a function of frequency, elevation and azimuth angles. Correction data for phase-center variation is commonly encoded in a standardized antenna exchange format or Antex file, which can be applied concurrently for precision applications.

    The azimuthal orientation of rover antennas is typically unknown, so that errors for specific orientations of the antenna in the horizontal plane cannot be accounted for. The PCV correction data provided in an Antex file is usually provided as a function of elevation angle and frequency, but with averaged azimuth data for each elevation angle and frequency entry (noazi corrections). Thus, corrections can be applied for each frequency and elevation angle, but errors due to the variation in the azimuthal PCV cannot be corrected in the receiver. For real-time kinematic (RTK) systems, the net system error is the root-mean-square sum of the base and rover antenna PCVs. It is usually possible to accommodate larger base-station antennas, which can commonly provide PCVs approaching +/- 1 mm (such as those from Tallysman VeraPhase or VeraChoke antennas). In this case, the accuracy of the combined system is largely determined by the PCV of the smaller rover GNSS antenna. Thus, even with correction data, azimuthal symmetry in the rover antenna is key. In the VeroStar, this was addressed by obsessive focus on symmetry for both the antenna element structure and the mechanical housing design.

    Antenna Efficiency and Impedance. Antenna efficiency can be narrowly defined in terms of copper losses of the radiating elements (because copper is not a perfect conductor), but feed network losses also contribute so that the objective must be optimization of both. Physically wide radiating elements are a basic requirement for wider bandwidth, and copper is the best compromise for the radiator metal (silver is better, but expensive and with drawbacks). This is true in our new antenna, which has wide radiating copper petals.

    However, the petals are parasitic resonators that are tightly coupled to a distributed feed network, which in itself is intrinsically narrowband. The resulting wide bandwidth response results from the load on the feed network provided by the excellent wideband radiation resistance of the petals.

    This arrangement was chosen because the resulting impedance at the de-embedded antenna feed terminals is close to the ideal impedance needed (50 ohms), thus requiring minimal impedance matching. The near ideal match over a wide bandwidth is very important because it allowed the impedance to be transformed to ideal using a very short transmission line (less than one-quarter of a wavelength), which included an embedded infinite balun (a balun forces unbalanced lines to produce balanced operation).

    Each of the orthogonal exciter axes are electrically independent and highly isolated electrically (better than –30 dB), even with the parasitic petal coupling. To achieve the desired circular polarization, the two axes are then driven independently in phase quadrature (derived from the hybrid couplers).

    Thus, the inherently efficient parasitic petals combined with the absolutely minimized losses of the distributed feed network has resulted in a super-efficient antenna structure that will be difficult to improve upon.

    Axial and Up-Down Ratio. AR characterizes the antenna’s ability to receive circularly polarized signals, and the UDR is the ratio of gain pattern amplitude at a positive elevation angle (α) to the maximum gain pattern amplitude at its mirror image (–α). Good AR and UDR across the full bandwidth of the antenna ensure the purity of the reception of the RHCP GNSS signals and multipath mitigation. GNSS signals reflected from the ground, buildings or metallic structures such as vehicles are delayed and their RHCP purity is degraded with a left-hand circularly polarized (LHCP) component. Because the VeroStar antenna has more gain at low-elevation angles, a very low AR and a high UDR are even more important for mitigating multipath interference. The design objective was an AR of 3 dB or better at the horizon.

    A Light, Robust and Compact Design. The user community demands ever smaller antennas from antenna manufacturers, but precision rover antennas are typically required to receive signals in both the low (1160 to 1300 MHz) and high (1539 to 1610 MHz) GNSS frequency bands. An inescapable constraint limits the bandwidth of small antennas, so that full-bandwidth (all GNSS signals) rover antennas are unavoidably larger. To date, probably the smallest, high performance all-band antenna was the original Dorne & Margolin C146-XX-X (DM) antenna, which was in its time a tour-de-force.

    The overall objective for our antenna was to design a small and light-weight radiating element (given the full bandwidth requirement) with a ground-plane size of around 100 millimeters, element height of 30 millimeters or lower, and a weight of 100 grams or less. Ideally, it would be possible to build a smaller version, perhaps with a degree of compromised performance. The applications envisaged for the VeroStar included housed antennas (such as for RTK rovers) and a lightweight element suitable for mobile applications such as drones or even cubesats.

    ANTECEDENTS

    The central goal of this project was a precision antenna with a broad beamwidth and a good AR combined with a very tight PCV. The objective was to provide for reception of signals from satellites at low-elevation angles, particularly necessary for reception of L-band correction signals, which can be expected to be incident at elevation angles of 10 degrees to 50 degrees above the horizon.

    A starting point for this development was an in-depth study of the well-known DM antenna. This antenna has been used for decades in GPS reference stations (usually in choke-ring antennas). It exhibits a higher gain at low-elevation angles (about –3 dBic at the horizon) compared to other antennas on the market (typically –5 dBic or less) and fairly good phase-center stability in a compact design. The antenna structure consists of two orthogonal pairs of short dipoles above a ground plane, with the feeds at the midpoint of the dipoles, as shown in FIGURE 1(a). The antenna can be considered in terms of the ground-plane image, replacing the ground plane with the images of the dipole as shown in FIGURE 1(b). The antenna structure then takes on the form of a large uniform current circular loop similar to the Alford Loop antenna, developed at the beginning of World War II for aircraft navigation.

    FIGURE 1. (a) Dorne & Margolin (DM) antenna current distribution; (b) Alford Loop antenna. (Image: Tallysman)
    FIGURE 1. (a) Dorne & Margolin (DM) antenna current distribution; (b) Alford Loop antenna. (Image: Tallysman)

    But the DM antenna does suffer from some drawbacks. By modern standards, the feed network is complex and lossy with costly fabrication, which affects repeatability and reliability. The AR at the zenith is marginal (up to 1.5 dB) and further degrades to 7 dB at the horizon, a factor that becomes less relevant in a choke-ring configuration where the DM element is the most commonly used. However, we took our inspiration from the DM structure and give a nod to its original developers.

    The structure of the VeroStar antenna is shown in FIGURE 2(a). It consists of bowtie radiators (petals) over a circular ground plane. The petals are coupled to a distributed feed network comprised of a simple low-loss crossed dipole between the petals and the ground plane. The relationship between the petals and the associated feed system provides a current maximum at the curvature of the petals instead of at the center of the antenna as seen in FIGURE 2(b), and in this respect achieves a current distribution similar to that of the DM element.

    FIGURE 2 . (a) VeroStar antenna element; (b) VeroStar antenna current distribution. (Images: Tallysman)
    FIGURE 2 . (a) VeroStar antenna element; (b) VeroStar antenna current distribution. (Images: Tallysman)

    This arrangement increases the gain at low-elevation angles, which greatly improves the link margin for low-elevation angle GNSS and L-band satellites. The circular polarization of the antenna at low-elevation angles can be significantly improved by optimizing the petal’s dimensions such as its height, width and angle with respect to the ground plane. This solves the problem of asymmetry between the electric and magnetic field planes of the antenna radiation pattern, which usually degrades the AR at low-elevation angles. Based on the studies conducted in our project, it was found that the bowtie geometry of the radiators, as well as its coupling to the feeding network, can improve both the impedance and AR bandwidth. By these means, we were able to produce a very wideband, low-loss antenna covering the entire range of GNSS frequencies from 1160 to 1610 MHz. The matching loss associated with the feed network is under 0.3 dB, and the axial ratio remains around 0.5 dB at the zenith and is typically under 3 dB at the horizon over the whole GNSS frequency range.

    In the early stages of the project, we thought that just four petals would be adequate for our purpose. However, as we progressed with further experimentation and simulation, it became clear that increasing the number of petals substantially improved symmetry, but at the cost of complexity. Ultimately, we determined that eight petals provided considerably better symmetry than four petals with an acceptable compromise with respect to feed complexity.

    MEASUREMENTS

    The far-field characteristics of the VeroStar antennas were measured using the Satimo anechoic chamber facilities at Microwave Vision Group (MVG) in Marietta, Georgia, and at Syntronic R&D Canada in Ottawa, Ontario. Data were collected from 1160 to 1610 MHz to cover all the GNSS frequencies.

    Radiation Patterns and Roll-Off. The measured radiation patterns at different GNSS frequencies are shown in FIGURE 3. The radiation patterns are normalized, showing the RHCP and LHCP gains on 60 azimuth cuts three degrees apart. The LHCP signals are significantly suppressed in the upper hemisphere at all GNSS frequencies. The difference between the RHCP gain and the LHCP gain ranges from 31 dB to 43 dB, which ensures an excellent discrimination between the signals. Furthermore, for other upper hemisphere elevation angles, the LHCP signals stay 22 dB below the maximum RHCP gain and even 28 dB from 1200 to 1580 MHz.

    Figure 3 also shows that the antenna has a constant amplitude response to signals coming at a specific elevation angle regardless of the azimuth angle. This feature yields an excellent PCV, which will be discussed later.

    FIGURE 3 . Normalized radiation patterns of the VeroStar antenna on 60 azimuth cuts of the GNSS frequency bands. (Data: Tallysman)
    FIGURE 3 . Normalized radiation patterns of the VeroStar antenna on 60 azimuth cuts of the GNSS frequency bands. (Data: Tallysman)

    FIGURE 4 shows a comparison of the VeroStar roll-off (that is, lower gain at the horizon) with six other commercially available rover antennas measured during the same Satimo session. The VeroStar roll-off is significantly lower than the other rover antennas. The amplitude roll-off from the VeroStar boresight (zenith) to horizon is between 6.5 to 8 dB for all the frequency bands.

    FIGURE 4. Comparison of the VeroStar roll-off versus six commercially available rover antennas. (Data: Tallysman)
    FIGURE 4. Comparison of the VeroStar roll-off versus six commercially available rover antennas. (Data: Tallysman)

    High gain at low-elevation angles (low roll-off) will cause the antenna to be more susceptible to multipath interference. Multipath signals are mainly delayed LHCP and RHCP signals. If they arrive at high-elevation angles, there is no issue because the AR of the antenna is low at those angles — thus there will be minimal reception of the multipath signals. However, in conventional antennas, low-elevation-angle multipath degrades observations due to the poor AR performance and low UDR. At lower elevation angles, our antenna has exceptional AR performance and good UDR, which significantly reduces multipath interference. Measurements in a high multipath environment were performed with the antenna and compared to other commercial rover antennas. The measurements show that the phase noise at a 5-degree elevation angle is approximately 6 to 10 millimeters over all GNSS frequencies. The other antennas perform similarly, but have a higher roll-off. This shows that the VeroStar provides a strong signal at low-elevation angles and also has a high level of multipath mitigation performance.

    Antenna Gain and Efficiency. FIGURE 5 shows the RHCP gain of our antenna at the zenith and at a 10-degree elevation angle for all GNSS frequencies. The measurements show that the antenna exhibits a gain range at the zenith from 4.1 dBic at 1160 MHz to 3.6 dBic at 1610 MHz. The antenna gain at a 10-degree elevation angle varies from –1.45 dBic to –2.2 dBic and is maximum in the frequency range used to broadcast L-band corrections (1539 to 1559 MHz). The radiation efficiency of the antenna is between 70 to 89 percent over the full bandwidth. This corresponds to an inherent (“hidden”) loss of only 0.6 to 1.5 dB, including copper loss, feedline, matching circuit and 90-degree hybrid coupler losses. This performance is a substantial improvement over other antenna elements such as spiral antennas, which exhibit an inherent efficiency loss of close to 4 dB at the lower GNSS frequencies. With the integration of wideband pre-filtering as well as a low-noise amplifier (LNA), we measured a G/T of –25 dB/K at a 10-degree elevation angle.

    FIGURE 5. RCHP gain at zenith and 10-degree elevation angle. (Data: Tallysman)
    FIGURE 5. RCHP gain at zenith and 10-degree elevation angle. (Data: Tallysman)

    Axial Ratio. The AR values of the VeroStar antenna at different elevation angles are shown in FIGURE 6. The antenna has exceptional AR performance over all GNSS frequency bands and at all elevation angles, with the value no greater than 3.5 dB. This increases the antenna’s ability to reject LHCP signals caused by reflections from nearby cars or buildings. Therefore, the susceptibility of the antenna to multipath interference is greatly reduced.

    FIGURE 6 Axial ratio versus frequency of the VeroStar at different elevation angles. (Data: Tallysman)
    FIGURE 6 Axial ratio versus frequency of the VeroStar at different elevation angles. (Data: Tallysman)

    In FIGURE 7, the AR performance of the antenna at the horizon is compared to six commercial rover antennas. The VeroStar antenna has an average AR of 2 dB at the horizon (competitive antennas are typically around 6 dB), showing its ability to track pure RHCP signals and enabling outstanding low-elevation-angle multipath mitigation.

    FIGURE 7. Comparison of the VeroStar axial ratio at the horizon versus six commercially available rover antennas. (Data: Tallysman)
    FIGURE 7. Comparison of the VeroStar axial ratio at the horizon versus six commercially available rover antennas. (Data: Tallysman)

    Phase-Center Variation. We developed Matlab code to estimate the PCV from the measured radiation pattern. FIGURE 8 shows the maximum PCV of the VeroStar antenna and six commercial rover antennas for four common GNSS frequencies. It can be seen that the antenna has a maximum total PCV of less than 2.9 millimeters for all frequency bands, which is less than the other commercially available rover antennas tested. Furthermore, the PCV of the antenna does not vary significantly with frequency. This comparison confirms the exceptional low PCV of our antenna.

    FIGURE 8. Comparison of the VeroStar maximum PCV at the horizon versus six commercially available rover antennas. (Data: Tallysman)
    FIGURE 8. Comparison of the VeroStar maximum PCV at the horizon versus six commercially available rover antennas. (Data: Tallysman)

    LOW-NOISE AMPLIFIER DESIGN

    The best achievable carrier-to-noise-density ratio (C/N0) for signals with marginal power flux density is limited by the efficiency of each of the antenna elements, the gain and the overall receiver noise figure. This can be quantified by the G/T parameter, which is usually dominated by the noise figure of the input LNA. In the LNA design for our antenna, the received signal is split into the lower GNSS frequencies (from 1160 to 1300 MHz) and the higher GNSS frequencies (from 1539 to 1610 MHz) in a diplexer connected directly to the antenna terminals and then pre-filtered in each band. This is where the high gain and high efficiency of the antenna element provides a starting advantage, since the unavoidable losses introduced by the diplexer and filters are offset by the higher antenna gain, and this preserves the all-important G/T ratio.

    That being said, GNSS receivers must accommodate a crowded RF spectrum, and there are a number of high-level, potentially interfering signals that can saturate and desensitize GNSS receivers. These signals include, for example, mobile-phone signals, particularly Long-Term Evolution (LTE) signals in the 700-MHz band, which are a hazard because of the potential for harmonic generation in the GNSS LNA. Other potentially interfering signals include Globalstar (1610 to 1618.25 MHz), Iridium (1616 to 1626 MHz) and Inmarsat (1626 to 1660.5 MHz), which are high-power communication satellite uplink signals close in frequency to GLONASS signals. The VeroStar LNA design is a compromise between ultimate sensitivity and ultimate interference rejection.

    A first defensive measure in the LNA is the addition of multi-element bandpass filters at the antenna element terminals (ahead of the LNA). These have a typical insertion loss of 1 dB because of their tight passband and steep rejection characteristics. However, the LNA noise figure is increased approximately by the additional filter-insertion loss. The second defensive measure in the design is the use of an LNA with high linearity. This is achieved without any significant increase in LNA power consumption, using LNA chips that employ negative feedback to provide well-controlled impedance and gain over a very wide bandwidth. Bear in mind that while an antenna installation might initially be determined to have no interference, subsequent introduction of new telecommunication services may change this, so interference defense is prudent even in a quiet radio-frequency environment. A potentially undesirable side effect of tight pre-filters is the possible dispersion that can result from variable group delay across the filter passband. Thus, it is important to include these criteria in the selection of suitable pre-filters. The filters in our LNA give rise to a maximum variation of less than 10 nanoseconds in group delay over both the lower GNSS frequencies (from 1160 to 1300 MHz) and the higher GNSS frequencies (from 1539 to 1610 MHz).

    CONCLUSION

    In this article, we have described the performance of a novel RHCP antenna optimized for modern multi-constellation and multi-frequency GNSS rover applications. We have developed a commercially viable GNSS antenna with superior electrical properties. The VeroStar antenna has high sensitivity at low elevation angles, high efficiency, very low axial ratio and high phase-center stability. The lightweight and compact antenna element is packaged in several robust housings designed and built for durability to stand the test of time, even in harsh environments.

    The VeroStar antenna has sufficient bandwidth to receive all existing and currently planned GNSS signals, while providing high performance standards. Testing of the antenna has shown that the novel design (curved petals coupled to crossed driven dipoles associated with a high performance LNA) has excellent performance, especially with respect to axial ratios, cross polarization discrimination and phase-center variation. These features make the VeroStar an ideal rover antenna where low-elevation angle tracking is required, providing users with new levels of positional precision and accuracy.

    ACKNOWLEDGMENTS

    Tallysman Wireless would like to acknowledge the partial support received from the European Space Agency and the Canadian Space Agency.

    REZA MOVAHEDINIA is a research engineer with Tallysman Wireless, Ottawa, Ontario, Canada. He has a Ph.D. degree in electrical and computer engineering from Concordia University, Montreal, Quebec, Canada.

    JULIEN HAUTCOEUR is the director of GNSS product R&D at Tallysman Wireless. He received a Ph.D. degree in signal processing and telecommunications from the Institute of Electronics and Telecommunications of Université de Rennes 1, Rennes, France.

    GYLES PANTHER is president and CTO of Tallysman Wireless. He holds an honors degree in applied physics from City University, London, U.K.

    KEN MACLEOD is a product-line manager with Tallysman Wireless. He received a Bachelor of Science degree from the University of Toronto. 

    FURTHER READING

    • GNSS Antennas in General

    “Antennas” by M. Maqsood, S. Gao and O. Montenbruck, Chapter 17 in Springer Handbook of Global Navigation Satellite Systems edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.

    GPS/GNSS Antennas by B. Rama Rao, W. Kunysz, R. Fante and K. McDonald, published by Artech House, Boston and London, 2013.

    “GNSS Antennas: An Introduction to Bandwidth, Gain Pattern, Polarization, and All That” by G.J.K. Moernaut and D. Orban in GPS World, Vol. 20, No. 2, Feb. 2009, pp. 42–48.

    “A Primer on GPS Antennas” by R.B. Langley in GPS World, Vol. 9, No. 7, July 1998, pp. 50–54.

    • Tallysman VeraPhase GNSS Antenna

    Static Testing and Analysis of the Tallysman VeraPhase VP6000 GNSS Antenna by R.M. White and R.B. Langley, a report prepared for Tallysman Wireless Inc., Feb. 2018.

    “Evolutionary and Revolutionary: The Development and Performance of the VeraPhase GNSS Antenna” by J. Hautcoeur, R.H. Johnston and G. Panther in GPS World, Vol. 27, No. 7, July 2016, pp. 42–48.

    • The Alford Loop

    “Ultrahigh-frequency Loop Antennas” by A. Alford and A.G. Kandoian in Electrical Engineering, Vol. 59, No. 12, Dec. 1940, pp. 843–848. doi: 10.1109/EE.1940.6435249.

  • Innovation: Improving ARAIM

    Innovation: Improving ARAIM

    An approach using precise point positioning

    By R. Eric Phelts, Kazuma Gunning, Juan Blanch and Todd Walter

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    AS WE NOTED IN THE LAST INNOVATION COLUMN, integrity — at least from a safety viewpoint — is the most important characteristic of a navigation system. Yes, accuracy, availability and continuity are also required but, without integrity, the advertised accuracy of a system might become meaningless and perhaps misleading. While GPS and user receivers are highly reliable, we cannot presume that there will never be an erroneous signal transmitted by a GPS satellite that would result in a receiver outputting a hazardously misleading position solution. While “supervisory” systems such as satellite-based augmentation systems monitor GPS signals and can alert users about defective satellites within a very short period of time, it is advantageous for a user receiver to autonomously detect problematic satellites and quarantine them so that they do not perturb the position solution.

    It is for this reason that receiver autonomous integrity monitoring (RAIM) techniques were developed. As we know, a receiver needs signals from a minimum of four satellites simultaneously to determine its 3D position and its clock offset. However, typically there are more than four satellites in view, and so multiple solutions using subsets of four satellites are possible. If five satellites are visible, then it is possible to determine that one of them is faulty, but not which one (geometry plays a role here). This is called fault detection (FD). And if six satellites are visible, the faulty satellite can be determined and then excluded from the position solution (fault detection and exclusion, or FDE). This is the basic principle of RAIM.

    Advanced RAIM (ARAIM) extends RAIM to other constellations beyond GPS. ARAIM enables the use of the newer GNSS constellations to provide better levels of performance than RAIM with GPS alone. It also uses dual-frequency measurements for enhanced vertical positioning reliability.

    Central to positioning techniques providing a safety-of-life service is the notion that the uncertainty of a provided position must be conservatively estimated and provide for both nominal uncertainty and the uncertainty of a faulted solution such as that detected using RAIM. These conservative estimates are known as the horizontal and vertical protection levels. The horizontal protection level (HPL) is the radius of a circle in the horizontal plane with its center at the true position, which describes the region that is assured to contain or bound the provided horizontal position to a very high probability. The vertical protection level is half the length of a segment in the vertical direction with its center at the true position, which describes the region that is assured to contain or bound the provided vertical position to a very high probability. The probability levels are typically taken to be 99.9999998 and 99.99999% for HPL and VPL, respectively.

    The usual approach for RAIM and ARAIM is to use the so-called “snapshot” approach, where measurements are assumed to be uncorrelated epoch to epoch. In this month’s column, a team of authors from Stanford University discusses a superior approach for ARAIM using the technique of precise point positioning.

    Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is implemented using solution separation in positioning and navigation software. Solution separation computations presume one or more GNSS satellites may be faulty, and they iteratively compute multiple position solutions comprised of subsets of the n satellites in view (n, n-1, n-2, and so on) to ensure that at least one of the solutions is fault-free. Using assumptions on the nominal and faulted uncertainty of the solutions, the software can compute conservative horizontal and vertical protection levels (PLs) by bounding the uncertainty from all the solutions. This assures (to a targeted level of probability) that the user position is contained within these limits.

    Traditional solution separation techniques generally operate as a “snapshot.” The basic measurements are dual-frequency, carrier-smoothed pseudorange (code), and errors are generally assumed to be uncorrelated from epoch to epoch. This procedure requires that errors at each time step are conservatively bounded with large uncertainties (sigmas) designed to protect the user against the worst-case error. These assumptions minimize the complexity and computational cost of the solution by providing a robust, provably safe bound. However, the PLs computed are relatively large. In addition, they can change suddenly from one epoch to the next due to changes in available satellites or platform dynamics. This can make meeting performance goals (such as continuity) for aircraft approaches more challenging.

    Solution separation procedures using techniques based on precise point positioning (PPP) implement an extended Kalman filter (EKF) to filter measurements over time. In this case, the basic measurements are dual-frequency code and carrier phase, and errors are assumed to have some correlation between each time step to the next. Accordingly, these techniques leverage higher quality measurements (that is, carrier-phase-based as opposed to code-based) to smooth and reduce large sigmas and to estimate (and calibrate) errors over time. The complexity associated with defining and characterizing the decorrelation models for the errors, so that the nominal covariance produced by the EKF conservatively describes the actual error, is significant. Also, the computational cost of estimating the error states may be substantially higher than with the traditional snapshot approach. However, the computed protection levels provide integrity and are often significantly smaller. In addition, the filtering makes them more robust to platform dynamics, which makes them well-suited for aircraft in flight.

    Flight Data: Outages and Cycle Slips. ARAIM performance may be significantly affected by aircraft dynamics. Specifically, banking can induce satellite outages and cycle slips. Outages weaken the constellation geometry and can cause sudden changes in the protection level. Frequent cycle slips prevent code measurements from being smoothed, potentially inflating protection levels of carrier-phase-smoothed code measurements for extended periods of time.

    When the outages and cycle slips are computed as a rate, a trend can be seen. Both increase notably as the relative elevation angle to the satellites decrease. FIGURE 1 shows an example of outages as a function of the apparent elevation angle of the satellites (relative to the aircraft). Cycle slips on GPS L1-L5 and Galileo El-E5a are plotted in FIGURES 2 (a) and (b), respectively.

    FIGURE 1. Outages as a function of body frame or apparent elevation angle during aircraft banking. (Image: Authors)
    FIGURE 1. Outages as a function of body frame or apparent elevation angle during aircraft banking. (Image: Authors)
    FIGURE 2a. Cycle-slip rate (per satellite-second) for GPS L1-L5. (Image: Authors)
    FIGURE 2a. Cycle-slip rate (per satellite-second) for GPS L1-L5. (Image: Authors)
    FIGURE 2b. Cycle-slip rate (per satellite-second) for E1-E5a. (Image: Authors)
    FIGURE 2b. Cycle-slip rate (per satellite-second) for E1-E5a. (Image: Authors)

    For this article, we have used the flight data from one of our earlier papers on the effect of aircraft banking on ARAIM performance (see Further Reading). With this data, we show that significant advantages of PPP can be retained even during aircraft maneuvers when outages and cycle slips threaten ARAIM continuity and availability the most.

    MODEL ASSUMPTIONS

    The traditional snapshot solution separation approach is well-established and was implemented according to the standards established by a working group operating under the U.S.-European Union Agreement on GPS-Galileo Cooperation, which has been extended to all constellations (see Further Reading). For this article, we limited the constellations to GPS and Galileo, and the prior probabilities assumed for satellite and constellation faults were as follows:

    Psat = 10-5, Pconst,GPS = 10-8 and Pconst,GAL = 10-4

    We implemented the PPP algorithm with solution separation using an EKF using dual-frequency code and carrier-phase measurements (from GPS and Galileo) with estimated parameters comprising the receiver position and velocity, clock biases for each constellation in use, a residual tropospheric delay, carrier-phase float ambiguities for each tracked carrier, multipath error, receiver differential code bias, and broadcast orbit and clock error. Modeled (not estimated) effects include solid Earth tide modeling, ocean loading, an initial tropospheric delay and relativistic effects. Many of the details of the implementation can be found in our paper “Design and Evaluation of Integrity Algorithms for PPP in Kinematic Applications” (see Further Reading).

    PPP techniques typically utilize precise ephemeris information obtained from a global network of ground reference stations such as those operating in the network coordinated by the International GNSS Service. Snapshot solution separation techniques, however, use only ephemeris information broadcast from the satellites themselves. For a proper comparison of the protection levels computed by each technique, the PPP filter was constrained to use this broadcast information.

    The model we have applied is mostly typical of a traditional PPP implementation with one significant exception — the state tracking the error contribution of the broadcast orbit and clock on each line-of-sight signal. The error contributed by the broadcast orbit and clock is handled by the filter leveraging a characterization of the rate of change of the error, then including it as an estimation state for each line of sight and only adding enough process noise to capture the slowly changing error. We have previously characterized the rate of change of the error in the broadcast orbit and clock and process noise (for GPS). Complete tables of initial state uncertainties and additional settings for process and measurement noise were provided in our earlier work (see Further Reading).

    RESULTS

    Flight data collected over a period of approximately one year was used to evaluate ARAIM performance through momentary outages and cycle slips due to aircraft dynamics. A multi-constellation, multi-frequency receiver tracked GPS (L1 C/A and L5) and Galileo (E1 and E5a) satellites. This receiver is installed in a Global 5000 jet owned and operated by the FAA William J. Hughes Technical Center. It records and stores GNSS measurements whenever flights are taken. The data we used for this article included data recorded over approximately 35 flights from September 2017 to April 2018.

    FIGURE 3 shows the trajectory and altitude information corresponding to a single flight (Flight #6) taken on Sept. 20, 2017, and FIGURE 4 compares the corresponding horizontal and vertical protection levels computed using snapshot and “broadcast” PPP techniques. For an additional reference, we also computed protection levels using PPP with precise orbits and clocks (we call this precise PPP despite the terminology redundancy) and plotted these in Figure 4, too.

    FIGURE 3b. Altitude information for Flight #6 (Sept. 20, 2017). (Image: Authors)
    FIGURE 3b. Altitude information for Flight #6 (Sept. 20, 2017). (Image: Authors)
    FIGURE 4a. Horizontal protection levels for Flight #6 (Sept. 20, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 4a. Horizontal protection levels for Flight #6 (Sept. 20, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 4b. Vertical protection levels for Flight #6 (Sept. 20, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 4b. Vertical protection levels for Flight #6 (Sept. 20, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)

    Several things are readily apparent from these comparisons. First, after the initial time required for convergence, there is a substantial reduction in the PLs using the broadcast-PPP-based approach. The precise PPP PLs, as expected, produce the largest reduction, but use additional information not available to the snapshot method. In addition, the snapshot solution separation PLs vary significantly due to cycle slips and momentary satellite outages. FIGURE 5 shows the number of satellites tracked by the receiver during this flight; red circles plotted on the snapshot protection-level line indicate when satellites are coming into and out of view. Despite numerous abrupt changes in number of measurements and measurement quality, the EKF of the PPP techniques produces PLs that are relatively smooth and continuous.

    FIGURE 5. Number of satellites tracked for Flight #6 (Sept. 20, 2017). (Image: Authors)
    FIGURE 5. Number of satellites tracked for Flight #6 (Sept. 20, 2017). (Image: Authors)

    FIGURE 6 shows the trajectory and altitude information corresponding to Flight #4 taken on Sept. 15, 2017.

    FIGURE 6a. Flight path for Flight #4 (Sept. 20, 2017). (Image: Authors)
    FIGURE 6a. Flight path for Flight #4 (Sept. 20, 2017). (Image: Authors)
    FIGURE 6b. Altitude information for Flight #4 (Sept. 20, 2017). (Image: Authors)
    FIGURE 6b. Altitude information for Flight #4 (Sept. 20, 2017). (Image: Authors)

    FIGURE 7 compares the horizontal and vertical PLs for snapshot solution separation and the PPP-based techniques.

    FIGURE 7. Horizontal protection levels for Flight #4 (Sept. 15, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 7. Horizontal protection levels for Flight #4 (Sept. 15, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 7b. Vertical protection levels for Flight #4 (Sept. 15, 2017); red circles indicate a satellite being dropped or reentering the solution.
    FIGURE 7b. Vertical protection levels for Flight #4 (Sept. 15, 2017); red circles indicate a satellite being dropped or reentering the solution.

    As in the case shown in Figure 4, the PLs in Figure 7 reveal a substantial reduction in the mean PLs computed using the PPP-based approach. And the snapshot solution separation approach displays even more variations due to momentary satellite outages. Some of the cycle slips affected enough satellites to introduce brief spikes in the PPP solution as well. These reconverge quickly, but they suggest that some tuning of the EKF can still be done to mitigate these interruptions. Still, the filtered approach produces PLs that are more robust to the outages and are substantially smaller than with the snapshot method.

    FIGURE 8 compares the horizontal and vertical PLs computed using snapshot solution separation and PPP techniques for Flight #20 — where the airplane remained stationary on the runway. In the absence of flight dynamics, the levels for all the approaches were relatively smooth. However, a few discontinuities can still be observed for the snapshot case. Also note, in the case of the broadcast PPP, the convergence time is noticeably longer. This is likely because the integer ambiguity resolution in the solution took longer to converge without platform motion.

    FIGURE 8a. Horizonta protection levels for a stationary aircraft (Flight #20, Dec. 4, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 8a. Horizonta protection levels for a stationary aircraft (Flight #20, Dec. 4, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 8b. Vertical protection levels for a stationary aircraft (Flight #20, Dec. 4, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)
    FIGURE 8b. Vertical protection levels for a stationary aircraft (Flight #20, Dec. 4, 2017); red circles indicate a satellite being dropped or reentering the solution. (Image: Authors)

    The mean horizontal and vertical PLs for both techniques are summarized in FIGURE 9. (There were issues with the data from Flight #14 and it was not processed.) The PPP approach consistently produces protection levels anywhere from 30 to 75% smaller than those computed using the snapshot approach. The mean PLs for the PPP techniques were always below those computed with the snapshot method.

    FIGURE 9a. Comparison of mean horizontal PLs for “snapshot” vs. a PPP-based technique. (Image: Authors)
    FIGURE 9a. Comparison of mean horizontal PLs for “snapshot” vs. a PPP-based technique. (Image: Authors)
    FIGURE 9b. Comparison of mean vertical PLs for “snapshot” vs. a PPP-based technique. (Image: Authors)
    FIGURE 9b. Comparison of mean vertical PLs for “snapshot” vs. a PPP-based technique. (Image: Authors)

    CONCLUSIONS

    Data from 35 flights was used to compare ARAIM protection levels computed by the traditional “snapshot” solution separation versus a PPP-based approach during both in-flight and several static scenarios. We observed that the filtering of PPP methods yields mean PLs approximately 30 to 75% of those computed using traditional methods in all cases. This improvement can be attributed to exploiting — through filtering and estimation — carrier-phase-based measurements and a time-correlation of the errors. In addition, the EKF employed by the PPP approach demonstrated improved robustness to outages and cycle slips induced by aircraft dynamics. Despite the increased complexity and computational cost, we believe that PPP approaches hold promise for significantly improving ARAIM performance.

    ACKNOWLEDGMENT

    This article is based on the paper “Evaluating the Application of PPP Techniques to ARAIM Using Flight Data” presented at ION ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–25, 2020.

    MANUFACTURER

    The flight data was recorded using a Trimble BX935-INS receiver fed by an Antcom Avionic II GNSS antenna.

    R. ERIC PHELTS is a research associate in the Department of Aeronautics and Astronautics at Stanford University, California. He received a Ph.D. in mechanical engineering from Stanford University in 2001. His research involves signal deformation monitoring for SBAS and flight-data analyses for ARAIM.

    KAZUMA (KAZ) GUNNING is a Ph.D. candidate in the GPS Laboratory at Stanford University working under the guidance of Todd Walter. He is also the navigation algorithms and architecture lead at Xona Space Systems in San Mateo, California. His research interests are in precise point positioning and integrity.

    JUAN BLANCH is a senior research engineer at Stanford University, where he works on integrity monitoring algorithms for radionavigation. He received a Ph.D. in aeronautics and astronautics from Stanford University in 2003. He has received The Institute of Navigation (ION) Parkinson and Early Achievement awards.

    TODD WALTER is a research professor in the Department of Aeronautics and Astronautics at Stanford University. He received his Ph.D. in applied physics from Stanford University in 1993. His research focuses on implementing high-integrity air navigation systems. He has received the ION Thurlow and Johannes Kepler awards. Walter is also a Fellow of the ION and has served as its president.

    FURTHER READING

    • Authors’ Conference Paper

    “Evaluating the Application of PPP Techniques to ARAIM Using Flight Data” by R.E. Phelts, K. Gunning, J. Blanch and T. Walter in Proceedings of ITM 2020, the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 21–24, 2020, pp. 379–385.

    • Receiver Autonomous Integrity Monitoring

    “A Baseline RAIM Scheme and a Note on the Equivalence of Three RAIM Methods” by R.G. Brown in Navigation, Vol. 39, No. 3, Fall 1992, pp. 301–316.

    • Advanced Receiver Autonomous Integrity Monitoring

    “SBAS Corrections for PPP Integrity with Solution Separation” by K. Gunning, J. Blanch and T. in Proceedings of ITM 2019, the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 28–31, 2019, pp. 707–719.

    “Design and Evaluation of Integrity Algorithms for PPP in Kinematic Applications” by K. Gunning, J. Blanch, T. Walter, L. de Groot and L. Norman in Proceedings of ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018, pp. 1910–1939.

    “Effect of Aircraft Banking on ARAIM Performance” by R.E. Phelts, J. Blanch, K. Gunning, T. Walter and P. Enge in Proceedings of ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018, pp. 2632–2641.

    “ARAIM in Flight Using GPS and GLONASS: Initial Results from a Real-time Implementation” by R.E. Phelts, J. Blanch, Y.-H. Chen, P. Enge and S. Riley in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 3264–3269.

    Milestone 3 Report by EU-U.S. Cooperation on Satellite Navigation, Working Group C, ARAIM Technical Subgroup, Feb. 26, 2016.

    • Precise Point Positioning

    “Two Are Better Than One: Multi-frequency Precise Point Positioning Using GPS and Galileo” by F. Basile, T. Moore, C. Hill, G. McGraw and A. Johnson in GPS World, Vol. 29, No. 10, October 2018, pp. 27–37.

    “Where Are We Now, and Where Are We Going? Examining Precise Point Positioning Now and in the Future” by S. Bisnath, J. Aggrey, G. Seepersad and M. Gill in GPS World, Vol. 29, No. 3, March 2018, pp. 41–48.

    “Precise Point Positioning” by J. Kouba, F. Lahaye and P. Tétreault, Chapter 25 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.

  • Innovation: Integrity for safe navigation

    Innovation: Integrity for safe navigation

    A key feature of a new high-accuracy GNSS correction service

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    INTEGER VITAE SCELERISQUE PURUS. So wrote the Roman poet Horace at the beginning of one of his odes — one which, incidentally, was sung by college choirs at one time. It is usually translated as “upright of life and free from wickedness” and is just about the only common Latin quotation in which we find the word “integer.”

    Besides upright, the word can be translated as unimpaired, perfect or whole. It is this latter meaning that the English mathematician Thomas Digges appropriated to describe whole numbers. The modern mathematics definition of the set of integers includes the additive inverses of the whole numbers plus zero. We have to worry about the integer nature of carrier-phase ambiguities when trying to achieve high-precision GNSS positioning but that is a story for another day.

    The Latin word integer is the root of the English word integrity. In everyday speech, integrity means the quality of being honest or trustworthy (and having strong moral principles). But it is also used to describe something that is unimpaired or uncorrupted, especially in regard to electronic data such as that provided by a navigation system.

    As I wrote in an Innovation column back in 1999, “The performance of any navigation system is characterized by its accuracy, availability, continuity, and integrity. From a safety point of view, integrity is arguably the most important factor. Without some assurance of a system’s integrity, we have no way of knowing whether the information we receive is correct: How are we to know whether a navigation system is actually achieving its advertised accuracy and not misleading us with faulty information?” Navigation systems that provide safety-of-life services must ensure a very high level of integrity. For example, the Wide Area Augmentation System (WAAS) continuously assesses the integrity of GPS satellite signals as well as its own corrections, warning WAAS users when a failure is encountered within about 6 seconds of failure. This helps to ensure that aircraft do not use misleading data that could potentially create hazards.

    And now, high-precision GNSS positioning technology using real-time augmentation is being adopted for autonomous applications in the automotive, rail, aviation and marine industries. These applications need high integrity in their position determinations in addition to high accuracy. As with the pioneering non-autonomous aviation use, augmentation services for the new market will need to monitor many aspects of their service to ensure a high level of integrity including the high-end data processing algorithms, real-time data transmission, end-to-end encryption, and functional safety assurance. This will be a challenging task that will require a multi-disciplinary approach, deep understanding of GNSS error modeling and risk assessment.

    In this month’s column, we look at the design, construction, operation and performance of the first safety-critical, high-accuracy augmentation service created specifically for autonomous applications.

    In addition to the need for high accuracy, the adoption of high-precision GNSS positioning technology for autonomous applications in the automotive, rail, aviation and marine industries has brought with it the need for high integrity and reliability. GNSS integrity concepts had their beginning in safety-critical applications in the aviation and marine industries, which have used GNSS to provide absolute position for precision runway approach, enroute navigation, port approaches, open sea and coastal waterway navigation.

    For precision GNSS users (those using precision or high-end equipment) in the surveying, construction and agriculture industries, the focus has primarily been on accuracy. Over the past decade, real-time networks have been developed to offer sub-2-centimeter performance to end users. Although some integrity information has been provided, it has often been in the form of disturbance indices that network operators can use to inform users of suspected down time or periods of poor performance. But the information lacks a functional safety component. Additionally, this information has not typically been integrated in real time into position engines to aid in the generation of reliable integrity parameters for the end users.

    Although GNSS does have limitations, particularly in urban environments, GNSS equipment is one of the few sensor types available to system integrators that can provide absolute position in autonomous applications.

    This realization — combined with the further miniaturization, lower power consumption and expansion of inexpensive multi-frequency, multi-constellation GNSS chips capable of real-time-kinematic- (RTK-) style processing — has made the adoption of GNSS for mass-market applications very appealing.

    Most mass-market applications don’t have the same accuracy requirements that drive the professional high-precision market. TABLE 1 summarizes applications that can benefit from a high-precision GNSS correction service. In most cases, decimeter-to-meter-level accuracy is typically acceptable. Reliability becomes more critical for these applications.

    Table 1. Applications that can benefit from a high-precision GNSS service with integrity. (Data Sapcorda)
    Table 1. Applications that can benefit from a high-precision GNSS service with integrity. (Data: Sapcorda)

    The integrity demand, which we define as the degree of difficulty an application poses to the integrity monitoring system, is based on the required accuracy, availability, failure rate and continuity requirements of the application. Applications with a high integrity demand pose the most difficult challenges.

    With the spread of autonomous applications in various areas, the likelihood of liability and legal cases being decided based on PVT data provided by the systems is high. This eventuality brings with it a need for a non-proprietary open standard for ensuring consistent implementation of the integrity information and functional safety along with the separation of end-user and provider responsibility. In this article, we describe the requirements and concepts for a high-precision GNSS correction system with high integrity.

    SYSTEM OVERVIEW

    Our Sapcorda correction service provides high-precision GNSS correction data on a continental scale. Its core component is an underlying tracking network of reference stations used to generate the precise corrections. The reference stations operate in real time and continuously transmit their data to the data control center. The data control center processes the data, computing orbit, clock, instrumental bias and atmosphere corrections and integrity information, and then encrypting the data before broadcasting it to the end user (see FIGURE 1).

    FIGURE 1. High-level description of Sapcorda’s GNSS correction service. (Image: Sapcorda)
    FIGURE 1. High-level description of Sapcorda’s GNSS correction service. (Image: Sapcorda)

    The corrections are broadcast in the Safe Position Augmentation for Real Time Navigation (SPARTN) format  developed by a consortium of GNSS manufacturers and service providers, via two communication channels, L-band and the internet. The data is then received by the end users who must decrypt it before it is used in processing. The SPARTN correction format consists of a set of messages that broadcast the GNSS corrections in a state-space representation. With our network, Sapcorda can offer a high-accuracy positioning service with fast convergence. An example of positioning performance for a monitoring station in Sapcorda’s European network coverage area is shown in FIGURE 2. The typical accuracy level is close to that of traditional network RTK services.

    
FIGURE 2. Horizontal position performance for a monitoring site in Europe using Sapcorda’s high-precision service. (Image: Sapcorda)
    FIGURE 2. Horizontal position performance for a monitoring site in Europe using Sapcorda’s high-precision service. (Image: Sapcorda)

    The system provides coverage for both North America and Europe as shown in FIGURE 3. Unlike traditional local or regional network RTK systems, Sapcorda’s network provides seamless coverage on the continental scale and operates in broadcast-only mode.

    FIGURE 3. Initial operation coverage of Sapcorda's high-precision GNSS correction service. (Image: Sapcorda)
    FIGURE 3. Initial operation coverage of Sapcorda’s high-precision GNSS correction service. (Image: Sapcorda)

    INTEGRITY CONCEPTS

    The integrity of a system can be described as the trustworthiness of the positions generated by the position engine. Trustworthiness is defined by the protection level associated with a given solution. Many of the concepts related to GNSS integrity originated from the development of the Wide Area Augmentation System (WAAS). The integrity concept was formalized by the Stanford Integrity Diagram, which outlines the key concepts related to integrity. TABLE 2 defines the terminology surrounding the integrity concept.

    Table 2. Integrity terms. (Data Sapcorda)
    Table 2. Integrity terms. (Data Sapcorda)

    The integrity risk is the probability that a user will experience a position error larger than the alert limit without an alarm being triggered. When this occurs, the user is in a potentially dangerous situation as the system is providing dangerously misleading information to the user, who is unaware.

    The protection levels are computed based on the expected behavior of the error sources encountered in a GNSS positioning system. If the protection level is less than the system’s alert limit, then the system is operating within its normal bounds. If the error sources are not properly monitored or quantified, protection levels become optimistic. This occurs when the true position error, which is typically unknown, exceeds the protection level supplied by the system. When this situation occurs, it is labeled hazardously misleading information (HMI) because the system may believe that its position is more accurate than it truthfully is. If the true position error remains less than the alert limit, then this is classified as misleading information. As the true position is not beyond the alert limit, the operator/system can continue to rely on this information without being in a potentially dangerous scenario.

    To define the true integrity risk of the system, it is necessary to understand its error sources, threat models, frequency of occurrences and potential failure modes. Many threats could render a correction service unavailable, including hardware failures, data outages or software bugs, atmospheric anomalies and satellite failures. The following section describes these threats along with the capabilities used for monitoring them.

    Error Sources. The primary error sources in high-precision GNSS positioning are described in TABLE 3.

    Table 3. GNSS network error sources, their magnitude and mitigation approach. (Data Sapcorda)
    Table 3. GNSS network error sources, their magnitude and mitigation approach. (Data Sapcorda)

    Although not mentioned in this table, additional error sources include site displacement effects such as solid earth tides, ocean tide loading and polar tides; carrier-phase wind-up at both the receiver and satellite; and satellite and receiver antenna phase-center variations and relativistic delays. These effects must be consistently modeled at both the server and the end-user for centimeter-level positioning.

    Based on the error sources described in Table 3, it is necessary to convert this information into a format that can be used by the position engine to derive protection levels for each solution. How the final protection level is derived by a position engine is not within the scope of this article. For this, several approaches can be used including carrier-phase-based receiver autonomous integrity monitoring (CRAIM), solution separation and others.

    The following equation can be used to describe the overall error contribution for each measurement:

    Authors

    where

    Photo:  is the total uncertainty for satellite i

    Photo:  is the uncertainty of the ionosphere model

    Photo:  is the uncertainty of the troposphere model

    Photo: is the uncertainty of the combined orbit, clock and bias (ephemeris) corrections

    Photo:  is the uncertainty of the measurements in the given environment

    The terms Photo:, Photo:and Photo: are derived from the real-time reference network operator while the term must be computed by the end-user receiver. This final term Photo: is perhaps the most difficult to determine, particularly for kinematic environments, as the value is highly dependent on antenna quality, multipath and measurement quality.

    PERFORMANCE AND RESULTS

    We processed 24 hours of data at three stations covered by Sapcorda’s European network and within the red circle shown in FIGURE 5.

    FIGURE 5. Location of stationary testing carried out within Sapcorda's European network. (Image: Sapcorda)
    FIGURE 5. Location of stationary testing carried out within Sapcorda’s European network. (Image: Sapcorda)

    The test stations were situated in an open-sky environment with high-quality geodetic antennas and receivers. The position results and protection levels were derived from Sapcorda’s own position engine.

    FIGURE 6. Integrity plots for the horizontal error and protection levels for three stations within Sapcorda's European network coverage area.(Image: Sapcorda)
    FIGURE 6. Integrity plots for the horizontal error and protection levels for three stations within Sapcorda’s European network coverage area.(Image: Sapcorda)

    FIGURE 6 shows the horizontal component integrity plots for the three stations. The protection levels are computed for the five-sigma level. In all three examples, the protection level can properly bound the horizontal position error. In terms of the measured accuracy, the typical performance observed at the three stations is between 3 and 7 centimeters for the 95th percentile.

    In addition to the stationary testing, two kinematic trials were carried out in cooperation with a system integrator. The integrator setup consisted of a commercial RTK receiver and position engine being fed with SPARTN corrections. The equipment was mounted onto the vehicle used for the tests. Both tests were carried out in an urban environment, which introduced measurement outages due to trees, overpasses and urban canyons. FIGURE 7 shows the area in which the kinematic trails were carried out, as well as some of the urban conditions with which the system had to contend.

    FIGURE 7. Location of kinematic trials using Sapcorda's North American correction service and examples of the environment encountered during the testing. (Image: Sapcorda)
    FIGURE 7. Location of kinematic trials using Sapcorda’s North American correction service and examples of the environment encountered during the testing. (Image: Sapcorda)

    FIGURES 8 and 9 show the position performance and integrity plots for the two kinematic trial scenarios. The reference trajectory was computed using a short baseline post-processed kinematic solution computed with a third- party application. The typical accuracy of the Sapcorda-enabled solution was on the order of 2 to 4 centimeters, while the maximum error was 10 centimeters. In both cases, the protection levels were able to properly bound the horizontal position error. Figure 8 shows an area of increased position error, which occurs around the 22.6- to 22.7-hour mark of the day. This period coincides with the image in the bottom right of Figure 7, where the vehicle passes into a difficult environment with overhead trees and walkways, as well as significant shading from a tall building. Even in this type of environment, the protection levels were able to properly bound the horizontal position error.

    FIGURE 8a. Horizontal position performance for kinematic trial #1. The red line indicates the 1-sigma error of the position engine. (Image: Sapcorda)
    FIGURE 8a. Horizontal position performance for kinematic trial #1. The red line indicates the 1-sigma error of the position engine. (Image: Sapcorda)
    FIGURE 8b. Horizontal position performance for kinematic trial #1: The 5-sigma integrity diagram. (Image: Sapcorda)
    FIGURE 8b. Horizontal position performance for kinematic trial #1: The 5-sigma integrity diagram. (Image: Sapcorda)
    FIGURE 8b. Horizontal position performance for kinematic trial #1: The 5-sigma integrity diagram. (Image: Sapcorda)
    FIGURE 8b. Horizontal position performance for kinematic trial #1: The 5-sigma integrity diagram. (Image: Sapcorda)
    FIGURE 9b. Horizontal position performance for kinematic trial #2: The 5-sigma integrity diagram. (Image: Sapcorda)
    FIGURE 9b. Horizontal position performance for kinematic trial #2: The 5-sigma integrity diagram. (Image: Sapcorda)

    In addition to the position performance, re-initialization time plays a critical role for precise positioning systems operating in difficult environments. Due to the regular outage and signal blockages, which occur in urban environments, the re-initialization time is critical to providing high availability. Traditional precise point positioning (PPP) systems, even those that perform ambiguity resolution, can take anywhere from 5 to 20 minutes to re-initialize and achieve an acceptable accuracy level (typically 10 centimeters) after a complete outage. Researchers in both academia and industry have developed several methods to reduce this time by “bridging the gap” after outages.

    However, these approaches rely on assumptions about either the vehicle trajectory or the stability of the ionosphere before and after outages. The impact of these assumptions on overall integrity have not been adequately studied. Systems that rely on inertial measurement units (IMUs) to constrain the position after an outage introduce a dependency between what should be two independent sensors in the overall system.

    FIGURE 10 shows the re-initialization time of the integrator’s position engine when using Sapcorda’s correction service. In this case, the re-initialization time is computed as the time it takes to return to RTK-ambiguity-fixed mode as indicated in the position engine output after an outage. Results based on comparisons against short-baseline RTK positioning showed typical accuracies below 10 centimeters upon re-initialization. In this definition, the time of the outage is included in the overall re-initialization time. In nearly all of the 42 occurrences, the time to re-initialize is less than 10 seconds. This is sufficient to allow an IMU to provide position updates during the GNSS outage.

    FIGURE 10. Re-initialization time of the integrator’s position engine enabled by Sapcorda’s correction service. (Image: Sapcorda)
    FIGURE 10. Re-initialization time of the integrator’s position engine enabled by Sapcorda’s correction service. (Image: Sapcorda)

    SYSTEM DESIGN CONSIDERATIONS

    In addition to understanding GNSS error sources and performance, it is also important to consider the integrity of the entire system. This includes software development processes, hardware selection, data communication standards and security.

    Software Design

    Aspects needing to be addressed include:

    Software Coding Standards. As software is used more and more in safety-critical scenarios, standards have been developed to minimize common errors and failures. Some standards relevant for safety-critical applications development include International Organization for Standardization (ISO) standard 26262 and Motor Industry Software Reliability Association (MISRA) C/C++ coding standards. Many of these standards can be automated via the static analysis tools described below.

    Functional Safety. The objective of this analysis is to understand the possible failure modes of a system, how likely they are to occur, and how to mitigate their risk. Several methods can be applied for functional safety analysis. One such approach is failure mode effect analysis (FMEA). In general, functional safety analysis is a complex task requiring a wide range of experience and expertise. Understanding how design or feature choices impact overall failure modes is also critical for simplifying the number of cases and overall system complexity.

    Test Coverage. Unit tests provide the fundamental verification that a function can perform its expected task. Coverage analysis tools provide insight into which sections, paths and combinations are being tested. Various metrics are possible, including:

    • statement coverage: measures the number of executable lines of code that are evaluated
    • branch coverage: measures which code paths are being evaluated (for example, if statements, both true and false must be covered)
    • modified condition/decision coverage (MC/DC): in addition to checking all branches, all combinations of branches must be considered.

    The degree of effort to meet target coverage metrics greatly varies based on the type of metric chosen.

    Code Quality Metrics. Code quality metrics attempt to reduce the complexity of functions and methods in the software. Code quality metrics may include:

    • cyclomatic complexity scores
    • establishing the maximum number of control statements within a function
    • establishing the maximum number of lines or methods called within a single function.

    Static Analysis. Static code analysis provides an examination of source code prior to execution. It can detect common implementation issues such as divide-by-zero errors, bounds overrun, poorly defined loops or control statements, among others. Most commercial products provide support for MISRA C/C++ guidelines and other best practices for safety-critical applications.

    Automated Testing. Test automation is critical for monitoring performance changes and ensuring high-quality code changes. Critical scenarios such as leap-second changes, week rollovers and ephemeris failures can be logged and then used as part of the automated test plan. And, as bugs emerge, adding additional test scenarios for these is also beneficial.

    Data Communication Protocol

    One must also consider several aspects related to the transmission of the correction service to users.

    Open Source. A standardization of an open-source data communication protocol for mass-market applications allows for a receiving system to employ multiple corrections from more than a single specific provider without requiring independent functional safety requirements. This can provide a much higher level of redundancy than is possible when depending on only a single service provider.

    Integrity and Functional Safety. To properly quantify the protection level, it is necessary to provide quality information about the corrections being provided by the service. Employing “do not use” flags ensures users drop satellites that may be unhealthy or performing poorly. General system status messages identifying the cause of a failure are also critical for proper separation of issues between server and recipient.

    Encryption and Anti-Spoofing. As the use of GNSS expands, the threat of spoofing has become more significant. Data message encryption must be robust and resilient to protect the user of the data against external threats.

    Self-Contained and Repeatable. Replication of events is important for safety-critical applications. A message format used for such applications should be self-contained and not rely on any external sources for factors such as initialization or the expansion of data. This may include the expansion of time-tagged data, or limiting the expansion of ephemeris to a specific Issue of Data Ephemeris (IODE).

    SUMMARY

    High-precision GNSS correction services for applications requiring both accuracy and integrity will continue to grow. To meet these demands, GNSS correction services that previously focused on accuracy as their primary goal must begin to work toward providing adequate integrity information to provide reliable positions and protection levels. This requires a multidisciplinary approach to achieve an in-depth understanding of GNSS error sources, integrity concepts and functional safety.

    End users will benefit from the clear separation of the server and recipient responsibilities and through an open communication standard that facilitates the use of multiple correction service providers and is developed with safety and integrity at its core.

    The adoption of formal safety practices, including software development strategies to reduce risk and mitigate errors, is also critical in achieving a reliable and safe high-precision correction service.

    ACKNOWLEDGMENT

    This article is based on the paper “Integrity for High Accuracy GNSS Correction Services” presented at ION ITM 2019, the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 28–31, 2019.

    LANDON URQUHART is the R&D engineering manager for Sapcorda Services Inc., with offices in Berlin and Hanover, Germany, and Scottsdale, Arizona, USA. He obtained his M.Sc.E. from the Department of Geodesy and Geomatics Engineering at the University of New Brunswick (UNB), Fredericton, Canada. His research interests are GNSS correction services for mass-market applications.

    RODRIGO LEANDRO is the chief technology officer at Sapcorda Services in Scottsdale. He holds a Ph.D. in spatial geodesy from UNB. Dr. Leandro has been active in GNSS R&D for more than 15 years and has served in engineering leadership roles in various companies in the GNSS industry.

    PAOLA GONZALEZ is a product engineer at Sapcorda Services and is based in Hanover. She completed her B.Sc. in geodesy at Zulia University in Maracaibo, Venezuela, and her master’s degree in geomatics at Karlsruhe University of Applied Sciences in Karlsruhe, Germany. In the past few years, she has been working in the GNSS industry, focusing mostly on performance analysis, evaluation and verification of different equipment, software and services.

    FURTHER READING

    • Authors’ Conference Paper
    “Integrity for High Accuracy GNSS Correction Services” by L. Urquhart, R. Leandro and P. Gonzalez in Proceedings of ITM 2019, the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 28–31, 2019, pp. 543–553, https://doi.org/10.33012/2019.16709.

    • GNSS Integrity
    “GNSS Position Integrity in Urban Environments: A Review of Literature” by N. Zhu, J. Marais, D. Betaille and M. Berbineau in IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 9, September 2018, pp. 2762–2778, doi: 10.1109/TITS.2017.2766768.

    “Expert Opinions: Integrity in the Vehicle Environment. Question: Why do we need to take integrity seriously in the vehicle environment?” by C. Rizos, R. Bryant and S. Pullen in GPS World, Vol. 28, No. 1, January 2017, p. 8.

    “Integrity for Non-Aviation Users: Moving Away from Specific Risk” by S. Pullen, T. Walter and P. Enge in GPS World, Vol. 22, No. 7, July 2011, pp. 28–36.

    “Carrier Phase-based Integrity Monitoring for High-accuracy Positioning” by S. Feng, W. Ochieng, T. Moore, C. Hill and C. Hide in GPS Solutions, Vol. 13, No. 1, January 2009, pp. 13–22, doi: 10.1007/s10291-008-0093-0.

    “New Tools for Network RTK Integrity Monitoring” by X. Chen, H. Landau and U. Vollath in Proceedings of ION GPS/GNSS 2003, the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 9–12, 2003, pp. 1355–1360.

    “The Integrity of GPS” by R.B. Langley in GPS World, Vol. 10, No. 3, March 1999, pp. 60–63.

    • Autonomous Vehicles
    “Autonomous Driving Guidance: Multi-band GNSS with Embedded Functional Safety for the Automotive Market” by F. Pisoni, D. di Grazi, G. Avellone, L. Serrano, B. Kruger, L. Norman and N.W. Ken in GPS World, Vol. 30, No. 6, June 2019, pp. 86–92.

    Self-driving Vehicles (SDVs) & Geo-information. A report prepared by Geonovum and Geospatial Media and Communications, May 2017.

    • Satellite-Based Augmentation Systems
    “Satellite Based Augmentation Systems” by T. Walter, Chapter 12 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.

    Minimum Operational Performance Standards for Global Positioning/Satellite-Based Augmentation System Airborne Equipment, RTCA/DO-229E, prepared by SC-159, RTCA Inc., Washington, D.C., Dec. 15, 2016.

    “The Stanford – ESA Integrity Diagram: A New Tool for The User Domain SBAS Integrity Assessment” by M. Tossaint, J. Samson, F. Toran, J. Ventura-Traveset, M. Hernandez-Pajares, J.M. Juan, J. Sanz and P. Ramos-Bosch in Navigation, Journal of The Institute of Navigation, Vol. 54, No. 2, Summer 2007, pp. 153–162.

    “Validation of the WAAS MOPS Integrity Equation” by T. Walter, A. Hansen and P. Enge in Proceedings of the 55th Annual Meeting, The Institute of Navigation, Cambridge, Massachusetts, June 28–30, 1999, pp. 217–226.

    “WAAS MOPS: Practical Examples” by T. Walter in Proceedings of NTM 1999, the 1999 National Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 25–27, 1999, pp. 283–293.

    • Jamming and Spoofing
    “Interference” by T. Humphreys, Chapter 16 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.

    “Jamming and Spoofing of GNSS Signals – An Underestimated Risk?!” by A. Ruegamer and D. Kowalewski in Proceedings of FIG Working Week 2015, Sofia, Bulgaria, May 17–21, 2015.

    • Ionospheric Threats
    “Ionospheric Impact on GNSS Signals” by N. Jakowski, C. Mayer, V. Wilken and M.M. Hoque in Física de la Tierra, Vol. 20, 2008, pp. 11–25.

    “Ionospheric Disturbance Indices for RTK and Network RTK Positioning” by L. Wanniger in Proceedings of ION GNSS 2004, the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation, Long Beach, California, Sept. 21–24, 2004, pp. 2489–2854.

  • Innovation: Monitoring sea level in the Arctic using GNSS

    Innovation: Monitoring sea level in the Arctic using GNSS

    A Tidal Shift

    Traditional tide gauges are in contact with the water surface and as a result are susceptible to measurement error and damage during extreme weather. An alternative approach is the use of GNSS reflectometry. We learn how this innovative use of satellite navigation signals works in this month’s Innovation column.

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    Seawater level is conventionally monitored by tide gauges that measure the vertical distance of the water surface from a point on the ground. As the tide gauges provide seamless and highly accurate measurements, many countries operate a tide-gauge network to monitor sea-level changes and to assess flood risk. For example, the National Oceanic and Atmospheric Administration (NOAA) operates a permanent observing system, the National Water Level Observation Network (NWLON), with more than 400 gauges throughout the United States.

    However, some challenges of tide gauges can be identified. Firstly, tide-gauge measurements require direct contact with the water, which causes limitations in installing and maintaining the equipment. The equipment requiring direct sensing is highly vulnerable to coastal hazards, such as coastal flooding and tsunamis, resulting in potential measurement errors or even equipment destruction during severe natural events.

    Furthermore, tide gauges require maintenance on a regular basis, which is expensive because it requires the use of divers. This greatly limits the operation of tide gauges, especially in extreme environments such as in the Arctic. Alaska, for example, has significant gaps in its available spatially-varying tidal information. However, in the Arctic, it is also very important to constantly and closely monitor the long- and short-term variation of water levels because this area has a significant impact on global climate and ecosystems. Consequently, more support is needed for sea-level monitoring and coastal mapping in this region.

    GNSS can serve as an alternative approach for water-level monitoring. GNSS satellites continuously transmit radio signals and ground-based, space-based, and airborne receivers access the signals regardless of weather conditions. Some of the received signals are reflected from obstacles or surfaces near the antenna, a phenomenon referred to as multipath (see FIGURE 1).

    FIGURE 1. Schematic drawing of the GNSS-based tide gauge. (Image: Authors)
    FIGURE 1. Schematic drawing of the GNSS-based tide gauge. (Image: Authors)

    Multipath tends to be regarded as one of the major error sources for GNSS positioning where it causes unexpected phase delays when compared to the direct signal. Consequently, various procedures have been developed to mitigate the multipath effect. However, the GNSS signals reflected from the Earth’s surface contain information about the geophysical properties of the reflecting surface. The use of these signals is known as GNSS reflectometry (GNSS-R). GNSS-R allows us to monitor the temporal variation of water levels by calculating phase delays of GNSS signals reflected from the water surface. A GNSS-R-based tide gauge does not require direct contact with the water because it measures the water levels based on a remote-sensing technique. Thus, a GNSS-R-based tide gauge can be effectively applied to water-level monitoring.

    However, several challenges exist in processing GNSS signals observed at high latitudes compared to mid-latitudes. Not only do we have to contend with extreme weather conditions and limited infrastructure availability, but also with problematic satellite geometry and ionospheric effects on the GNSS signals. To overcome these limitations in the use of GNSS-R in the Arctic, we introduce enhanced algorithms to improve the temporal and spatial resolutions of GNSS-R sea-level measurements.

    Our approach includes an enhanced spectrum analysis based on multi-frequency signals and statistical reliability verification. Moreover, we include the signals transmitted by the Galileo constellation in addition to GPS to improve the quantity and the quality of GNSS observations in the Arctic. We have tested the proposed method with an experiment in Alaska and validated the results with nearby tide gauges. The experimental results clearly show the feasibility of employing GNSS-R-based tide gauges in the Arctic.

    GNSS-R-BASED WATER-LEVEL MONITORING

    Martin-Neria first introduced a method of monitoring sea level using the GNSS-R technique in 1993. Thereafter, many studies have been conducted to apply GNSS-R to water level estimation. Anderson proposed a method to estimate sea level using the interference pattern caused by the direct and reflected GNSS signals, which relies on the fact that the spacing between peaks in the interference pattern is almost entirely dependent on the height of the antenna above the reflecting surface.

    The phase difference in the GNSS receiver between the direct and the reflected satellite signals varies while the geometry of a GNSS satellite changes (see Figure 1), generating the interference pattern. The interference pattern is particularly noticeable in signal-to-noise ratio (SNR) data. The reflected signals contribute to the SNR data in the form of oscillations, while the smoothly rising overall arc mostly depends on the signal strength and the antenna gain pattern.

    The reflected signals can be isolated from the SNR data by removing the main trend — for example, by polynomial fitting — indicative of the direct signal. The frequency of the remaining dSNR oscillations is constant with respect to the sine of the elevation angle, assuming that the water level does not change during the satellite arc and the reflection surface is horizontal. Consequently, the frequency of the oscillation is linearly proportional to the height of the antenna above the reflecting surface.

    The frequency can be derived from the dSNR data by spectral analysis. Among a number of spectral-analysis methods, the Lomb-Scargle periodogram (LSP) is commonly applied since it allows for processing of unevenly sampled data.

    Determining the frequency of the oscillations. The antenna height above the water surface is directly calculated from the frequency of the oscillations derived from LSP processing. However, it is difficult to determine the dominant frequency because of the roughness of the water surface, especially in extreme environments such as Arctic regions with high currents and strong winds. In addition, the observed SNR data is easily affected by obstacles near the GNSS antenna. Therefore, it is difficult to distinguish the spectral peak of the signal reflected from the water surface from other additional reflected signals, especially when additional and unexpected reflections occur near the sea surface.

    To minimize the erroneous determination of the frequency of the oscillations using dSNR, we can take advantage of the multiple frequencies of modern GNSS signals. In our study, we processed signals from both the GPS and Galileo constellations, with GPS transmitting three carrier signals (L1, L2 and L5) and Galileo transmitting five carrier signals (E1, E5a, E5b, E5ab and E6).

    By comparing the spectrum peaks from the multiple signals on different frequencies, one can analyze the dominant peaks across the different frequencies on the same raypath. This algorithm is based on the fact that the multiple frequency signals should detect consistent sea-level heights because they are transmitted along the same raypath during the same period. One of the biggest advantages of this approach is that no additional data or equipment is required to accurately determine the frequency of oscillations of the GNSS signals reflected from the water surface.

    Statistical Testing of Retrieved Sea Levels. Reflected signals are not necessarily all from the sea surface. To remove erroneous solutions, we conducted a statistical test. Data including measurement errors and/or some noise can be approximated to the model by the least squares method that determines the model parameters by minimizing the sum of squared residuals. However, this method yields an incorrect result when many outliers deviating from the normal distribution are included in the data set.

    This problem can be overcome by applying RANdom SAmple Consensus (RANSAC). RANSAC stochastically estimates the model parameters maximizing consensus, that is, the parameter supported by the largest number of sample data through an iterative process. However, the RANSAC results can act differently each time for the same input data because it is essentially a statistical estimation method using random samples. Therefore, we perform RANSAC with rough constraints primarily to remove outliers significantly out of normal range, then the remaining noise in the data can be excluded by performing secondary fitting using tightly constrained least squares. For the least squares procedure, a series was applied for the fitting model, which represents various motions of the sea surface such as ocean tide loading, as a sum of trigonometric functions.

    SEA-LEVEL MONITORING IN ST. MICHAEL

    The Plate Boundary Observatory (PBO) network operated by UNAVCO (formerly the University NAVSTAR Consortium) is primarily designed to monitor long-term tectonic and volcanic deformation. However, it can also be used for GNSS-R applications. A new PBO station, AT01, was installed in May 26, 2018, in St. Michael, Alaska, which is designed to be suitable as a GNSS-R-based tide gauge with a clear and wide-open view toward the sea covering from 0° to 230° in azimuth (see FIGURE 2). The equipment at this site consists of a Trimble choke-ring geodetic antenna and a Septentrio PolaRx5 receiver that can receive not only GPS signals but also those of Galileo, with data recorded every 15 seconds.

    FIGURE 2. The surrounding area of AT01 in St. Michael, Alaska: south view. (Photo: Authors)
    FIGURE 2. The surrounding area of AT01 in St. Michael, Alaska: south view. (Photo: Authors)

    We have used this station to assess our technique using one month of SNR data from June 2018. It should be emphasized that not only GPS but also Galileo signals were processed, and the Center for Orbit Determination in Europe’s Multi-GNSS Experiment final orbit and satellite clock products were used to minimize the satellite orbit error. Additionally, NOAA tide gauge stations (9468132 and 9468333) were used for comparison and verification of the water levels measured from the GNSS-R-based tide gauge (see FIGURE 3).

    FIGURE 3. Locations of AT01 and two NOAA tide-gauge stations (9468132 in St. Michael and 9468333 in Unalakleet). The red box represents the zoomed area at the bottom right. (Image: Authors)
    FIGURE 3. Locations of AT01 and two NOAA tide-gauge stations (9468132 in St. Michael and 9468333 in Unalakleet). The red box represents the zoomed area at the bottom right. (Image: Authors)

    The 9468132 tide gauge in St. Michael is the nearest tide gauge at approximately 1.5 kilometers from AT01. However, since it is not operational, NOAA only provides water-level predictions (just high and low tides) based on the harmonic constituents, not the actual measurements. On the other hand, the 9468333 tide gauge in Unalakleet is approximately 74 kilometers away from AT01. This makes it difficult to use the tide gauge as ground truth, but it does provide the actual sea-level measurements including any abnormal daily variations during the observation period. Therefore, we used the water-level predictions and measurements from both stations to validate the GNSS-R-based water-level measurements at AT01.

    Determination of Water Level. The GPS and Galileo SNR data were independently analyzed using our in-house software package (written in MATLAB) using the following procedures.

    As a preprocessing step, each SNR data series was examined to filter out the signals reflected from other surfaces surrounding the antenna and to isolate the signals that were reflected by the sea surface. Since AT01 PBO station was installed to investigate the feasibility of its use as a GNSS-R-based tide gauge, the most effective azimuth and elevation ranges were given, which are 0° to 230° and 10° to 25°, respectively.

    The azimuth and elevation angle ranges were applied, which effectively removed reflected signals from surfaces other than the sea surface. After identifying the SNR data affected by the reflection from the sea surface, the processing windows were dynamically determined by the continuous path and direction (ascending and descending) of the satellites, and the height of the sea surface was estimated using only a portion of the satellite arc contained within each processing window.

    For example, FIGURE 4 shows the processing windows determined for the GPS satellite PRN 1 on June 1, 2018. The red dots in the figure show the parts of the satellite arcs affected by multipath from the sea surface. The data was divided into three processing windows due to the arc discontinuities and satellite path directions. It should be noted that only the processing windows with a data span of 30 minutes or longer were used for water level estimation. This minimum data span duration of 30 minutes was empirically determined by observing the probability of failure of the water -level calculation for shorter spans.

    FIGURE 4. An example of the processing window determination for GPS satellite PRN 1 on June 1, 2018. (Image: Authors)
    FIGURE 4. An example of the processing window determination for GPS satellite PRN 1 on June 1, 2018. (Image: Authors)

    To isolate multipath effects from the SNR observation, we removed the trend in the SNR by a second-order polynomial fitting using only the portion of a satellite arc contained within each window. FIGURE 5 (b) shows the detrended SNR (dSNR) from FIGURE 5 (a), and the impact of the multipath is clearly identified in the form of the oscillation. As discussed earlier, the oscillation frequency is related to the antenna height above the sea surface. Accordingly, the dSNR data was analyzed through an LSP. As shown in FIGURE 5 (c), multiple peaks are founded from the LSP results of each dSNR series, and it is not easy to distinguish the frequency of the reflected signal from the sea level among these peaks.

    Since multiple frequency signals from the same satellite must detect the same sea-level height, the final dominant peak was determined by checking the consistency of the resulting heights from each dominant peak among the multi-frequency signals. After that, the dominant frequency was converted to the antenna height above the reflection surface, which was then subtracted from the orthometric height of the antenna (the height above the geoid or, approximately, the height above mean sea level [MSL]) to refer the height of the instantaneous sea surface to MSL.

    FIGURE 5. SNR data-analysis procedures with PRN 1 GPS on June 1, 2018: (a) The SNR data affected by the reflection from the sea surface, (b) detrended SNR data through a second-order polynomial, and (c) LSP results and dominant peaks of each frequency. (Image: Authors)
    FIGURE 5. SNR data-analysis procedures with PRN 1 GPS on June 1, 2018: (a) The SNR data affected by the reflection from the sea surface, (b) detrended SNR data through a second-order polynomial, and (c) LSP results and dominant peaks of each frequency. (Image: Authors)

    After analyzing all SNR data observed during one day, we carried out the reliability test of the retrieved sea levels to reject erroneous sea-level solutions.

    RESULTS AND VALIDATION

    The water-level changes from the GNSS-R-based tide gauge at St. Michael were compared to the independently predicted and measured sea levels from the neighboring St. Michael and Unalakleet tide gauges during June 1–30, 2018. Although the tide gauges are considered reliable ground truth, our experimental study must take into account the physical distance between the sites (about 1.5 and 74 kilometers from AT01, respectively) as well as the difference coming from the model versus the actual measurement.

    In addition, a vertical offset between the data time series of the GNSS-R-based tide gauge and the standard tide gauges should be considered due to their different datums. Whereas the GNSS-R-derived sea level refers to a geodetic datum — namely the U.S. National Spatial Reference System (NAVD 88) — a standard tide gauge is highly localized with reference to a tidal datum such as local mean sea level. Generally, the difference between the geodetic and tidal datums is provided by NOAA, which allows us to convert between two vertical datums.

    However, the vertical datum in Alaska has significant gaps in the spatially varying tidal information because of the difficulties of operating tide gauges there so that accurate information for datum conversion cannot be obtained. Therefore, the averages of the vertical differences were calculated (–6.44 centimeters for the St. Michael tide gauge and 9.54 centimeters for the Unalakleet tide gauge), which were then applied to each of the time series to make the comparisons. In fact, such a problem implies another advantage of a GNSS-R-derived tide gauge: it already returns a water-level height based on the terrestrial datum so that the datum of the land and the ocean can be consistently retained.

    FIGURE 6 shows the sea level derived from the GNSS-based tide gauge measurements using GPS (red dots), Galileo (blue dots), the predicted sea level from the St. Michael tide gauge (green dots and lines) and measured sea level from the Unalakleet tide gauge (blue line).

    FIGURE 6. Time series of sea level derived by GNSS-R-based tide gauge (AT01) in St. Michael, Alaska, during a month (red and blue dots for GPS and Galileo satellites, respectively; yellow dashed lines for the smoothed time series from two hours’ moving average filter) together with sea-level measurements from the Unalakleet tide gauge (blue solid line) and sea-level predictions from the St. Michael tide gauge (green dots for high- and low-tide predictions and green dashed line for interpolated predictions). (image: Authors)
    FIGURE 6. Time series of sea level derived by GNSS-R-based tide gauge (AT01) in St. Michael, Alaska, during a month (red and blue dots for GPS and Galileo satellites, respectively; yellow dashed lines for the smoothed time series from two hours’ moving average filter) together with sea-level measurements from the Unalakleet tide gauge (blue solid line) and sea-level predictions from the St. Michael tide gauge (green dots for high- and low-tide predictions and green dashed line for interpolated predictions). (image: Authors)

    The overall results show good agreement with the tide predictions at the nearby St. Michael tide-gauge station. It should be noted that the St. Michael tide gauge only provides high- and low-tide predictions so these were interpolated. However, some tidal characteristics not represented in the published predictions were also confirmed. In particular, as shown in the red-shaded segments of the time series marked (a) and (b) in Figure 6, larger and lower amplitudes than the tide predictions for the St. Michael tide gauge were identified on June 3 and 16, respectively.

    These inconsistencies can be explained by the comparison with actual sea-level measurements at the Unalakleet tide gauge (solid blue line in Figure 6), which show very similar sea-level changes compared to those of the GNSS-R-based tide gauge. In addition, the overall larger amplitudes in the time series from the Unalakleet tide gauge can be explained by considering the fact that the amplitudes of the water levels vary along the coastline in Alaska and the Unalakleet tide gauge is approximately 74 kilometers from AT01.

    To quantitatively investigate the agreement between the GNSS-R-based tide gauge and the standard tide gauges, we computed correlation coefficients. To ensure simultaneous data, the standard tide-gauge measurements and predictions were interpolated to the time tags of the GNSS-R-based time series. The correlation coefficients are 0.87 and 0.81 with the St. Michael and Unalakleet tide gauges, respectively.

    The statistical analysis of the comparison result is summarized in TABLE 1. The mean and maximum values were computed using the absolute sea-level differences. From the results, it could be established that the GNSS-R-derived sea level shows better agreement with actual sea-level measurements at the Unalakleet tide gauge even though it is approximately 74 kilometers away from AT01.

    Table 1 Statistical analysis of the sea-level differences between the GNSS-R-based tide gauge (AT01) and the standard tide gauges (Unalakleet and St. Michael).
    Table 1 Statistical analysis of the sea-level differences between the GNSS-R-based tide gauge (AT01) and the standard tide gauges (Unalakleet and St. Michael).

    Spectral analysis was additionally conducted to validate the sea levels from the GNSS-R-based tide gauge. Because the St. Michael tide gauge does not provide actual measurements (only predictions), only the Unalakleet tide gauge was used in the spectral comparison. A fast Fourier transform (FFT) was applied to convert the time series of the sea levels to the frequency domain.

    The GNSS-R-based tide gauge showed good agreement with the Unalakleet tide gauge overall. In addition, from the corresponding spectral analysis results, we were able to find meaningful harmonic constituents, M2, K1 and O1. The harmonic constituents estimated from the sea-surface measurements of the GNSS-R-based tide gauge have amplitudes most similar to the published harmonic constituents of the nearest St. Michael tide gauge, although the difference in amplitudes of the three harmonic constituents averages 12.3 centimeters.

    In fact, the Unalakleet tide gauge also does not exactly match the amplitude of the estimated harmonic constituents and the published harmonic constituents. But by summarizing the corresponding results, we can conclude that the harmonic constituents estimated from the GNSS-R-based tide gauge are reliable.

    As mentioned earlier, in our study, we estimated the water-level change by using GPS and Galileo satellite signals to overcome the degradation of GNSS performance due to the satellite geometry in the Arctic. The smoothed time series, calculated from a moving-average filter of two-hour intervals, is shown in Figure 6 (yellow dashed lines). The time series of sea level derived by the GNSS-R-based tide gauge during the whole month were used as ground truth for evaluating the accuracy.

    This was done because the Unalakleet tide gauge is approximately 74 kilometers away from AT01 and the St. Michael tide gauge does not provide actual measurements, making it difficult to use as ground truth. As a result, the sea levels determined using the Galileo and GPS signals showed very similar accuracy with an average difference of 0.11 meters. Therefore, even if Galileo is additionally used, the estimated final water levels were at a similar level of accuracy.

    However, the number of water-level observations dramatically increased (approximately doubled) when GPS and Galileo signals were both involved, even though the number of Galileo satellites is fewer than the number of GPS satellites. This is because Galileo transmits on five frequencies while GPS transmits on just three, so we can achieve more robust solutions by including Galileo.

    We investigated how adding Galileo satellites changes the temporal resolution of the final sea-level measurements. At this time, several sea-level measurements pointing to the same epoch (such as sea levels from several frequency observations of the same satellite arc) were considered as one measurement for the time interval computation.

    Overall, sea-level measurements using only Galileo satellites show lower temporal resolution compared to GPS satellites alone, with a mean time interval of 48.97 minutes because Galileo is not fully operational yet and fewer satellites are available. However, combining GPS and Galileo satellites to the sea-level analysis significantly increased the time resolution.

    When only GPS satellites were used, the maximum time interval between two water-level measurements was greater than 3 hours, while the maximum time interval was shortened to about 1.5 hours when Galileo satellites were included in the water-level measurement.

    However, even if both GPS and Galileo satellites were used, the average time interval was still 14.1 minutes, which is considerably longer than the time resolution of the standard tide gauge of 6 minutes. The lower time resolution of a GNSS-R-based tide gauge is explained by the limited ranges (azimuth and elevation angle ranges of 0° to 230° and 10° to 25°, respectively) toward the ocean at station AT01. It means the time resolution can be improved by securing a wider view of the ocean from the GNSS-R-based tide gauge.

    SUMMARY AND CONCLUSION

    The purpose of our study was to evaluate and verify the feasibility of using GNSS-R for sea-level monitoring in the Arctic. We used data from a GNSS station in St. Michael, Alaska, and applied an advanced algorithm that accurately determines sea levels through the comparisons of results from multiple GNSS signals along with an effective filtering procedure. Our results were validated through comparisons with measurements and predictions from nearby standard tide gauges.

    From the corresponding analysis, we could confirm that the GNSS-R technique overcomes the limitations of standard tide gauges in the Arctic and successfully estimated the sea-level change in St. Michael, Alaska. The results from this study show many promising applications for a GNSS-R-based tide gauge in the Arctic, such as tsunami and flood monitoring and tidal datum determination.

    In future studies, additional research should be conducted on how well the GNSS-R-based tide gauge can operate in extreme conditions such as low temperatures, wind gusts, storms, and snow. And, for further improvement of the temporal resolution of the technique, all active GNSS constellations including GPS, GLONASS, Galileo, and BeiDou should be included — that will certainly improve the temporal resolution and also potentially improve the accuracy and reliability. It would be also worth studying the spatial variations of sea-level changes by investigating the specular reflection points of GNSS multipath signals.

    ACKNOWLEDGMENTS

    This article is based on the paper “Monitoring Sea Level Change in the Arctic Using GNSS-Reflectometry” presented at ION ITM 2019, the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 28–31, 2019.

    SU-KYUNG KIM is a graduate research assistant at Oregon State University in Corvallis, Oregon. She received her M.Sc. in geoinformation engineering from Sejong University in Seoul, South Korea, in 2013. Her research interests are focused on sea-level change monitoring and crustal deformation studies using GNSS.

    JIHYE PARK is an assistant professor of geomatics at Oregon State University. She holds a Ph.D. in geodetic science and surveying from The Ohio State University in Columbus, Ohio. Her research interests include GNSS positioning and navigation, GNSS reflectometry, ionospheric and tropospheric monitoring for natural hazards and artificial events, and other geospatial-related topics.

    FURTHER READING

    • Authors’ Conference Paper

    “Monitoring Sea Level Change in the Arctic Using GNSS-Reflectometry” by S.-K. Kim and J. Park in Proceedings of ION ITM 2019, the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 28–31, 2019.

    • Pioneering Work by Manuel Martin-Neira

    “The PARIS Concept: An Experimental Demonstration of Sea Surface Altimetry Using GPS Reflected Signals” by M. Martín-Neira, M. Caparrini, J. Font-Rossello, S. Lannelongue and C.S. Vallmitjana in IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 1, 2001, pp. 142–150, doi: 10.1109/36.898676.

    “A Passive Reflectometry and Interferometry System (PARIS): Application to Ocean Altimetry” by M. Martín-Neira in ESA Journal, Vol. 17, No. 4, 1993, pp. 331–355.

    • Using GNSS Reflectometry to Monitor Water Level

    Local Sea Level Observations Using Reflected GNSS Signals by J.S. Löfgren, Ph.D. dissertation, Chalmers University of Technology, 2014.

    “Coastal Sea Level Measurements Using a Single Geodetic GPS Receiver” by K.M. Larson, J.S. Löfgren and R. Haas in Advances in Space Research, Vol. 51, No. 8, 2013, pp. 1301–1310, doi: 10.1016/j.asr.2012.04.017.

    “Monitoring Coastal Sea Level Using Reflected GNSS Signals” by J.S. Löfgren, R. Haas and J.M. Johansson in Advances in Space Research, Vol. 47, No. 2, 2011, pp. 213–220, doi: 10.1016/j.asr.2010.08.015.

    “Three Months of Local Sea Level Derived from Reflected GNSS Signals” by J.S. Löfgren, R. Haas, H.-G. Scherneck and M.S. Bos in Radio Science, Vol. 46, No. 6, 2011, RS0C05, doi:10.1029/2011RS004693.

    “Determination of Water Level and Tides Using Interferometric Observations of GPS Signals” by K.D. Anderson in Journal of Atmospheric and Oceanic Technology, Vol. 17, No. 8, 2000, pp. 1118-1127, doi: 10.1175/1520-0426(2000)017<1118:DOWLAT>2.0.CO;2.

    • Earlier Innovation Columns Dealing with GNSS Refectometry

    “How Deep Is That White Stuff? Using GPS Multipath for Snow-Depth Estimation” by F.G. Nievinski and K.M. Larson in GPS World, Vol. 25, No. 9, September 2014, pp 38–50.

    “Friendly Reflections: Monitoring Water Level with GNSS” by A. Egido and M. Caparrini in GPS World, Vol. 21, No. 9, September 2010, pp 50–56.

    “It’s Not All Bad: Understanding and Using GNSS Multipath” by A. Bilich and K.M. Larson in GPS World, Vol. 20, No. 10, October 2009, pp. 50–56.

    • Tides and Water Level

    Tides, Surges and Mean Sea-Level by D. Pugh, published originally by J. Wiley & Sons, Chichester, U.K., 1987, reprinted with corrections in 1996 and subsequently issued in e-print form by NERC Open Research Archive.

    • Random Sample Consensus

    “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography” by M.A. Fischler and R.C. Bolles in Communications of the ACM, Vol. 24, No. 6, 1981, pp. 381–395, 10.1145/358669.358692.

    • Vertical Datums

    “Vertical Datum Transformation: Integrating America’s Elevation Data.”

    • NOAA Tides and Currents

    Local water levels, tide and current predictions, and other oceanographic and meteorological conditions are available on this NOAA website.

  • Innovation: Multi-band GNSS with embedded functional safety for the automotive market

    Innovation: Multi-band GNSS with embedded functional safety for the automotive market

    Autonomous Driving Guidance

    GNSS chip manufacturers and positioning systems developers are working on bespoke devices for autonomous driving. This month, we look at a development with embedded functional safety.

    By Fabio Pisoni, Domenico Di Grazia, Giuseppe Avellone, Luis Serrano, Brett Kruger, Laura Norman and Natasha Wong Ken

    INNOVATION INSIGHTS by Richard Langley
    INNOVATION INSIGHTS by Richard Langley

    I DRIVE A 10-YEAR OLD KIA SPORTAGE. It is still quite roadworthy despite having to contend with New Brunswick winters. However, it lacks some of the safety features that are present in newer cars. There is no back-up camera, no forward-collision warning, no automatic emergency braking, and no blind-spot warning, for example. These are just some of the safety systems that come as standard or optional on most new cars these days. Still, the driver is responsible for the safety and operation of the car at all times. True, help might be provided for parallel parking and cruise control, but that’s about it for automated operation with most vehicles.

    But things are changing and changing fast. Real automation is coming to automobiles. Already partial automation is available in some high-end vehicles that can take over steering, braking and acceleration in certain circumstances. The driver is still responsible for other aspects of the vehicle’s operation including paying attention to road conditions. Soon, we will have conditional automation where the car can drive itself but the driver must stay alert and be prepared to take over immediately at any time. Next will come high automation where a computer fully drives the car at certain times on certain routes such as a highway. Multiple systems, including back-up systems, will maintain a required safety level and the car will determine if it is safe to operate autonomously. If not, it could pull over to the side of the road and shut down. And finally, we may have full automation of cars. They will be able to drive on any road under virtually any conditions and won’t need any controls such as steering wheels or accelerator or brake pedals.

    Augmented GNSS guidance will play a major role in the automation of vehicles. As with any navigation or guidance system, there are four important requirements: accuracy, availability, continuity and integrity. Perhaps the most obvious requirement, accuracy describes how well a measured value agrees with a reference value, typically the true value. How well a system accounts for various errors or biases determines the accuracy of corrected measurements and, ultimately, the accuracy of a derived position. A navigation system’s availability refers to its ability to provide the required function and performance within the specified coverage area at the start of an intended operation. In many cases, system availability implies signal availability. Environmental factors such as signal attenuation or blockage or the presence of interfering signals might affect availability. Ideally, any navigation system should be continuously available to users. But, because of scheduled maintenance or unpredictable outages, a particular system may be unavailable at a certain time. Continuity, accordingly, is the ability of a navigation system to function without interruption during an intended period of operation.

    While accuracy, availability and continuity of a guidance system are all important, it is the integrity or trustworthiness of the system that is paramount. It is why the automotive industry has already developed integrity standards for the automation of vehicles. And it is why GNSS chip manufacturers and positioning systems developers are working on bespoke devices for autonomous driving, whatever the level of automation. In the Innovation column this time around, we’ll learn about one such development — one with embedded functional safety.

    Autonomous driving applications are raising the requirements for onboard GNSS receivers to new highs. Position accuracy, protection levels, high availability, robustness of operation and integrity are the priorities shaping a new class of automotive components and architectures. Autonomous driving deals with life-critical issues: the expectation of reliability and safety for this new generation of receivers, as well as for other sensors and systems, is very high.

    The International Organization for Standardization (known by the language-independent short form ISO) has issued documents codifying functional safety (FuSa) for automotive applications: ISO 26262: part 1 to part 11. ISO 26262 complements the well-known automotive reliability standard published by the Automotive Electronics Council, AEC-Q100. With respect to FuSa, a system can be defined as functionally safe if it always operates correctly and predictably. More importantly, in the event of failures, the system must remain safe for people. Lastly, as security is becoming paramount, a new standard for cybersecurity in automotive applications — ISO/SAE 21434 — is in development by ISO and SAE International (initially called the Society of Automotive Engineers) that will require a GNSS receiver to be robust against jamming, spoofing and meaconing attacks.

    The Automotive Safety Integrity Level (ASIL) is a key part of ISO 26262 compliance, and the standard specifically identifies the minimum testing requirements depending on the ASIL of the component. The ASIL of a component or system depends on the ASIL of the target application. The ASIL is determined at the beginning of a development process. It varies from ASIL-A to ASIL-D, where A is for less critical applications and D for the most critical ones such as steering and breaking systems. ASIL-rated lane-level positioning performance can be demonstrated today by combining an ASIL-B software positioning engine and TerraStar-X correction technology from Hexagon Positioning Intelligence with GNSS measurements from an ASIL-B-rated GNSS chipset.

    To conjugate performance requirements with the demand of embedded functional safety, STMicroelectronics has developed TeseoAPP (STA9100), a next-generation GNSS component, designed to meet an ASIL-B level of safety. TeseoAPP is a multi-band GNSS measurement engine. It outputs all the observables, navigation and integrity data required by a safety-critical precise positioning algorithm, located on a host processor. TeseoAPP also computes a local L1 code-based standard position, velocity and time (PVT) solution (SPS) for monitoring and integrity purposes. Also part of the baseline features are autonomous satellite acquisition (cold start condition), real-time assistance, data decoding and storage on external non-volatile memory (NVM), accurate timing and pulse-per-second generation under vehicle dynamics.

    RECEIVER ARCHITECTURE

    The target architecture for a safety-critical platform is sketched in FIGURE 1, where a host microprocessor is in charge of collecting GNSS observables and sensor data from the TeseoAPP. The latter includes on the same chip die a first configurable RF chain for the L1 signal ensemble and the baseband part for processing all the signals in the served bands, while the second chip is an RF front end (code-named STA5635), configurable for receiving the other served bands (such as GPS L2 or L5, Galileo E5a or E5b or E6, and so forth). The two chips are clearly visible in the photograph of a TeseoAPP evaluation module of FIGURE 2.

    FIGURE 1. Block diagram of the TeseoAPP platform for safety-critical applications, featuring surface-acoustic-wave (SAW) filters, a temperature-compensated crystal oscillator (TXCO), non-volatile memory (NVM) and both internal and external STA5635 tuners. (See text for other initialisms used.) Diagram: Authors)
    FIGURE 1. Block diagram of the TeseoAPP platform for safety-critical applications, featuring surface-acoustic-wave (SAW) filters, a temperature-compensated crystal oscillator (TXCO), non-volatile memory (NVM) and both internal and external STA5635 tuners. (See text for other initialisms used.) Diagram: Authors)
    FIGURE 2 The TeseoAPP Evaluation Module, including the STA9100 (TeseoAPP) and STA5635 (external tuner). Photo: Authors
    FIGURE 2 The TeseoAPP Evaluation Module, including the STA9100 (TeseoAPP) and STA5635 (external tuner). Photo: Authors

    The selected frequency plan and constellation configuration depend on the specific autonomous driving scenario and the target geographic area. The TeseoAPP supports a mix of frequencies and signals as shown in TABLE 1. The chipset baseband unit can track up to 80 channels. A tracking snapshot from a rooftop antenna (located at the ST office in Naples, Italy) is illustrated in FIGURE 3.

    Both the TeseoAPP and the STA5635 have been designed for ASIL-B following the concept of “safety element out of context” (SEooC) described in ISO standard ISO 26262:2012. In this context, assumptions have been made for the application (such as on the mission profile), identifying the related safety goals from which functional and technical safety requirements have been derived.

    TABLE 1. The TeseoAPP (STA5635) supported frequency plans and scenarios.
    TABLE 1. The TeseoAPP (STA5635) supported frequency plans and scenarios.
    FIGURE 3 Screenshot of the L1-L5 TeseoAPP configuration, from the ST Teseo-Suite tool (using the Naples rooftop antenna). Image: Authors
    FIGURE 3. Screenshot of the L1-L5 TeseoAPP configuration, from the ST Teseo-Suite tool (using the Naples rooftop antenna). Image: Authors

    Following the guidelines identified in the ISO 26262 flow for safety-relevant product development, several safety mechanisms have been identified at the hardware, firmware and system/boot level. The microcontroller unit (MCU) supports dual-core operation in a lock-step configuration to verify processor output errors together with a memory built-in self-test (executed at startup) and error correction code on a safety-related embedded random access memory. Other hardware redundancies have been introduced in safety relevant parts such as triple-voted registers for critical configuration parameters. For the real-time operating system (RTOS), an ASIL-D-level product — the highest level — was selected.  Functional safety analysis of the GNSS sub-system has produced a dedicated technical safety concept, including aspects such as tuner operation, interference and jamming mitigation, signals and observables quality management (QM), reliable host communication (using generic end-to-end or E2E protocols for data integrity and resilient flow control), and reliable system software. A simplified overview of all these safety mechanisms is outlined in FIGURE 4, where the orange-colored blocks are specific for the GNSS sub-system.

    FIGURE 4. Overview of the TeseoAPP safety mechanisms. (See text for acronyms and initialisms used.) Diagram: Authors
    FIGURE 4. Overview of the TeseoAPP safety mechanisms. (See text for acronyms and initialisms used.) Diagram: Authors

    Safety Mechanisms. The technical safety concept of the GNSS sub-system is implemented by a security, integrity and safety (SIS) monitoring layer (see FIGURE 5). The SIS collects information and metrics from other receiver blocks embedded in the RF/baseband hardware and from different components of the GNSS firmware stack. The SIS internally computes integrity risk estimates, which are delivered to a central intelligence monitor (CIM) capable of switching the receiver into a safe state, within a fault-tolerant time interval, when the overall receiver integrity appears compromised. In its simplest form, the CIM can be represented by a weighted sum of integrity risk inputs, followed by some activation function. During this process, a first layer of logic (CIM-L1) combines a subset of signal quality metrics to decide a priori which observables shall be passed to the host or discarded (not delivered).

    FIGURE 5 Safety information flow through the TeseoAPP security, integrity and safety layer. (IP = intellectual property; other short forms in text.) Diagram: Authors
    FIGURE 5 Safety information flow through the TeseoAPP security, integrity and safety layer. (IP = intellectual property; other short forms in text.) Diagram: Authors

    The collected signal metrics include quality estimators (based on multi-correlation techniques for example) or classic linear combinations of observables (such as dual-frequency carrier-phase differences or code-minus-carrier). Receiver metrics, on the other hand, have a more global scope and include estimators for inter-frequency biases, system-time cross-checks among constellations, and so on. The fault collection and control unit (FCCU) conveys hardware status flags to the SIS. Typically, an FCCU exception indicates some critical hardware failure and takes a priority path when switching the safe state. For example, a fault in the MCU lock-step monitor will trigger an immediate firmware action, mediated by the FCCU.

    POSITIONING PERFORMANCE

    To demonstrate the performance that can be achieved using the ST TeseoAPP chipset, Hexagon Positioning Intelligence (PI) has combined measurements from the TeseoAPP with an automotive-grade antenna and Terrastar-X correction technology, and processed the data using Hexagon PI’s software positioning engine. Even with a modern receiver supporting dual-frequency, multi-constellation measurements, such as the TeseoAPP, corrections are necessary to deliver decimeter-level performance and safety information required by an autonomous vehicle.

    In clear-sky environments, lane-level positioning accuracy is achieved, enabling GNSS as a key input to autonomous systems. FIGURE 6 shows the horizontal error performance of the combined ST+PI solution in the form of an error time series and an error cumulative distribution function (CDF). The error performance expected from today’s single frequency automotive-grade GNSS without corrections and processing is also shown for comparison.

    FIGURE 6. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone and of the TeseoAPP with PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)
    FIGURE 6. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone and of the TeseoAPP with Hexagon PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)

    For guidance systems in autonomous applications, the GNSS position must be accompanied by safety information and integrity guarantees. The concept of protection levels (PLs) has been introduced to provide this. A horizontal protection level defines a circle or ellipse around the reported GNSS position, which will have some error, within which the actual position is guaranteed to fall. The Hexagon PI software positioning engine is ASIL-B rated, so its position and PL outputs are available for use in safety-related autonomous applications. The autonomous system using the GNSS position is assured that its actual position is within the protection level ellipse. To output ASIL-B-rated positions accompanied by PLs, ASIL-rated GNSS measurement inputs are required.

    Using the inputs and techniques described above, the Hexagon PI software positioning engine calculates PLs for every GNSS position output. The Hexagon PI data from Figure 6 is shown again in FIGURE 7 with accompanying PL information. In this case, a PL with integrity risk of 10-7 is shown, meaning that the actual position error is expected to exceed the reported PL at a rate less than 10-7 per hour.

    FIGURE 7 Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)
    FIGURE 7. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the Hexagon PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)

    The PLs shown in Figure 7 are typically much greater than the position error. This is because the protection level calculation must account for a large number of potential faults that are not generally present. For instance, undetectable GNSS satellite faults can occur at rates greater than 10-7 per hour, and so must be accounted for in the PL.

    In non-clear-sky environments, the GNSS position calculation is complicated by frequent loss of “sight” of the GNSS satellites. This is mitigated by having additional constellations and frequencies. However, for added availability of a precise position in challenging environments, it is necessary to incorporate sensor fusion into the position calculation, typically by using a six degree-of-freedom inertial measurement unit (IMU) as input, which includes three accelerometers and three gyroscopes to measure 3D translational and rotational motion. The IMU can maintain position accuracy for short periods when GNSS is unavailable, such as when driving under an overpass on a highway. The IMU provides a relative positioning output, so the absolute error growth is unconstrained in the absence of GNSS inputs. Therefore, it is important to have the GNSS receiver as the primary sensor in the positioning solution to constrain IMU drift and to reacquire GNSS signals rapidly after emerging from a GNSS outage.

    Position error results for a typical highway environment are shown in FIGURE 8 after adding input from an automotive-quality IMU to the Hexagon PI software positioning engine. Small spikes in position error are due to short GNSS outages along the test route. However, the error growth due to loss of GNSS is minimal due to the coupling of the IMU data with the GNSS measurements.

    FIGURE 8 Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone, and of the TeseoAPP with PI software positioning engine (SWPE) in a highway environment. (Image: Authors)
    FIGURE 8. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone, and of the TeseoAPP with Hexagon PI software positioning engine (SWPE) in a highway environment. (Image: Authors)

    FIGURE 9 shows the Hexagon PI highway data with accompanying PLs. Though the errors are well-constrained through GNSS outages, the PLs typically increase significantly. This is due to the higher noise of low-cost IMUs, and the uncertainty associated with reacquiring GNSS signals. PLs must account for worst-case IMU performance, which can have errors orders of magnitude greater than the nominal performance. During GNSS signal reacquisition, minimizing receiver noise is critical for fast position reconvergence, reinforcing the need for high-quality GNSS in autonomous applications.

    FIGURE 9. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the PI software positioning engine (SWPE) in a highway environment. (Image: Authors)
    FIGURE 9. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the Hexagon PI software positioning engine (SWPE) in a highway environment. (Image: Authors)

    CONCLUSION

    The TeseoAPP is the first generation of multi-band GNSS chipsets designed by STMicroelectronics to meet the two main requirements of autonomous driving: accuracy and safety-critical operation. The execution of the ISO 26262 standard for TeseoAPP is still a work in progress and encompasses two main aspects: 1) a safety plan implementation, code quality metrics and processes management and 2) the technical safety concept. Both of these aspects presented specific challenges, mainly due to the inherent complexity of the product and the large amount of firmware involved.

    To exploit the maximum benefit of the TeseoAPP in safety-critical automotive applications, a high-accuracy ASIL-B-rated position engine is required. Hexagon PI’s software positioning engine is designed to use measurements from an ASIL-rated GNSS receiver, along with GNSS corrections and IMU data, to generate ASIL-rated position outputs, with accompanying integrity guarantees. The Hexagon PI software positioning engine computes protection levels. The calculation and determination of PLs is required to meet the safety and integrity guarantees necessary in autonomous driving for functionally safe operation.  The software positioning engine also outputs ASIL-rated velocity, attitude and absolute time data, although we have not discussed these in this article.

    The required high performance and safety expectations suggested, since the early stages of the project, a system composition in which the TeseoAPP was configured as an ASIL-B measurement-engine whereas the ASIL-B software positioning engine algorithms (by Hexagon PI) run on a separate ASIL host processor. We believe this synergy of competencies will represent the key for a successful solution to enable safe and reliable positioning in autonomous driving applications.

    ACKNOWLEDGMENTS

    The TeseoAPP chipset has been developed with the support and in the framework of the European Safety Critical Applications Positioning Engine project, which is funded by the European GNSS Agency under the European Union’s Fundamental Elements research and development program.

    FABIO PISONI leads the GNSS System Architecture and Software Team (Automotive and Discrete Group) at STMicroelectonics Italy in Milan, where he has worked since 2009. He has a degree in electronics from Politecnico di Milano and has previous experience as a GNSS and digital signal processing (DSP) engineer.

    DOMENICO DI GRAZIA is a GNSS signal senior staff engineer at STMicroelectronics Italy in Naples, where he has worked since 2003. He has a degree in telecommunication engineering from the University of Naples Federico II, holds patents in the GNSS area, and has previous experience in digital communications.

    GIUSEPPE AVELLONE is in the GNSS System Architecture and Software Team (Automotive and Discrete Group) at STMicroelectonics Italy in Catania, where he has worked since 1998. He has a degree in electronics from Università di Palermo and previous experience as a GNSS and DSP engineer.

    LUIS SERRANO is a GNSS technical marketing manager with STMicroelectronics based in Munich. He holds a Ph.D. in GNSS from the Department of Geodesy and Geomatics Engineering, University of New Brunswick, Canada. He has been active in the GNSS precise positioning field since 2007, and holds a patent on GNSS.

    BRETT KRUGER is a software engineer specializing in GNSS/INS integration in the Safety Critical Systems Group at the Hexagon Positioning Intelligence (PI) NovAtel brand  in Calgary, Canada. He holds an M.A.Sc. in electrical engineering from the University of Toronto, Canada.

    LAURA NORMAN is a geomatics engineer specializing in GNSS integrity and protection levels in Hexagon PI’s Safety Critical Systems Group. She obtained her M.Sc. from the Department of Geomatics Engineering at the University of Calgary, Canada.

    NATASHA WONG KEN is the Safety Critical Systems product manager at Hexagon PI. She has worked at Hexagon PI since 2012 after obtaining a B.Sc. in geomatics engineering from the University of Calgary.

    FURTHER READING

    • Standards for Vehicle Safety

    “Keeping Safe on the Roads: Series of Standards for Vehicle Electronics Functional Safety Just Updated” by C. Naden, ISO, 19 Dec. 2018.

    Road vehicles – Functional safety, ISO 26262:2018 (parts 1 to 12), International Organization of Standardization, Geneva, Switzerland, December 2018.

    Failure Mechanism Based Stress Test Qualification for Integrated Circuits, AEC – Q100 – Rev-H, Automotive Electronics Council, 11 Sept. 2014.

    • STMicroelectronics TeseoAPP (STA9100)

    STA9100MGA, Automotive TeseoAPP (ASIL Precise Positioning) Family Multi Band GNSS Precise Measurement Engine Receiver, DB3546, Data Brief, STMicroelectronics, Geneva, Switzerland, 26 Feb. 2018.

    • Future GNSS Automotive Positioning

    “NovAtel Pioneers Autonomous Solutions with Positioning Engine, Corrections Services, Integrity Research” by T. Cozzens in GPS World, Vol. 29, No. 5, May 2018, pp. 33–34.

    “Lane-level Positioning with Low-cost Map-aided GNSS/MEMS IMU Integration” by M. M. Atia and A. Hilal in GPS World, Vol. 29, No. 5, May 2018, pp. 18–32.

    “Quo Vademus: Future Automotive GNSS Positioning in Urban Scenarios” by M. Escher, M. Stanisak and U. Bestmann in GPS World, Vol. 27, No. 5, May 2016, pp. 46–52.

    • Precise Point Positioning

    “Two Are Better Than One: Multi-frequency Precise Point Positioning Using GPS and Galileo” by F. Basile, T. Moore, C. Hill, G. McGraw and A. Johnson in GPS World, Vol. 29, No. 10, October 2018, pp. 27–37.

    “More Is Better: Instantaneous Centimeter-level Multi-frequency Precise Point Positioning” by D. Laurichesse and S. Banville in GPS World, Vol. 29, No. 7, July 2018, pp. 42–47.

    “Where Are We Now, and Where Are We Going? Examining Precise Point Positioning Now and in the Future” by S. Bisnath, J. Aggrey, G. Seepersad and M. Gill in GPS World, Vol. 29, No. 3, March 2018, pp. 41–48.

    • Integrity of Automobile Positioning

    “Expert Opinions: Integrity in the Vehicle Environment. Question: Why do we need to take integrity seriously in the vehicle environment?” by C. Rizos, R. Bryant and S. Pullen in GPS World, Vol. 28, No. 1, January 2017, p. 8.

    “Integrity for Non-Aviation Users: Moving Away from Specific Risk” by S. Pullen, T. Walter and P. Enge in GPS World, Vol. 22, No. 7, July 2011, pp. 28–36.

    “The Integrity of GPS” by R.B. Langley in GPS World, Vol. 10, No. 3, March 1999, pp. 60–63.

  • Innovation: Better jamming mitigation

    Innovation: Better jamming mitigation

    Using Wavelets for a Robust Vector-Tracking-Based GPS Software Receiver

    Innovation Insights with Richard Langley
    Innovation Insights with Richard Langley

    ALFRÉD HAAR. Who is he, you might ask? Alfréd Haar was a Hungarian mathematician who introduced the concept of wavelets during his Ph.D. work on orthogonal functional systems under David Hilbert of Hilbert transform fame. And what is a wavelet? Generally speaking, a wavelet, as its name suggests, is a brief oscillation in time with an amplitude that begins at zero, goes through one or more variations, and returns to zero. It’s a bit like the cardiac cycle of each heartbeat shown on an electrocardiogram. But wavelets, unlike heartbeats, are mathematical functions with well-defined properties.

    Although Haar initiated the use of wavelets back in 1909, it was not until the 1970s and 1980s that the study of the use of wavelets — wavelet analysis — was undertaken to help solve a variety of problems in science and engineering with new application areas springing up all the time. We’ll get to one of these new areas — GNSS jamming mitigation — in just a bit, but let’s discuss a more mundane application first.

    Let’s say we have a digitized audio recording of Maynard Ferguson’s rendition of “MacArthur Park” in our computer. We could do a Fourier transform (related to the Hilbert transform mentioned earlier) of the entire recording, which would show us all of the specific audio frequencies making up the song. But what if we wanted to determine where in the song Ferguson played a particular high note, such as double high C (not his highest)? We could create a wavelet with that frequency and a short duration such as that of a 32nd note and use the mathematical operation of convolution (involving shifting, multiplication and integration) to find one or more spots in the recording with a similar frequency. We could extend the procedure and use a set or bank of wavelets to fully study the song in both frequency and time.

    Wavelet analysis will work on many kinds of data, not just audio signals. With an appropriate set of wavelets, we could decompose the data without gaps or overlap, store the resulting product for further analyses and, if necessary, reconstitute the original data with minimal distortion. The U.S. Federal Bureau of Investigation uses wavelet analysis to store compressed digital versions of fingerprint images. A heavily damaged recording of Brahms playing one of his own compositions on an Edison wax cylinder was partially restored using wavelet analysis despite the music being immersed in noise. And the small effect of El Niños on the Earth’s rotation has been studied using wavelet analysis.

    And, yes, wavelet analysis is helping to improve the use of GNSS. The tasks being undertaken include de-noising of pseudorange measurements, cycle-slip detection and elimination in carrier-phase measurements, and separating biases such as multipath from high-frequency receiver noise. In this month’s column (which, by the way, now appears four times per year), we learn about another GNSS application of wavelet analysis — specifically the use of the wavelet packet transform — to efficiently identify and separate a jamming signal from the combined signal in a GPS receiver. In a narrowband jamming test using a hardware simulator system, no positioning was possible with conventional receiver operation. But with the proposed approach, the jamming signal was readily suppressed, allowing the satellite signals to be acquired and a positioning solution to be computed. Thank you, Alfréd Haar.

    GPS technology has been integrated into many aspects of our daily lives. Hence, there is a growing demand for a robust GPS receiver that can operate efficiently without external aiding to provide continuous, reliable and accurate positioning, navigation and timing (PNT) solutions. However, this is not always possible due to frequent loss or attenuation of signals, multipath or interference. In such challenging conditions, a system malfunction can cause safety problems, especially in health-critical applications.

    Receiver architecture plays a major role in defining a receiver’s robustness against the challenges just mentioned. Scalar-tracking-based GPS receivers can achieve high navigation accuracy under line-of-sight (LOS) conditions. However, they always fail to provide adequate accuracy in signal-degraded environments such as urban, suburban and dense foliage environments. On the contrary, vector-tracking-based GPS receivers provide better performance in such challenging environments. In vector-tracking-based receivers, both the tracking loops and the navigation processor are combined to solve a single estimation problem. Hence, there are many advantages of this architecture over that of scalar-tracking-based receivers. First, information from strong signals from healthy satellites is used to track weak signals, when signals are highly attenuated or even totally blocked. Thus, vector-tracking-based receivers have better immunity to jamming and interference. Second, they can rapidly reacquire signals after a satellite outage. Third, they have an improved navigation solution accuracy compared to that of scalar-tracking-based receivers, even under normal LOS conditions. All of these advantages make vector-tracking-based receivers the best platform for our research on receiver robustness. However, vector-tracking-based receivers still suffer from degraded performance in the presence of strong jamming signals. Therefore, we are proposing a new anti-jamming technique to be employed for interference mitigation in vector-tracking-based GPS receivers.

    The spread-spectrum nature of GPS signals provides resistance to narrowband interference due to the spreading and despreading processes that take place at the transmitter and receiver respectively. However, a GPS signal reaches the receiver with very low power on the order of –158 dBW, which makes it vulnerable to jamming. A jammer with enough power and suitable time and frequency properties can degrade the positioning solution accuracy and may cause a total blockage of the GPS signals. Besides, the presence of a jamming signal increases the challenge of acquisition of the desired GPS signal.

    Therefore, many anti-jamming techniques have been employed for interference mitigation in GPS receivers. There are various anti-jamming methods for GPS receiving systems, which are mainly classified into four groups:

    1. Antenna-level techniques, which are based on the use of antenna arrays to generate a radiation (reception) pattern that attenuates the interference signal based on the direction of arrival.
    2. Automatic gain control (AGC), where interference can be detected by the saturation of the AGC.
    3. Post-correlation techniques, which process the signals after passing through the correlators.
    4. Pre-correlation techniques, which are based on processing the signals after passing through the analog-to-digital converter but before they get to the correlators.

    This article introduces a novel interference mitigation technique based on the wavelet packet transform (WPT), which belongs to the pre-correlation techniques category. The WPT enables the received interfered combined GPS signal to be represented in a transformed domain in which an interference signal can be better identified and separated, without significant degradation of the useful GPS signal. The WPT has been extensively discussed in the literature in the framework of GPS and other GNSS. For example, wavelet multi-resolution analysis has been used in one study to remove the multipath error and leave the useful signal untouched. In another study, multi-resolution analysis using wavelets was applied to pseudorange and carrier-phase GPS double differences to reduce multipath effects. And in another, researchers developed a technique to detect and remove cycle slips based on wavelet multiresolution analysis.

    The WPT has been widely used in the context of jamming to mitigate pulsed and narrowband interference. Although the WPT showed outstanding performance in jamming mitigation, the main drawback of this technique is the computational complexity. In this article, we introduce a novel wavelet packet-based technique for narrowband jamming mitigation with significantly reduced computational complexity.

    Signal and Interference Models

    The GPS signal employs a direct sequence spread spectrum communication technique, in which the signal is multiplied by a spreading or pseudorandom noise (PRN) code. As mentioned earlier, this spreading technique gives GPS some immunity to narrowband jamming. The received digitized spread spectrum signal at the output of the receiver’s analog to digital converter (ADC) can be represented by:

    Photo:

    (1)

    where, for signal s, ym(nTs) is the useful GPS signal received from mth LOS satellite, j(nTs) is the jamming signal, w(nTs) is additive white Gaussian noise (AWGN), M is the number of visible satellites, n is the sample number and Ts is the sampling rate.

    The useful received GPS signal can be described as follows:

    Photo:(2)
    where P is the signal power, d(nTs) is the navigation data, c(nTs) is the spreading pseudorandom noise code, fIF is the intermediate frequency, no is the code delay, fd is the Doppler shift, and θo is the carrier phase.

    Interference signals are classified based on their spectrum characteristics: narrowband or wideband depending on the signal’s bandwidth relative to the bandwidth of the desired GPS signal.

    Our focus in this article is on the mitigation of narrowband interference, specifically a linear chirp signal. A chirp signal can be expressed as:

    Photo:(3)

    where a is the chirp signal amplitude, fo is the starting frequency, k is the sweeping frequency, and Tsw is the sweeping frequency period. The chirp is continuously repeated.

    Wavelet Packet Transform

    The wavelet packet transform or WPT is a class of transformed domain techniques that has been widely used in the context of jamming mitigation in GPS signal reception. It allows for the study of a signal in both time and frequency domains simultaneously. In the WPT, the signal is decomposed into approximations (the low-pass component) and details (the high-pass component) with respect to a group of local basis functions. These functions can be obtained through dyadic scaling and shifting of the so-called mother wavelet. The discrete wavelet basis functions are given by:

    Photo:(4)

    where j and k are integers, so is the dilation step, and τo is the scaling coefficient. The decomposition of the signal with respect to a scaling function acts as low-pass filtering of the signal, while the decomposition with respect to a wavelet function acts as high-pass filtering of the signal. The signal is then down-sampled, and this procedure is further iterated on all the sub-bands using scaled and dilated versions of the wavelet and scaling functions. This filtering process allows the decomposition of the GPS signal with respect to a local basis function, in which each of these sub-bands identifies a limited frequency band of the received signal, and the frequency resolution is dependent on the level of decomposition. The wavelet packet decomposition can be realized as a filter-bank as depicted in FIGURE 1.

    FIGURE 1. Wavelet packet filter banks. (Image: Authors)
    FIGURE 1. Wavelet packet filter banks. (Image: Authors)

    Jamming Mitigation Algorithm

    As mentioned earlier, the main drawback of WPT is the time complexity. Due to the decomposition of both approximation and detail components, if the signal is decomposed into L levels, the resultant number of coefficients is 2L. For instance, if we used 10 decomposition levels, the resultant number of wavelet coefficients is 210 (1,024). However, as each wavelet coefficient component represents a limited portion of the frequency of the received signal, the jamming signal will only affect a few coefficients. Thus, the main idea of the proposed algorithm is to identify those coefficients that are affected by the jamming signal and reconstruct the jamming signal after denoising them. Then, the estimated jamming signal is subtracted from the received signal to obtain the jamming-free useful GPS signal.

    Identifying the wavelet coefficients affected by interference is achieved by computing the median absolute deviation (MAD). As those coefficients that are affected by interference have a higher MAD value than those that are not affected, the decision of whether the wavelet coefficients are affected by interference is based on comparing their MAD values with a certain predefined threshold. This threshold is determined based on the desired detection and false alarm probabilities according to the distribution of the received signal samples in an interference-free environment. Only the sub-bands whose MAD values exceed the threshold are considered to be affected by interference and are further decomposed.

    Therefore, only the sub-bands affected by interference are isolated and iterated. This technique allows for a considerable reduction in complexity, as both detection and mitigation can be applied in a limited number of sub-bands. FIGURES 2 and 3 show the tree decomposition of the received signal of two jamming scenarios based on the proposed algorithm. The frequency offset of the jamming signal from the GPS signal is 200 kHz in the first scenario and 600 kHz in the second one. The figures clearly illustrate the huge reduction in computational complexity as for 10 levels of decomposition; we ended up having only eight wavelet coefficients instead of 1,024.

    FIGURE 2. Tree decomposition for scenario I. (Image: Authors)
    FIGURE 2. Tree decomposition for scenario I. (Image: Authors)
    FIGURE 3. Tree decomposition for scenario II. (Image: Authors)
    FIGURE 3. Tree decomposition for scenario II. (Image: Authors)

    The proposed wavelet packet-based detection and mitigation algorithm is explained in three steps.

    Decomposition Step. The incoming GPS signal is decomposed through a uniform filter bank by only one level. Then, MAD is computed for all the decomposed sub-bands. Only sub-bands with a MAD value greater than the predefined threshold will be further decomposed. This step is repeated until the maximum predefined decomposition level is reached.

    Detection Step. The MAD value is computed for all sub-bands from the last decomposition level. Only sub-bands whose MAD value exceeds the predefined threshold are considered affected by interference and are used to reconstruct the jamming signal using the inverse wavelet transform.

    Reconstruction Step. In this step, the useful GPS signal is reconstructed free of interference by subtracting the estimated jamming signal from the received signal.

    Experimental Work

    In our investigation, a GNSS simulation system was used to create a fully controlled environment to examine and validate the performance of the proposed method using semi-real simulation scenarios. The simulator was controlled by simulation software that enables the simulation of multipath reflections through an advanced multipath model as well as atmospheric degradation to signals and the effects of antenna patterns and terrain obscuration. Moreover, it can generate simulated land, air, space and sea trajectories. Furthermore, the simulator when connected to an interference simulation system can provide various controlled jamming environments using an interference signal generator. The full setup is shown in FIGURE 4.

    FIGURE 4. Hardware experimental setup. (Image: Authors)
    FIGURE 4. Hardware experimental setup. (Image: Authors)

    The receiver used in this research is a prototype of a digital front end. The front end collects the output radio frequency (RF) signal from the simulator. Then, the RF signal is down-converted to baseband through several down-conversion stages, generating the in-phase (I) and quadrature-phase (Q) data. Then, the data is sampled and quantized through the ADC. The front end collects GPS L1 signals at different bandwidths ranging from 2.5 MHz to 20 MHz with quantization levels ranging from 1-bit to 8-bit. After that, the sampled digitized signal is sent to the computer via an Ethernet connection.

    The raw I/Q GPS samples are then processed by a GPS software receiver. Our proposed algorithms have been implemented using Matlab by modifying the open-source GPS software-defined radio (SDR) receiver composed by Borre and Akos, which is widely used in research.

    To verify the performance of the new proposed algorithm, a full GPS C/A-code signal was simulated using the previously mentioned simulation system. A static simulated scenario was generated for this purpose. This static scenario was run twice, once in an interference-free environment for reference, and one where the jamming signal was enabled. The simulation, front end and SDR receiver parameters are shown in TABLE 1.

    Table 1. Data collection and processing parameters. (Data: Authors)
    Table 1. Data collection and processing parameters. (Data: Authors)

    FIGURES 5 and 6 show the power spectral density (PSD)of the received signal before and after applying the proposed jamming mitigation technique. The figures demonstrate that the interference components have been highly attenuated. To confirm the benefits of the proposed technique, the reconstructed useful GPS signal has been acquired using the SDR receiver.

    FIGURE 5. PSD before jamming mitigation. (Image: Authors)
    FIGURE 5. PSD before jamming mitigation. (Image: Authors)
    FIGURE 6. PSD after jamming mitigation. (Image: Authors)
    FIGURE 6. PSD after jamming mitigation. (Image: Authors)

    FIGURE 7 shows that the receiver is in a total blockage as it failed to acquire any satellite before applying the jamming mitigation technique. However, FIGURE 8 shows that the proposed algorithm allowed the retrieval of seven satellites.

    FIGURE 7. Acquisition results before jamming mitigation. (Image: Authors)
    FIGURE 7. Acquisition results before jamming mitigation. (Image: Authors)
    FIGURE 8. Acquisition results after jamming mitigation. (Image: Authors)
    FIGURE 8. Acquisition results after jamming mitigation. (Image: Authors)

    FIGURE 9 shows the cross-ambiguity function (CAF) of PRN31 before jamming mitigation. It is obvious from the figure that the search space is quite noisy, and the receiver fails to acquire the GPS signal due to the difficulty of isolating the peak from the noise. However, FIGURE 10 shows that the peak clearly emerges from the noise floor and can be easily detected by the receiver after applying the jamming mitigation algorithm.

    FIGURE 9. CAF of PRN31 before jamming mitigation. (Image: Authors)
    FIGURE 9. CAF of PRN31 before jamming mitigation. (Image: Authors)
    FIGURE 10. CAF of PRN31 after jamming mitigation. (Image: Authors)
    FIGURE 10. CAF of PRN31 after jamming mitigation. (Image: Authors)

    These figures demonstrate the power of the proposed algorithm and confirms that the useful signal is not lost during the filtering process. Before applying the jamming mitigation algorithm, the receiver lost lock on all satellites and failed to provide a navigation solution. However, after applying the proposed algorithm, the navigation solution is available with an accuracy of about 10 meters in the east and north components and around 20 meters in the up component, as shown in FIGURE 11.

    FIGURE 11. Navigation solution. (Image: Authors)
    FIGURE 11. Navigation solution. (Image: Authors)

    Conclusion

    In this article, we have proposed a new method for mitigating a linear chirp narrowband jamming signal based on the WPT. Although the WPT has been widely used in the literature in the context of mitigating narrowband jamming, this technique is characterized by a significant computational complexity that is not only proportional to the length of the signal, but also proportional to the wavelet decomposition level.

    The results show that our proposed algorithm is able to maintain excellent performance in the suppression of the jamming signal with a significant reduction in complexity. In the proposed technique, the sub-bands affected by interference are identified and are further decomposed to be used to reconstruct the jamming signal. Then, the useful GPS signal is obtained by subtracting the estimated jamming signal from the received signal. The performance of the algorithm has been assessed with respect to acquisition and navigation performance. The results show that the proposed algorithm successfully suppressed narrowband jamming without significantly degrading the useful GPS signal.

    Acknowledgments

    This article is based on the paper “A Novel Wavelet Packet-based Jamming Mitigation Technique for Vector Tracking-based GPS Software Receiver” presented at ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018. The research was supported by the Natural Sciences and Engineering Research Council of Canada.

    Manufacturers

    The simulation system used a Spirent Communications Inc. GSS6700 Multi-GNSS Constellation Simulator, a Spirent GSS8366 Interference Combiner Unit and a Keysight Technologies N5172B-503 Interference Signal Generator. The receiver front end used was a NovAtel Inc. FireHose D17088 prototype digital GNSS front end.

    HAIDY Y. ELGHAMRAWY is a Ph.D. candidate in the Department of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario, Canada. She received her M.Sc. degree in engineering physics and mathematics from the Faculty of Engineering, Cairo University, Egypt.

    MOHAMED YOUSSEF is leading GPS/GNSS product development activities for Sony North America. He holds an interdisciplinary Ph.D. degree from the Department of Geomatics Engineering and the Department of Electrical and Computer Engineering, University of Calgary, Canada.

    ABOELMAGD M. NOURELDIN is a cross-appointment associate professor in the Departments of Electrical and Computer Engineering at Queen’s University and the Royal Military College (RMC) of Canada in Kingston. He is the director of RMC’s Navigation and Instrumentation Research Laboratory.

     

    FURTHER READING

    (to come)

  • Happy Pi Day

    Happy Pi Day

    In honor of 3.14.2019, here is what GPS World’s Innovation column editor Richard Langley wrote about π in an article (“A Sideways Look at How the Global Positioning System Works“) nine years ago.

    3.1415926…. π. Every nerd’s favorite number. It is the ratio of a circle’s circumference to its diameter in conventional or Euclidean space. We use it, for example, to convert angles measured in radians to degrees (π radians = 180 degrees). π is an irrational number, which means that its value cannot be expressed exactly as a fraction m/n, where m and n are integers. Consequently, its decimal representation never ends or repeats. But we sometimes use an easily remembered fraction, such as 22/7, to get an approximate value. In this case, 3.14. But, if we compute more digits with this fraction, we get 3.1428571…, clearly an incorrect result. A better way to remember π to eight digits is to count the number of letters in each word of the mnemonic “May I have a large container of coffee?”

    In computations related to GPS, how many digits of π should be used? It depends. If you are developing your own algorithms and software for modeling GPS observations or determining precise orbits for the satellites, you’ll likely need π to 16 digits for double-precision floating-point calculations. But it would be a mistake to use π to this precision in computing the position of a satellite from the broadcast ephemeris. The GPS interface specification document, IS-GPS-200, specifies a 14-digit value for π (3.1415926535898) in the satellite coordinate computation. Use fewer or more digits, and the resulting satellite coordinates will not be as accurate.

    Full article here.

    Thank you, Dr. Langley.

  • Innovation: An alternative to GNSS for maritime positioning

    Innovation: An alternative to GNSS for maritime positioning

    Enter the BinoNav

    An electronic pelorus is poised to become a useful tool in any mariner’s toolbox of resilient PNT systems. Learn how it works, and the benefits it brings to position fixing at sea.

    INNOVATION INSIGHTS by Richard Langley
    INNOVATION INSIGHTS by Richard Langley

    POP QUIZ: What do a character from Greek mythology, a point on the coast of Sicily, the pilot of Hannibal’s ship, a fizzy wine from New Zealand, and a navigation instrument have in common?

    They are all called Pelorus or pelorus in the case of the instrument as it’s not a proper noun (grammar lesson over). And while a discussion of each of the uses of the word could be quite educational, this month’s column, perhaps predictably, will be about the pelorus or rather a modernized version of it.

    If you are a landlubber, like me, you may not have heard of the pelorus. Yet, in one form or another it has been around for hundreds of years although not always going by that name. In appearance and use, it resembles a compass with sighting vanes.

    But it has no magnetic components of any sort. And while a compass is used to get a magnetic bearing of a charted feature such as a tower or lighthouse or the magnetic heading of a vessel, a pelorus is used to measure a relative bearing between a feature and a reference direction such as the heading of the vessel, commonly called the ship’s head.

    If a line is drawn on a chart through the sighted feature at an angle equal to the measured bearing, the vessel must be somewhere along this so-called line of position. If a second bearing on another feature significantly displaced from the first is measured in quick succession, a second line of position can be drawn on the chart, crossing the first.

    The intersection point gives the (two-dimensional or horizontal) location, or position fix, of the vessel. Since the measured bearings will have some error, generally at least three lines of position are established with their intersections forming a small triangle, sometimes called a “cocked hat.” The location of the vessel is either inside the triangle or nearby depending on the similarity of the bearing errors.

    Position fixes can also be obtained from instruments that measure ranges. In this case, the lines of position are circles for terrestrial systems providing two-dimensional fixes or spheres of position in the case of three-dimensional fixes obtained from GNSS measurements.

    But let’s get back to the pelorus. Most vessels of a certain size are equipped with a pelorus. Frequent use of the pelorus helps to maintain situational awareness and being a completely passive device, it is not dependent on receiving an electronic signal of any kind. Only an acceptable level of visibility is required. And it can provide a manual check on any automated ship’s systems such as a GNSS receiver.

    However, determining position fixes using a pelorus and a paper chart is laborious and time consuming and it is cumbersome to manually add lines of position to an electronic chart. What is needed is an electronic pelorus, which measures bearings electronically and automatically generates a line of position on an electronic chart.

    The General Lighthouse Authorities of the United Kingdom and Ireland, the agencies responsible for aids to navigation in the U.K. and Ireland, have developed such an instrument. Dubbed the BinoNav, it is poised to become a useful tool in any mariner’s toolbox of resilient PNT systems and in this month’s column, we learn about its genesis, how it works, and the benefits it brings to position fixing at sea.

    The overreliance on GNSS is well known and widely publicized. While GNSS is generally available, concerns remain on how maritime operations, and safe navigation in particular, are affected should GNSS not be usable, or become denied for any reason.

    The General Lighthouse Authorities of the United Kingdom and Ireland (GLA) have been working on resilient positioning, navigation and timing (PNT) for many years. This work has included a comprehensive review of different potential solutions and their availability. One option proposed is the development of a ship-based positioning system that makes use of a modernized pelorus to work with a modern bridge.

    Pelorus systems work by providing bearings from fixed positions, normally on the vessel bridge wings, to specific targets visible to the mariner and identified on the navigation chart. By taking several bearings in quick succession, intersecting lines can be drawn on the navigation chart, providing a position estimation. Clearly, there are limitations to this approach — these are explored within this article, but can be summarized as:

    • Automation. The time taken to measure the bearings can limit the achieved accuracy.
    • Visibility. Performance is limited by the mariner’s ability to see unique targets.
    • Paperless bridges. Many vessel bridges are moving away from paper, limiting the mariner’s ability to take bearings and plot them.
    • e-Navigation. More bridge systems require electronic values of latitude and longitude.

    In an attempt to resolve most of these limitations, the GLA has been working on the development of an enhanced pelorus, or ePelorus, with its name registered to the Research and Radionavigation Directorate (R&RNAV) as BinoNav.

    Prototype BinoNav systems have been developed and installed on all GLA vessels for trial. They enable the navigator to take visual bearings to known targets, from anywhere on the bridge using a handheld device — they are no longer confined to the bridge wings and targeting port or starboard objects.

    Measured bearings are automatically registered and drawn on an electronic chart. Multiple bearings can then be made with ease, each of which is displayed on the chart and the intersecting “cocked-hat” position (to be discussed later), calculated automatically. This information can then be used to feed other bridge systems and confirm the vessel’s position.

    In this article, I will provide a comprehensive overview of the BinoNav system, provide the results of initial trials and explain the planned development of the proposed resilient PNT solution.

    e-NAVIGATION

    Much has been written about e-Navigation elsewhere, but briefly, it is the International Maritime Organization’s (IMO’s) concept for the future of navigation, instigated by the U.K. Department for Transport in 2004. It will lead to the integration of systems and data — for the exchange of relevant geolocated information — faster and more cost effectively, and it will do this in the context of larger, faster vessels operating in ever more constricting shipping lanes and increasing offshore obstacles such as renewable energy infrastructure as well as the legacy of non-renewable energy infrastructure.

    e-Navigation is designed to enhance safety of life for the mariner, improve protection of the environment, and increase energy efficiency in terms of shorter routing for fuel-efficient shipping. Moreover, it will allow more effective use of resources and integration across transport modes, including the more effective provision of integrated port operations.

    Since its inception in 2004, development and delivery of e-Navigation services has been slow. Even now, some 14 years later, only a few prototype projects have delivered anything like what was anticipated in the original e-Navigation vision. This sluggishness has been caused by minimal leadership and drive from the IMO.

    Despite this, some initiatives have been successfully delivered on a local or regional basis. These initiatives have come largely through projects such as Accessibility for Shipping, Efficiency Advantages and Sustainability (ACCSEAS), Efficient Safe and Sustainable Traffic at Sea (EfficienSea) 1 & 2, Motorways and Electronic Navigation by Intelligence at Sea (MonaLisa) 1 & 2, and Sea Traffic Management (a MonaLisa project), all of which have been supported by funding from the European Union.

    Resiliency in PNT has been identified by the IMO as a lead area in the delivery of e-Navigation, and all these projects have used resilient PNT as the basis of what they have delivered.

    REQUIREMENT FOR RESILIENT PNT

    FIGURE 1. Ships’ systems affected by GPS jamming. (Data: Author)
    FIGURE 1. Ships’ systems affected by GPS jamming. (Data: Author)

    It is now well recognized that all GNSS are vulnerable to interference, whether these interferers are from natural causes such as space weather or from synthetic sources such as jamming or spoofing devices. GNSS receiving units and satellite failures also occur. There are many examples of each of these problems affecting GNSS worldwide.

    Resilient PNT information is needed to ensure continuity of maritime operations and safe navigation — especially for e-Navigation, management of sea traffic, and autonomous vessels.

    GPS jamming trials were conducted by GLA’s R&RNAV in 1994, 2008, 2009 and 2012. These trials showed the real-time vulnerability of maritime systems to jamming. They identified that many ships’ systems were affected by GPS jamming. However, some systems we did not expect to be affected actually were (see Figure 1). Devices such as the helicopter-deck stabilization system and the ship’s gyrocompass are good examples.

    GLA Work on Resilient PNT. GLA, through R&RNAV, has conducted a program of work that has looked at the issues of GNSS vulnerability and what they can do about it through a series of studies. These have looked at a number of systems such as

    • enhanced Loran, absolute radar positioning (two different methods)
    • ranging mode or R-mode, which is the use of ranging signals from existing marine infrastructure (two different methods)
    • signals of opportunity (many methods)
    • hybrid systems
    • dead reckoning
    • inertial
    • other on-board systems.

    The timeline for the introduction of some of these systems into operational use, as well as current and new GNSS, can be seen in Figure 2. This article deals with equipment that falls into the “other on-board systems” category.

    FIGURE 2. Timeline for resilient PNT (GNSS and complementary systems). (Diagram: Author)
    FIGURE 2. Timeline for resilient PNT (GNSS and complementary systems). (Diagram: Author)

    A DRIVER FOR OPTICAL NAVIGATION SYSTEMS

    The need for new optical navigation systems has been driven by a number of marine incidents, one of which I will discuss in detail.

    MV Tricolor Incident. On Dec. 14, 2002, in early morning thick fog, on its way from Zeebrugge to Southampton, the MV Tricolor, with a load of almost 3,000 BMW, Volvo and Saab cars, collided with a Bahamian-flagged container ship named Kariba, about 20 miles north of the French coast in the Dover Strait Traffic Separation Scheme.

    Albeit damaged above the water line, the Kariba could continue, while the MV Tricolor remained wedged on her side in 30 meters of water in a busy area of navigation. No lives were lost and the crew were rescued by the Kariba and a tugboat. Nevertheless, approximately 2,862 cars and 77 units of cargo, consisting mainly of tractors and crane parts, could not be salvaged.

    The shipping lane, being the busiest in the world, was marked by buoys and guarded by the French police vessel Glaive and HMS Anglesey, thereby warning other vessels of the MV Tricolor’s presence. Despite the marking and patrolling, only two days later a cargo ship, Nicola, followed by another vessel, Vicky (carrying 70,000 tonnes of highly flammable gas oil) collided with the wreck of the Tricolor, after failing to heed several French naval warnings. In between the two further collisions, more buoyage and patrol vessels were deployed. On Jan. 22, a third accident happened when a salvage tug knocked a safety valve off the Tricolor, resulting in a massive oil spill.

    Besides the heavy economic losses, including the estimated operation cost of around £25M (roughly $40M), the incident caused massive marine pollution and environmental contamination by spilling large quantities of oil. The Royal Society for the Protection of Birds estimated more than 1,000 birds were found dead or damaged by oil spilled from Tricolor.

    Why Did It Happen? The incident was blamed on declining professional standards among seafarers, which was leading to scores of near misses in the area every day. Indeed, Andrew Linnington of the National Union of Marine Aviation and Shipping Transport Officers is quoted as saying that ship owners had been cutting costs by reducing use of deep-sea pilots to guide vessels through the world’s most crowded shipping lanes. Ships were increasingly crewed by one trained officer and a few poorly paid sailors from parts of the developing world.

    “We know of at least four cases in the past year of ships going the wrong way in shipping lanes against the flow of traffic,” Linnington said. “Complaints are made to the states where the ships are registered, but they are often small countries used as flags of convenience and don’t have the resources to take action.”

    It is clear from the incident and the ensuing investigation that navigators were not looking out the window, despite various radio navigation warnings and other methods, not the least of which was deploying wreck-marking buoys and virtual aids to navigation.

    A very good way of mitigating the failure of any navigation system is by using reversionary methods of navigation, like looking out the window! This was a big driver in the GLA development of the BinoNav.

    WHAT IS BINONAV?

    FIGURE 3. A pelorus. (Photo: Author)
    FIGURE 3. A pelorus. (Photo: Author)

    BinoNav is an electronic pelorus. A pelorus is a device that is completely independent of any other system or electronic position fixing system (EPFS), and this is important for providing resiliency.

    Pelorus. A standard pelorus (see Figure 3) is used to take relative (to the vessel’s head) bearings to charted objects in the vicinity. The navigator then draws a line on the relevant navigation chart through the charted object. It is clear now that the vessel lies somewhere on this “line of position” from the charted object. This process is then repeated several times using different charted objects, with a minimum of three iterations.

    This process then creates a “cocked hat” (a triangle in the case of three lines of position) generated from the intersection of the lines. Accounting for systematic errors, the vessel should lie somewhere within this cocked hat (see Figure 4 for an example).

    This process is laborious and time consuming, but it does have the advantage of getting the navigator to look at real features outside the vessel — not just a red line on an electronic chart that they follow without question.

    FIGURE 4. An example of positioning using a pelorus. (Chart: Author)
    FIGURE 4. An example of positioning using a pelorus. (Chart: Author)

    What about Electronic Chart Display? Electronic Chart Display and Information Systems (ECDISs) are excellent, when used correctly, and have driven innovation in the shipping industry. However, they do have disadvantages: If you are using a pelorus, you cannot very easily draw on a screen. You can generate an electronic bearing line (EBL) on an ECDIS, but it is a very long, convoluted way of providing a position not derived from an EPFS, such as a GNSS fix.

    Any system that needs to generate an EBL on an ECDIS needs to do it electronically. Moreover, it needs to do this without having to rely on GNSS for position or time to avoid the issues of GNSS vulnerability: it should be completely independent. It should also be able to carry out optical to electronic integration to ensure that the mariner is looking out the window. Another GLA requirement was that it should be relatively low cost to make and distribute to enable take up across all users. So the idea of BinoNav was born. BinoNav fulfills all these criteria easily, intuitively and quickly, updating the electronic position of the vessel. Furthermore, with its wireless connection, bearings can be taken anywhere on the bridge of a vessel.

    BINONAV FEATURES

    In this section, I will describe the BinoNav and how it is used.

    FIGURE 5. The BinoNav configuration. (Photo: Author)
    FIGURE 5. The BinoNav configuration. (Photo: Author)

    Easy to Use. BinoNav comprises two parts: the “Bino” unit, which is a modified pair of binoculars, and a “base” unit that performs the communication link between the Bino unit and the electronic chart. Pick up the Bino unit from the base unit (see Figure 5 for overall configuration of the BinoNav).

    Line up the graticule inside the Bino unit with a charted feature of use, press either of the buttons to automatically generate a line on the displayed electronic chart, which is relative to the ship’s head. As with a standard pelorus, one needs at least another two of these EBLs to generate a cocked-hat position on the electronic chart. Using either the touch screen or the mouse, “hover” over the cocked hat to generate a triangle. Now, right click to drop a marker at the center of the cocked-hat position and delete all lines. Once the vessel has moved (and dictated by the operating environment at the time), this process can be repeated. When two or more of the markers have been dropped, a line is drawn between the marks, thereby showing a track on the chart.

    Features. From the use of the BinoNav unit as described above, a track is produced on an electronic chart that is not derived from an EPFS. This is important as it shows the integration of visual navigation into e-Navigation, something which e-Navigation has tried to do from the very beginning, as described by Brian Wadsworth in his earliest vision of e-Navigation (see Further Reading).

    Another feature of BinoNav is “radar mode” for charted feature recognition. This feature draws a continuously moving line on the display that points at the position relative to the ship’s head. This is useful for the recognition of charted features when in unfamiliar territory.

    The BinoNav is very easy to install, with only a connection for power and a connection for a suitable National Marine Electronics Association (NMEA) protocol data feed for heading. Many of its electronic components are available off the shelf and are widely available commercially with bespoke printed circuit boards. Some modification to the binocular unit has been necessary, with the addition of a bespoke unit, which links to the base unit for both orientation measurement and power when the unit is docked. The binoculars are readily available for around $500. The gyros incorporated in both the base unit and the binocular unit are high-grade microelectromechanical systems (MEMS) devices giving an angular resolution of 0.25-0.5 degrees, similar to that of a standard pelorus.

    Currently, the BinoNav is 3D-printed, which allows for the quick production of one-off units. However, this approach is clearly not a suitable solution for long production runs and would require a different method of production.

    FIGURE 6. The BinoNav installation on THV Alert. (Photo: Author)
    FIGURE 6. The BinoNav installation on THV Alert. (Photo: Author)

    Something for the Future. R&RNAV has received a lot of interest in the BinoNav not only from our own mariners, but also from a variety of influencers in the maritime world. We have had a great deal of positive feedback on potential improvements and additional features that we plan to develop.

    We will also seek to gain approvals through IMO and the International Electrotechnical Commission to integrate BinoNav with ECDIS, so there will be no need for separate displays (unless being used on non-SOLAS vessels; that is, ones to which the International Convention for the Safety of Life at Sea does not apply.)

    CURRENT GLA INSTALLATIONS

    FIGURE 6. The BinoNav installation on THV Alert. (Photo: Author)
    FIGURE 7. Using the BinoNav on ILV Granuaile. (Photo: Author)

    The BinoNav has been installed on all six GLA vessels: ILV (Irish Lights Vessel) Granuaile, NVL (Northern Lighthouse Vessel) Pharos, NVL Pole Star, THV (Trinity House Vessel) Alert, THV Galatea and THV Patricia. The installation on Alert is shown in Figure 6 and BinoNav use on Granuaile is shown in Figure 7.

    CONCLUSIONS

    The key points made in this article can be summarized as follows:

    • e-Navigation is based on the premise of electronic navigation from “berth to berth.”
    • Many accidents happen because crews do not look out the window.
    • There is a need for electronic positioning from non-GNSS sources.
    • The BinoNav integrates visual navigation and electronic navigation through an ECDIS.
    • The BinoNav provides an independent verification of position with or without EPFS.

    INTELLECTUAL PROPERTY

    BinoNav is a registered trade mark and carries unregistered design rights. BinoNav has patents pending.

    ACKNOWLEDGMENTS

    The author thanks the masters, officers and crews of all the GLA vessels for their help and for the benefit of their experience throughout the whole process of the BinoNav development. Special thanks go to those who helped during the various development trials on ILV Granuaile and THV Alert prior to the mainstream installations.

    This article is based on the paper “BinoNav® – A New Positioning System for Maritime” presented at ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018.

    MARTIN BRANSBY is the head of the Research and Radionavigation Directorate at the General Lighthouse Authorities of the UK and Ireland, stationed in Harwich, Essex. He is responsible for the delivery of its program portfolio in research and development in technically diverse areas such as resilient PNT, e-Navigation, GNSS, Automatic Identification System (AIS) and visual signaling. He is a fellow of the Royal Institute of Navigation, and holds memberships in the Institute of Engineering and Technology and The Institute of Navigation. He is also a member of the International Association of Marine Aids to Navigation and Lighthouse Authorities’ AtoN (Aid to Navigation) Requirements and Management Committee.

    FURTHER READING

    • Author’s Conference Paper

    “BinoNav® – A New Positioning System for Maritime” by M. Bransby in Proceedings of ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018, pp. 1728–1735.

    • The Sinking of the Tricolor

    “MV Tricolor.” Wikipedia article: https://en.wikipedia.org/wiki/MV_Tricolor

    “Tricolor/Kariba.” Report by Cedre: Centre of Documentation, Research and Experimentation on Accidental Water Pollution, Aug. 31, 2004.

    “The Tricolor Incident: From Collision to Environmental Disaster” by F. Kerckhof, P. Roose, and J. Haelters in Atlantic Seabirds, Vol. 6, No. 3, 2004, pp. 85–94.

    “Cargo Ship Hits Sunken Car Carrier” by O. Bowcott and A. Clark in The Guardian, Dec. 17, 2002.

    • eNavigation

    “Marine eNavigation: An Orientation Paper” by B. Wadsworth, document WEND9-INF4, presented to the 9th meeting of the International Hydrographic Organization World-wide Electronic Navigational Chart Database (WEND) Committee, Monaco, April 7–8, 2005.

    • GPS Jamming and Its Consequences

    Satellite-derived Time and Position: A Study of Critical Dependencies, edited by S. Battersby, U.K. Government Office for Science, London, U.K., 2018.

    The Economic Impact on the UK of a Disruption to GNSS by G. Sadlier, R. Flytkjær, F. Sabri and D. Herr, London Economics, June 2017.

    “Know Your Enemy: Signal Characteristics of Civil GPS Jammers” by R.H. Mitch, R.C. Dougherty, M.L. Psiaki, S.P. Powell, B.W. O’Hanlon, J.A. Bhatti and T.E. Humphreys in GPS World, Vol. 23, No. 1, January 2012, pp. 64–72.

    “The Impact of GPS Jamming on the Safety of Navigation” by S. Basker, A. Grant, P. Williams and N. Ward, presented at the 48th meeting of the Civil GPS Service Interface Committee, Savannah, Georgia, Sept. 15–16, 2008.