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  • UAV Shipboard Landing with RTK

    plane_landing-O

    Carrier Phase Compensates for Wind and Wave Motion

    Limited landing area as well as interference due to wind disturbance and wave motion make shipboard landings of unmanned aerial vehicles (UAVs) extremely difficult. Use of UAVs at sea can enhance the efficiency of intelligence gathering and surveillance, and could also increase long-range air-strike capability. To successfully land aircraft in such a challenging environment requires a high-precision navigation system; this prototype applies RTK measurements.

    By Chiu-Jung Huang and Shau-Shiun Jan

    UAVs can perform functions such as surveying, imaging, detection, sensor work, rescue, and geographic information systems (GIS) data collection. The exploitation of UAVs with portable launching and recovery systems using an automatic guidance equipment can enhance their flexibility in many practical applications. In particular, UAVs can achieve great effectiveness from launch and recovery aboard ships at sea. However, the landing area is narrow on a ship, and interference related to the maritime environment due to wind disturbance and wave motions varies greatly, making maritime UAV landings quite difficult. Recovering these aircraft in such a rapid-dynamic environment requires a high-precision UAV navigation system.

    Generally, UAVs use a differential GPS (DGPS) aiding station to continuously transmit positioning correction information during landing approach; this method can provide about 0.7 to 1-meter accuracy. However, shipboard landings require more stringent accuracy. According the Joint Precision Approach and Landing System (JPALS), the requirements of shipboard landing include vertical accuracy on the order of 0.3 meters, and the requirement for the vertical protection level is 1.1 meters. To fulfill these accuracy requirements, we have chosen the real-time kinematic (RTK) technique. Recently, researchers have studied the use of RTK satellite navigation. The Boeing Unmanned Little Bird program has been examining shipboard launch and recovery using related navigation techniques.

    The accuracy of using RTK navigation is 1 centimeter + 1 part per million.

    Figure 1. Flow chart for software-in-the-loop.
    Figure 1. Flow chart for software-in-the-loop.

    Since development of shipboard landing is costly in terms of time and many resources, including human resources, this research is an attempt to evolve a software-in-the-loop (SIL) simulation system to analyze the accuracy of using RTK for landing navigation. The SIL system uses the MATLAB Simulink interface becasue of its helpfulgraphic user interface and block diagrams. A flowchart of the SIL system is shown in Figure 1.

    The simulated RTK message provides the navigational data used as the analysis results from the experiments. To ensure the stability of the landing process, the aircraft models were control by a linear quadratic Gaussian regulator (LQG), which is able to reject the environmental disturbances encountered in the landing process. The ship motions were simulated using the factors and the model formulated by the International Towing Tank Conference. A combined position error consisting of the aircraft controls and ship motions was calculated and then fed back to the RTK navigation message.

    RTK Performance

    RTK navigation provides high positioning performance in the range of a few centimeters; the technique can eliminate main errors, including ionospheric and tropospheric errors and satellite clock errors, among others. A base station and a rover station can cover a service area of about 10 to 20 square kilometers. The data transition should be in real time using a wireless VHF or Wi-Fi modem.

    Because data for shipboard landings are difficult to acquire, the navigation message in the SIL was simulated using experiments involving a variety of conditions. In this article, four kinds of experiments were included to help verify the availability and reliability of using RTK information as a navigational message.

    We started with a basic kinematic experiment, which was simply used to assess the RTK performance. Next, a relative positioning experiment was conducted to ensure the RTK relative positioning accuracy was adequate. After that, an antenna reversal experiment was designed in order to understand the ship’s swing effect in which aircraft altitude might cause a lack of common view satellites. Finally, an antenna forward flip experiment was conducted intended to show the different RTK positioning results for a variety of sea state effects.

    All of the experimental data were collected by a workshop computer through a program data file. The analyses of the results included the mean, standard deviations of positioning error, unavailable RTK percentages and the positioning accuracy when RTK was unavailable. All of the analysis results were imported to the SIL simulation using the Gaussian random variable model.

    Figure 2. Kinematic experimental setup.
    Figure 2. Kinematic experimental setup.

    Kinematic Experiment. The base station setup included an antenna, tripod, and receiver. The rover station setup included a portable vehicle with a battery, antenna, and receiver placed as shown in Figure 2. The data were transmitted and received using a wireless modem for which the transmitted rate was 115200 bps. The receiver was connected to a laptop used as a workshop to monitor satellite quality and collect the data. The region in which the experiment took place is shown in Figure 3: on the roof of the Aeronautics and Astronautics department building at National Cheng Kung University in Taiwan. The red star is the known position of the base station. The broken rectangular red line is 25 meters by 10 meters along which the moving rover station moved clockwise.

    Figure 3. Kinematic experimental region.
    Figure 3. Kinematic experimental region.

    However, it is difficult to show the true positions of the experiment. In this article, we tried to get the true position by using a linear regression method which used the time, t, as the explanatory variable and the position, y(t), as the dependent variable. The linear regression used the past five epoch positions as the dependent variables by which to obtain the linear polynomial, and the fifth position was put into the polynomial to get the position error. For example, in order to calculate an error at t=4, the position results from t=0 to t=4 must be taken into Equation (1) to form the second order polynomials with parameters P, Q, and R

    Eq-1 (1)

    The experimental results are shown in Figure 4, which is the ENU positioning error, and Table 1 shows the analysis error mean and standard deviations. The experimental results show that the horizontal positioning accuracy is 0.037 meters (95 percent).

    Figure 4. ENU error results for the kinematic experiment.
    Figure 4. ENU error results for the kinematic experiment.
    Table 1. Positioning results for the kinematic experiment.
    Table 1. Positioning results for the kinematic experiment.

    Relative Experiment. This experiment had one base station as before and included two rover stations which were placed on a T-bar, the relative distance being known, on a portable cart as shown in Figure 5. The region of the experiment is shown in Figure 6, where the star marks the location of the base station, with the rover station moving along the black arrow.

    Figure 5. Experimental setup.
    Figure 5. Experimental setup.
    Figure 6. Relative experimental region.
    Figure 6. Relative experimental region.

    The relative error was calculated using a known distance, 0.72 meters, to compare the two rover station positions. Figure 7 shows the relative results of the experiment for which the mean value and standard deviations were recorded in Table 2. In this experiment, only about 4.5 percent of the positioning results failed to meet the requirement of 0.3 meters.

    Figure 7. Relative error results.
    Figure 7. Relative error results.
    Table 2. Positioning results for the relative experiment.
    Table 2. Positioning results for the relative experiment.

    Common-View Satellite Experiment. Aircraft landing altitude and the ship’s swing motion caused by the state of the sea might affect GNSS information received by the antenna. This experiment had one base station and one rover station at fixed positions as before, but we attempted to flip the antenna of the base station toward the north by 80 degrees, as shown in Figure 8, and the rover station changed direction according to Table 3. The antenna directional change of 80 degrees were chosen for the extreme case that the base station and rover station could experience completely different satellites in view.

    Table 3. Common view satellite experimental setup for antenna.
    Table 3. Common view satellite experimental setup for antenna.
    Figure 8. Common view satellite experimental setup.
    Figure 8. Common view satellite experimental setup.

    Results of the experiment are shown in Figure 9, in which the vertical lines indicate antenna directional changes. For this experiment, every change is 30 seconds. This experiment demonstrates that the position performance definitely varies. The position analysis is shown in Table 4, which shows a horizontal error of 0.116meters (95 percent).

    Figure 9. ENU results of the common view satellite experiment.
    Figure 9. ENU results of the common view satellite experiment.
    Table 4. Positioning results for the common view satellite experiment.
    Table 4. Positioning results for the common view satellite experiment.

    Sea-State Experiment. In this experiment, one base station and one rover station were required in a fixed position, but the rover station changed the direction of the antenna, as shown in Figure 10, where the angle of x is decided according to the sea state in Table 5. On the other hand, the antenna changing toward a different direction simulated the swing motion of the boat.

    Figure 10. Swing experimental setup.
    Figure 10. Swing experimental setup.
    Table 5. Antenna angle in the swing experiment.
    Table 5. Antenna angle in the swing experiment.

    The experimental results shown in Table 6 are the mean values, and Table 7 shows the standard deviations. The simulation provides the analysis results in order to authenticate the integration simulations. The results show that the sea state slightly influences RTK positioning.

    UAV and Ship Motion Simulations

    During shipboard landing processing, many complicated conditions must be taken into account, including crosswinds, an air-wake model, wind gusts, and deck motion. The ship deck motion and crosswind effects are two key factors that further increase the difficulty of ship-borne operations.

    For this reason, the UAV controller must have anti- interference features. An LQG controller is able to reject the environmental disturbances encountered during landing in a lateral motion. For the ship deck motion, the chosen spectrum (the International Towing Tank Conference, or ITTC two-parameter spectrum) was used as the power spectrum of the sea waves to be simulated.

    Aircraft Simulation. The aircraft was in the simulation, the SP.X-6, was designed by the Remotely Piloted Vehicle and Microsatellite Research Laboratory of National Cheng Kung University (see opening photo and cover). For the longitudinal motion, a combination of a linear quadratic integral (LQI) controller and a Kalman filter in the inner-loop system was used to control the vertical velocity and height mainly using an elevator. For the lateral motion, the LQG autopilots were designed with guaranteed robustness properties that allowed quick return to the designed point.

    The SP.X-6 aircraft state functions are shown in Equation 2, in which the x, u, y, w, and v mean the system state vector, input, measurement, process error vector, and the measurement error, respectively. A, B, C, and K refer to the system state matrices, which can be evaluated by the system identifications that are derived by using the subspace identification to obtain an initial model. After that, the initial model will feed into the recursive prediction error method algorithm in order to arrive at further refined models.

    Eq-2 (2)

    Figure 11. Linear quadratic Gaussian regulator block diagram.
    Figure 11. Linear quadratic Gaussian regulator block diagram.

    After obtaining the aircraft’s model, the LQG controller is used, a block diagram for which is shown in Figure 11 and for which the close-loop dynamic is given by Equation 3. The Eq-x means the estimated states are feedback by which to form the optimal control law, u=−KEq-x. The y means the output command with the LQG variables F, G, K, and L.

    Eq-3 (3)

    The aircraft landing controls were divided into the longitudinal and lateral dynamics. For the longitudinal dynamics, the landing command was the vertical discrete height. In the case of the lateral dynamics, the stable condition was used when disturbances were encountered.

    Up till now, navigation of SP.X-6 relied solely on the GPS signal. Using RTK technique for the landing process will enhance navigation accuracy. The navigation method is the point-to-point guidance law illustrated in Figure 12.

    Figure 12. The point-to-point guidance law.
    Figure 12. The point-to-point guidance law.

    The basic concept of the point-to-point guidance law can be derived from the aircraft initial position A and the target position B in two-dimensional coordinate frame at every epoch. Desired heading angle θT and the distance between two points d can computed at each control loop via Equation 4.

    Eq-4 (4)

    The navigation signal used in the simulation is of 20 Hz.

    Deck Motion Simulation. Variations in waves are formed by the wind, and waves do not propagate only in one direction; the other direction will also affect wave propagation. The wave always is set as a stationary random process for the purpose of processing. The Longuet-Higgins model assumes that random waves are composed of many different wavelengths and harmonic amplitude superposition. Assuming the wave travels in a fixed direction, the peaks and troughs of the wave lines are parallel to each other and perpendicular to the forward direction of the waves, which are called two irregular waves or crested waves. Crested waves cause greater ship motion. The crested wave model indicates that point a at t epoch on a random sea wave height can be expressed as Equation 5, where ai -th represents harmonic waves with ωi frequency and εi initial condition.

    Eq-5 (5)

    It can be seen that the wave function can be expressed as a superposition of individual harmonics, so as long as waves establishing harmonic amplitudes and harmonic frequencies can be simulated in order to create the wave model. In this research, the amplitudes and the initial conditions are obtained from the sea wave spectrum of the ITTC model:

    Eq-6 (6)

    Four different sea state conditions were designed, as shown in Table 8 in the integrated simulation. Using the parameters from the spectrum analysis and the frequency divide method, the sea wave simulation could be obtained. Figures 13 and 14 show the simulation results of sea state A. Figure 15 shows all four state spectrum simulations results, and Figure 16 shows the sea wave height.

    Figure 13. Sea State A spectrum.
    Figure 13. Sea State A spectrum.
    Figure 14. Sea State A wave height.
    Figure 14. Sea State A wave height.
    Figure 15. Wave spectrum simulation results.
    Figure 15. Wave spectrum simulation results.
    Figure 16. Wave height simulation results.
    Figure 16. Wave height simulation results.

    Integrated Simulations

    In the integrated simulation, first the health of the RTK information was examined, and then, according the environment parameter settings, sea wave simulations were conducted. Subsequently, the aircraft landing process errors were presented using the experimental positioning analysis.

    The integrated simulation system is shown in Figure 17; it can be divided into three parts. The first part is the sea state options shown in the black line region, and the sea wave change is displayed and the maximum changing rate is calculated after the sea state option is selected. The second part is shown in the green line region that is the landing analysis which includes RTK health status, ENU error size. The last part is the landing animation which is enclosed in the red line region.

    Figure 17. Integrated simulations graphic user interface.
    Figure 17. Integrated simulations graphic user interface.

    Four sea-wave height simulation statuses can be selected, and the chosen sea state can be used to determine the corresponding landing environment, as shown in Figure 18, which illustrates the ship motion simulated by the wave height.

    Figure 18. Sea wave change.
    Figure 18. Sea wave change.

    RTK health information was simulated according to the experimental results in Table 9, in which the RTK information unavailability was 1.1 percent. A random Gaussian number was used to simulate the health of the RTK satellite information.

    After the sea-wave simulation and the RTK health simulation, the second concern was the landing process simulation. The landing process simulation has two conditions, namely the “normal landing” condition and the “landing with common-view satellite problem” condition. The normal landing process errors were presented using the Sea State Experiment results, while the landing with common-view satellite problem process errors was simulated by the result of Common View Satellite Experiment positioning analysis.

    For example, a ship was traveling at a velocity of 10 m/s in East, and an aircraft was cruising at a velocity of 20 m/s toward the East. The initial position of the ship was at (ES, NS, US) = (200, 0, 0) and the aircraft was at (EA, NA, UA) = (0,150,100). In the landing process, the desired heading angle and the distance to the waypoint were evaluated every epoch. The simulated landing process example is shown in Figure 19; the blue line is the ship’s trajectory and the red line indicates the aircraft’s trajectory.

    Figure 19. The simulated landing process example.
    Figure 19. The simulated landing process example.

    The guidance accuracy includes the control accuracy and the navigation sensor measurement accuracy. In the simulation result, the control accuracy (that is, controller error) was neglected. Therefore, the error for the landing process becomes only the navigation sensor measurement error which was the RTK error in this article. Users have the options to add different controllers as well as the controller error in the simulations.

    The landing positioning error was simulated using the imported analysis results in the correspondence sea state included in the RTK status shown in Figure 20 and the landing ENU errors are shown in Figure 21.

    Figure 20. RTK state simulation results.
    Figure 20. RTK state simulation results.
    Figure 21. The ENU errors of the simulated landing process example.
    Figure 21. The ENU errors of the simulated landing process example.

    Red stars in Figure 20 indicate the warning window when the simulated RTK statuses were unhealthy. For example, the 114th, 126th, 169th and 240th epochs in Figure 21 indicate that RTK data is unavailable during this time simulation. The unhealthy RTK signal might cause interruptions in navigation service in the landing process, as shown as the red stars in Figure 21. For the epochs with red stars, the simulated position results were exceeding the performance requirement for RTK shipboard landing. When this situation happened, the monitoring system might raise a flag to the aircraft’s guidance system not to use the RTK signal for landing at this period of time. Excluding these unhealthy RTK epochs, the simulated landing errors were well met the performance requirement for RTK shipboard landing, as shown in Figure 22.

    Figure 22. The ENU errors of the simulated landing process after excluding the unhealthy RTK results.
    Figure 22. The ENU errors of the simulated landing process after excluding the unhealthy RTK results.

    An overall simulation result is illustrated in Figure 23, when the successful landing message was shown in a pop-up window, the landing information of the whole landing process would be shown in the graphic user interface.

    Figure 23. Example simulation result.
    Figure 23. Example simulation result.

    Conclusions

    Experimental results showed that 99 percent of the horizontal positioning was in the range requirement of 0.3 meters. Using the common view satellite experiment and the sea state variation experiment conducted in this study, the limitations of RTK positioning can be understood. Monitoring the RTK status can provide high-quality accuracy with regard to guidance of the landing process. We hope that the results of this study will become a reference for building a shipboard landing system in Taiwan.

    Manufacturers

    All of the experimental data were collected by a workshop computer through a NovAtel (www.novatel.com) Connect program data file. The base station setup included a NovAtel GPS-703-GGG antenna with a Sokkia tripod and the NovAtel Propak-V3 RT2-G receiver. The rover station setup included a portable vehicle with a battery, a NovAtel GPS-703-GGG antenna and the NovAtel Propak-V3 RT2-G receiver.


    Chiu-Jung Huang received her B.S. degree from National Cheng Kung University (NCKU) in Taiwan. She is currently studying for her M.S. degree in aeronautics and astronautics at NCKU.

    Shau-Shiun Jan is an associate professor of aeronautics and astronautics at NCKU. He directs the NCKU Communication and Navigation Systems Laboratory (CNSL). His research focuses on GNSS augmentation system design, analysis, and application. He received his Ph.D. degree in aeronautics and astronautics from Stanford University.

  • Innovation: Reducing the Jitters

    Innovation: Reducing the Jitters

    Chip-scale atomic clock.
    Chip-scale atomic clock.

    How a Chip-Scale Atomic Clock Can Help Mitigate Broadband Interference

    Small low-power atomic clocks can enhance the performance of GPS receivers in a number of ways, including enhanced code-acquisition capability that precise long-term timing allows. And, it turns out, such clocks can effectively mitigate wideband radio frequency interference coming from GPS jammers. We learn how in this month’s column.

    By Fang-Cheng Chan, Mathieu Joerger, Samer Khanafseh, Boris Pervan, and Ondrej Jakubov

    GPS World photo
    INNOVATION INSIGHTS by Richard Langley

    THE GLOBAL POSITIONING SYSTEM is a marvel of science and engineering. It has become so ubiquitous that we are starting to take it for granted. Receivers are everywhere. In our vehicle satnav units, in our smart phones, even in some of our cameras. They are used to monitor the movement of the Earth’s crust, to measure water vapor in the troposphere, and to study the effects of space weather. They allow surveyors to work more efficiently and prevent us from getting lost in the woods. They navigate aircraft and ships, and they help synchronize mobile phone and electricity networks, and precisely time financial transactions.

    GPS can do all of this, in large part, because the signals emitted by each satellite are derived from an onboard atomic clock (or, more technically correct, an atomic frequency standard). The signals from all of the satellites in the GPS constellation need to be synchronized to within a certain tolerance so that accurate (conservatively stated as better than 9 meters horizontally and 15 meters vertically, 95% of the time), real-time positioning can be achieved by a receiver using only a crystal oscillator. This requires satellite clocks with excellent long-term stability so that their offsets from the GPS system timescale can be predicted to better than about 24 nanoseconds, 95% of the time. Such a performance level can only be matched by atomic clocks.

    The very first atomic clock was built in 1949. It was based on an energy transition of the ammonia molecule. However, it wasn’t very accurate. So scientists turned to a particular energy transition of the cesium atom and by the mid-1950s had built the first cesium clocks. Subsequently, clocks based on energy transitions of the rubidium and hydrogen atoms were also developed. These initial efforts were rather bulky affairs but in the 1960s, commercial rack-mountable cesium and rubidium devices became available. Further development led to both cesium and rubidium clocks being compact and rugged enough that they could be considered for use in GPS satellites. Following successful tests in the precursor Navigation Technology Satellites, the prototype or Block I GPS satellites were launched with two cesium and two rubidium clocks each. Subsequent versions of the GPS satellites have continued to feature a combination of the two kinds of clocks or just rubidium clocks in the case of the Block IIR satellites.

    While it is not necessary to use an atomic clock with a GPS receiver for standard positioning and navigation applications, some demanding tasks such as geodetic reference frame monitoring use atomic frequency standards to control the operation of the receivers. These standards are external devices, often rack mounted, connected to the receiver by a coaxial cable—too large to be embedded inside receivers.

    But in 2004, scientists demonstrated a chip-scale atomic clock, and by 2011, they had become commercially available. Such small low-power atomic clocks can enhance the performance of GPS receivers in a number of ways, including enhanced code-acquisition capability that precise long-term timing allows. And, it turns out, such clocks can effectively mitigate wideband radio frequency interference coming from GPS jammers. We learn how in this month’s column.


    “Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. Write to him at lang @ unb.ca.


    Currently installed Local Area Augmentation System (LAAS) ground receivers have experienced a number of disruptions in GPS signal tracking due to radio frequency interference (RFI). The main sources of RFI were coming from the illegal use of jammers (also known as personal privacy devices [PPD]) inside vehicles driving by the ground installations. Recently, a number of researchers have studied typical properties of popular PPDs found in the market and have concluded that the effect of PPD interference on the GPS signal is nearly equivalent to that of a wideband signal jammer, to which the current GPS signal is most vulnerable. This threat impacts LAAS or any ground-based augmentation system (GBAS) in two ways:

    • Continuity degradation — as vehicles with PPDs pass near the GBAS ground antennas, the reference receivers lose lock due to the overwhelming noise power.
    •  Integrity degradation — the code tracking error will increase when the noise level in the tracking loop increases.

    Numerous interference mitigation techniques have been studied for broadband interference. The interference mitigation methods can be separated according to the two fundamental stages of GPS signal tracking: the front-end stage, in which automatic gain control and antenna nulling/beam forming techniques are relevant, and the baseband stage, where code and carrier-tracking loop algorithms and aiding methods are applicable.

    In our current work, the baseband strategy and resources that are practically implementable at GBAS ground stations are considered. Among those resources, we focus on using atomic clocks to mitigate broadband GNSS signal interference. For GPS receivers in general, wide tracking loop bandwidths are used to accommodate the change in signal frequencies and phases caused by user dynamics. Unfortunately, wide bandwidths also allow more noise to enter into the tracking loop, which will be problematic when wideband inference exists. The general approach to mitigate wideband interference is to reduce the tracking loop bandwidth. However, a reference receiver employing a temperature-compensated crystal oscillator (TCXO) needs to maintain a minimum loop bandwidth to track the dynamics of the clock itself, even when all other Doppler effects are removed. The poor stability of TCXOs fundamentally limits the potential to reduce the tracking loop bandwidth. This limitation becomes much less constraining when using an atomic clock at the receiver, especially in the static, vibration-free environment of a GBAS ground station.

    Integrating atomic clocks with GPS/GNSS receivers is not a new idea. Nevertheless, the practical feasibility of such integration remained difficult until recent advancements in atomic clock technology, such as commercially available compact-size rubidium frequency standards or, more recently, chip-scale atomic clocks (CSACs). Most of the research using atomic clock integrated GPS receivers aims to improve positioning and timing accuracy, enhance navigation system integrity, or coast through short periods of satellite outages. In these applications, the main function of the atomic clock is to improve the degraded system performance caused by bad satellite geometries. As for using narrower tracking loop bandwidths to obtain better noise/jamming-resistant performance, the majority of work in this area has focused on high-dynamic user environments with extra sensor aiding, such as inertial navigation systems, pseudolites, or other external frequency-stable radio signals. These aids alone do not permit reaching the limitation of tracking loop bandwidth reduction since the remaining Doppler shift from user dynamics still needs to be tracked by the tracking loop itself. Our research intends to explore the lower end of the minimum tracking loop bandwidth for static GPS/GNSS receivers using atomic clocks.

    High-frequency-stability atomic clocks naturally reduce the minimum required bandwidth for tracking clock errors (since clock phase random variations are much smaller). We have conducted analyses to obtain the theoretical minimum tracking loop bandwidths using clocks of varying quality. Carrier-phase tracking loop performance under deteriorated C/N0 conditions (that is, during interference) was investigated because it is the most vulnerable to wideband RFI. The limitations on the quality of atomic clocks and on the receiver tracking algorithms (second- or third-order tracking loop bandwidths) to achieve varying degrees of interference suppression at the GBAS reference receivers are explored. The tracking loop bandwidth reductions and interference attenuations that are achievable using different qualities of atomic clocks, including CSACs and commercially available rubidium receiver clocks, are also discussed in this article.

    In addition to the theoretical analyses, actual GPS intermediate frequency (IF) signals have been sampled using a GPS radio frequency (RF) frond-end kit, which is capable of utilizing external clock inputs, connected to a commercially available atomic clock. The sampled IF data are fed into a software receiver together with and without simulated wideband interference to evaluate the performance of interference mitigation using atomic clocks. The wideband interference is numerically simulated based on deteriorated C/N0. The actual tracking errors generated from real IF data are used to validate the system performance predicted by the preceding broadband interference mitigation analyses.

    Signal Tracking Loop and Tracking Error

    The carrier-phase tracking phase lock loop (PLL) is introduced first to understand the theoretical connection between the carrier-phase tracking errors and the signal noise plus receiver clock phase errors. A simplified PLL is shown in FIGURE 1 with incoming signals set to zero. In the figure, n(s), c(s), and δθ(s) are receiver white noise, clock phase error or clock disturbance, and tracking loop phase error respectively, with s being the Laplace transform parameter. G(s) is the product of the loop filter F(s) and the receiver clock model 1/s.

    FIGURE 1. Simplified tracking loop diagram.
    FIGURE 1. Simplified tracking loop diagram.

    From Figure 1, the transfer functions relating the white noise and clock disturbance to the output can be derived as:
    In-E1(1)

    The frequency response of H(s) is complementary to 1-H(s). Therefore, the PLL tracking performance is a trade-off between the noise rejection performance and the clock disturbance tracking performance.

    Total PLL errors resulting from different error sources are presented as phase jitter, which is the root-mean-square (RMS) of resulting phase errors. Equation (2) shows the definition of the standard deviation of phase jitter resulting from the error sources considered in this work:
    In-E2 (2)

    where IN-TXT1, and IN-TXT2 are standard deviations of receiver white noise, receiver clock errors, and satellite clock error, respectively, for static receivers.

    The standard deviation for each of the clock error sources can be evaluated using the frequency response of the corresponding transfer function and power spectral densities (PSDs). The equations to evaluate the phase error from each error source are:
    In-E3 (3)

    where Srx and Ssv are one-sided PSDs for receiver clock and satellite clock, respectively. Bw is the bandwidth of the tracking loop and Tc is the coherent integration time.

    Receiver and Satellite Clock Models

    In general, the receiver noise can be reasonably assumed to be white noise with constant PSD with magnitude (noise density) of N0. However, it is not the case for clock errors. The clock frequency error PSD is usually formulated in the form of a power-law equation and has been used to describe the time and frequency behaviors of the random clock errors in a free running clock:

    In-E4(4)

    where sy(f) represents the PSD of clock frequency errors and is a function of frequency powers.

    The clock phase error PSD can be analytically derived from the frequency PSD equation because the phase error is the time integral of the frequency error:
    In-E5(5)

    where f0 is the nominal clock frequency. The h coefficients of the clock phase error PSD are the product of the h coefficients from the clock frequency error PSD and the nominal frequency.

    We have adopted the PSD clock error models in our work to perform tracking loop performance analysis. The PSD of the CSAC is derived from an Allan deviation figure published by the manufacturer and is shown in FIGURE 2. We took three piecewise Allan deviation straight lines, which are slightly conservative, and converted them to a PSD.

    FIGURE 2. Allan deviations for chip-scale atomic clock.
    FIGURE 2. Allan deviations for chip-scale atomic clock.

    Three PSDs of clock error models are listed in TABLE 1, which represent spectrums of the well known TCXO, the CSAC, and a rubidium standard. Phase noise related h0 and h1 coefficients in the CSAC model are assumed to be the same as the TCXO because they can’t be obtained from the Allan deviation figure. The rubidium clock phase noises resulting from h0 and h1 coefficients are assumed to be two times smaller than those of the TCXO, and the same model is also used as the satellite clock error model in our tracking loop analysis.

    TABLE 1. Coefficients of power-law model.
    TABLE 1. Coefficients of power-law model.

    Theoretical Carrier Tracking Loop Performance

    Second- and third-order PLLs are used to study the tracking loop performance. The loop filters for each PLL are given by:
    In-E6(6)

    where F2(s) and  F3(s) are second- and third-order loop filters respectively. Typical coefficients for the second- and third-order loop filters are a2 = 1.414; wo,2 = 4×Bw,2 × a2/[(a2)2+1]; a3 = 1.1; b3 = 2.4; wo,3 = Bw,3/0.7845. Bw,2 and Bw,3 are the second- and third-order tracking loop bandwidths accordingly.

    As stated earlier, three error sources are considered for static receivers. Using the clock error models described earlier, the contribution of different error sources to phase jitter is a function of PLL tracking bandwidth. The resulting phase tracking errors from different error sources are evaluated based on Equation (3) and shown in FIGURE 3.

    FIGURE 3. Phase error contribution from different error sources.
    FIGURE 3. Phase error contribution from different error sources.

    The third-order PLL performance using 2-, 1-, 0.5- and 0.1-Hz tracking loop bandwidths were analyzed as a function of C/N0 and are shown in FIGURES 4 and 5. For each selected bandwidth, three different qualities of receiver clocks were analyzed, and a conventional 15-degree performance threshold was adopted. The second-order PLL performs similarly to the third-order PLL. However, the phase jitter tends to be more biased when the tracking loop bandwidth becomes smaller. This phenomenon will be observed later on using signal data for performance validation. Therefore, only the third-order loop performance analysis is shown in Figures 4 and 5. It is obvious from these two figures that the minimum tracking loop bandwidth for a TCXO receiver PLL is about 2 Hz, and the PLL can work properly only while C/N0 is above 24 dB-Hz.

    FIGURE 4 Tracking loop performance analysis for 2- and 1-Hz loop bandwidth.
    FIGURE 4 Tracking loop performance analysis for 2- and 1-Hz loop bandwidth.
    FIGURE 5. Tracking loop performance analysis for 0.5- and 0.1-Hz loop bandwidth.
    FIGURE 5. Tracking loop performance analysis for 0.5- and 0.1-Hz loop bandwidth.

    As for the receiver using atomic clocks, CSAC and a rubidium frequency standard in our analysis, the PLL bandwidth can be reduced down to at least 0.1 Hz while C/N0 is above 15 dB-Hz.

    Experimental Tracking Loop Performance

    Experimental data were collected at Nottingham Scientific Limited. The experiment was conducted using a GPS/GNSS RF front end with a built-in TCXO clock. The RF front end also has the capability of accepting atomic clock signals through an external clock input connector to which the CSAC (see Photo) was connected during data collection. All data (using the built-in TCXO clock or the CSAC) were sampled at a 26-MHz sampling rate and at a 6.5-MHz IF with 2-MHz front-end bandwidth and four quantization levels.

    A MatLab-coded software defined receiver (SDR) was used to process collected IF samples for tracking loop performance validation. TCXO phase jitters resulting from different tracking loop bandwidths are shown in FIGURE 6 for a typical second-order PLL under a nominal C/N0, which is about 45 dB-Hz. A 45-degree loss-of-lock threshold was adopted (three times larger than the standard deviation threshold used in an earlier performance analysis). In our work, all code tracking delay lock loops (DLLs) are implemented using a second-order loop filter with 20-millisecond coherent integration time and 0.5-Hz loop bandwidth without any aiding. The resulting phase jitters in the figure become biased when the tracking loop bandwidth is reduced. This observed phenomenon implies that a second-order PLL time response cannot track the clock dynamics when the loop bandwidth approaches the minimum loop bandwidth (where loss of lock occurs).

    FIGURE 6. Second-order PLL phase jitter using TCXO.
    FIGURE 6. Second-order PLL phase jitter using TCXO.

    The same IF data was re-processed by the SDR using the third-order PLL with the same range of tracking loop bandwidths. The resulting phase jitters are shown in FIGURES 7 and 8. There is no observable phase jitter bias before the PLLs lose lock in the figures. These results demonstrate that a third-order PLL performs better in terms of capturing the clock dynamics when the tracking loop bandwidth is reduced close to the limitation. Therefore, only the third-order PLL will be considered further.

    FIGURE 7. Third-order PLL phase jitter using TCXO.
    FIGURE 7. Third-order PLL phase jitter using TCXO.
    FIGURE 8. Third-order PLL phase jitter using CSAC.
    FIGURE 8. Third-order PLL phase jitter using CSAC.

    The performance of the TCXO PLL can be evaluated from the results in Figure 7. It demonstrates that the minimum loop bandwidth is 2 Hz, which is consistent with the previous analysis shown in figure 4. However, the minimum bandwidth using the CSAC is shown to be 0.5 Hz in Figure 8. This result does not meet the performance predicted by the analysis, which shows that the working bandwidth can be reduced to 0.1 Hz.

    Analysis and Tracking Performance under PPD Interference

    The motivation of our work, as described earlier, is to improve the receiver signal tracking performance under PPD interference, or equivalently, wideband interference. We carried out a simple analysis first to understand how much signal deterioration a GBAS ground receiver could expect. A 13-dBm/MHz PPD currently available on the market was used to analyze the signal deterioration based on the distance between the PPD and the GBAS ground receiver. A simple analysis using a direct-path model shows that noise power roughly 30 dB higher than the nominal noise level (about -202 dBW/Hz) could be experienced by the GBAS ground receiver if the nearest distance is assumed to be 0.5 kilometers. In this case, any wideband interference mitigation method to address PPD interference has to handle C/N0 as low as 10 to 15 dB-Hz.

    Gaussian distributed white noises were simulated and added on top of the original IF samples, then re-quantized to the original four quantization levels to mimic the PPD interference signal condition. A 20-dB higher noise level was simulated to demonstrate the effectiveness of this signal deterioration technique.

    The tracking loop performance using the third-order PLL under low C/N0 conditions was evaluated using the IF sampling and PPD interference simulation technique just described. The evaluation results show that the minimum PLL bandwidth using the TCXO is still 2 Hz. This result is roughly consistent with a previous analysis showing a 24-dB-Hz C/N0 limitation using 2-Hz tracking bandwidth. The PLL using the CSAC performs better than that using the TCXO, which is expected.

    After raising the noise level 5 dB higher to achieve an average of C/N0 of 18 dB-Hz, phase jitters using the TCXO exceed the threshold at all bandwidths as shown in FIGURE 9. The same magnitude of noise was also added to the CSAC IF samples. The resulting phase jitters are shown in FIGURE 10, which demonstrates that the minimum bandwidth is 1 Hz for this deteriorated signal condition. Any further increase in noise level will result in loss of lock for PLLs using a CSAC at all tracking bandwidths.

    FIGURE 9. Phase jitter using TCXO under 18 dB-Hz C/N0.
    FIGURE 9. Phase jitter using TCXO under 18 dB-Hz C/N0.
    FIGURE 10. Phase jitter using CSAC under 18 dB-Hz C/N0.
    FIGURE 10. Phase jitter using CSAC under 18 dB-Hz C/N0.

    Summary and Future Work

    We explored a baseband approach for an effective wideband interference mitigation method in this article. We have presented the theoretical analysis and actual data validation to study the possible improvement of the PLL tracking performance under PPD interference, which has been experienced by LAAS ground receivers.

    The limitations of reducing PLL tracking loop bandwidths using different qualities of receiver clocks have been analyzed and compared with the experimental results generated by processing IF samples using an SDR. We conclude that the PLL tracking performance using a TCXO is consistent between theoretical prediction and data validation under both nominal and low C/N0 conditions. However, the PLL tracking performance using the CSAC was not as good as the analysis prediction under both conditions.

    In our future work, to understand the reason for the tracking performance inconsistency using the CSAC, we will carefully examine and evaluate the hardware components in line between the external clock input and the IF sampling chip. In this way, we will exclude the clock performance degradation due to any hardware incompatibility.

    Other types of high quality clocks, such as extra-low-phase-noise oven-controlled crystal oscillators and low-phase-noise rubidium oscillators, will also be tested to explore the limitation of PLL tracking bandwidth reduction. If the results using other clocks exhibit good consistency between performance analysis and data validation, it is highly possible that the CSAC clock error model mis-represents the available commercial products.

    In our future work, we will also consider simulating PPD interference more closely to the real scenario, by adding analog interference signals on top of GPS/GNSS analog signals before taking digital IF samples.

    Acknowledgments

    The authors would like to thank the Federal Aviation Administration for supporting the work described in this article. Also, the authors would like to extend their thanks to all members of the Illinois Institute of Technology NavLab and to the collaborators from Nottingham Scientific Limited for their insightful advice. This article is based on the paper “Using a Chip-scale Atomic Clock-Aided GPS Receiver for Broadband Interference Mitigation” presented at ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation held in Nashville, Tennessee, September 16–20, 2013.

    Manufacturers

    The CSAC used in our tests is a Symmetricom Inc., now part of Microsemi Corp. (www.microsemi.com), model SA.45s. We used a Nottingham Scientific Ltd. (www.nsl.eu.com) Stereo GPS/GNSS RF front end with the MatLab-based SoftGNSS 3.0 software from the Danish GPS Center at Aalborg University (gps.aau.dk).


    FANG-CHENG CHAN is a senior research associate in the Navigation Laboratory of the Department of Mechanical and Aerospace Engineering at the Illinois Institute of Technology (IIT) in Chicago. He received his Ph.D in mechanical and aerospace engineering from IIT in 2008. He is currently working on GPS receiver integrity for Local Area Augmentation System (LAAS) ground receivers, researching GPS receiver interference detection and mitigation to prevent unintentional jamming using both baseband and antenna array techniques, and developing navigation and fault detection algorithms with a focus on receiver autonomous integrity monitoring or RAIM.

    MATHIEU JOERGER obtained a master’s in mechatronics from the National Institute of Applied Sciences in Strasbourg, France, in 2002, and M.S. and Ph.D. degrees in mechanical and aerospace engineering from IIT in 2002 and 2009 respectively. He is the 2009 recipient of the Institute of Navigation Bradford Parkinson award, which honors outstanding graduate students in the field of GNSS. He is a research assistant professor at IIT, working on multi-sensor integration, on sequential fault-detection for multi-constellation navigation systems, and on relative and differential RAIM for shipboard landing of military aircraft.

    SAMER KHANAFSEH is a research assistant professor at IIT. He received his M.S. and Ph.D. degrees in aerospace engineering at IIT in 2003 and 2008, respectively. He has been involved in several aviation applications such as autonomous airborne refueling of unmanned air vehicles, autonomous shipboard landing, and ground-based augmentation systems. He was the recipient of the 2011 Institute of Navigation Early Achievement Award for his contributions to the integrity of carrier-phase navigation systems.

    BORIS PERVAN is a professor of mechanical and aerospace engineering at IIT, where he conducts research focused on high-integrity satellite navigation systems. Prof. Pervan received his B.S. from the University of Notre Dame, M.S. from the California Institute of Technology, and Ph.D. from Stanford University.

    ONDREJ JAKUBOV received his M.Sc. in electrical engineering from the Czech Technical University (CTU) in Prague in 2010. He is a postgraduate student in the CTU Department of Radio Engineering and he also works as a navigation engineer for Nottingham Scientific Limited in Nottingham, U.K. His research interests include GNSS signal processing algorithms and receiver architectures.


    FURTHER READING

    • Authors’ Conference Paper

    “Performance Analysis and Experimental Validation of Broadband Interference Mitigation Using an Atomic Clock-Aided GPS Receiver” by F.-C. Chan, S. Khanafseh, M. Joerger, B. Pervan and O. Jakubov in the Proceedings of ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 16–20, 2013, pp. 1371–1379.

    • Chip-Scale Atomic Clocks

    The SA.45s Chip-Scale Atomic Clock–Early Production Statistics” by R. Lutwak in the Proceedings of the 43rd Annual Precise Time and Time Interval (PTTI) Systems and Applications Meeting, Long Beach, California, November 14–17, 2011, pp. 207–219.

    Time for a Better Receiver: Chip-Scale Atomic Frequency References” by J. Kitching in GPS World, Vol. 18, No. 11, November 2007, pp. 52–57.

    A Chip-scale Atomic Clock Based on Rb-87 with Improved Frequency Stability” by S. Knappe, P.D.D. Schwindt, V. Shah, L. Hollberg, J. Kitching, L. Liew, and J. Moreland in Optics Express, Vol. 13, No. 4, 2005, pp. 1249–1253, doi: 10.1364/OPEX.13.001249.

    • Atomic Clocks and GNSS Receivers

    “Three Satellite Navigation in an Urban Canyon Using a Chip-scale Atomic Clock” by R. Ramlall, J. Streter, and J.F. Schnecker in the Proceedings of ION GNSS 2011, the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation, Portland, Oregon, September 20–23, 2011, pp. 2937–2945.

    “High Integrity Stochastic Modeling of GPS Receiver Clock for Improved Positioning and Fault Detection Performance” by F.-C. Chan, M. Joerger, and B. Pervan in the Proceedings of PLANS 2010, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Indian Wells, California, May 4–6, 2010, pp. 1245–1257, doi: 10.1109/PLANS.2010.5507340.

    “Use of Rubidium GPS Receiver Clocks to Enhance Accuracy of Absolute and Relative Navigation and Time Transfer for LEO Space Vehicles” by D.B. Cox in the Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 25–28, 2007, pp. 2442–2447.

    • Clock Stability

    “Signal Tracking,” Chapter 12 in Global Positioning System: Signals, Measurements, and Performance, Revised Second Edition by P. Misra and P. Enge. Published by Ganga-Jamuna Press, Lincoln, Massachusetts, 2011.

    “Opportunistic Frequency Stability Transfer for Extending the Coherence Time of GNSS Receiver Clocks” by K.D Wesson, K.M. Pesyna, Jr., J.A. Bhatti, and T.E. Humphreys in the Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of The Satellite Division of the Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 2937–2945.

    “Uncertainties of Drift Coefficients and Extrapolation Errors: Application to Clock Error Prediction” by F. Vernotte, J. Delporte, M. Brunet, and T. Tournier in Metrologia, Vol. 38, No. 4, 2001, pp. 325–342, doi: 10.1088/0026-1394/38/4/6.

    • Tracking Loop Filters and Inertial Navigation System Integration

    “Kalman Filter Design Strategies for Code Tracking Loop in Ultra-Tight GPS/INS/PL Integration” by D. Li and J. Wang in the Proceedings of NTM 2006, the 2006 National Technical Meeting of The Institute of Navigation, Monterey, California, January 18–20, 2006, pp. 984–992.

    “Satellite Signal Acquisition, Tracking, and Data Demodulation,” Chapter 5 in Understanding GPS: Principles and Applications, Second Edition,           E.D. Kaplan and C.J. Hegarty, Editors. Published by Artech House, Norwood, Massachusetts, 2006.

    “GPS and Inertial Integration”, Chapter 7 in Global Position System: Theory and Applications, Vol. 2, by R.L. Greenspan. Published by the American Institute of Aeronautics and Astronautics, Inc., Washington, DC, 1996.

    • GNSS Jamming

    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 Uninformed RF Interference on GBAS and Potential Mitigations” by S. Pullen, G. Gao, C. Tedeschi, and J. Warburton in the Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 17–21, 2012, pp. 780–789.

    “Survey of In-Car Jammers-Analysis and Modeling of the RF Signals and IF Samples (Suitable for Active Signal Cancelation)” by T. Kraus, R. Bauernfeind, and B. Eissfeller in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation, Portland, Oregon, September 20–23, 2011, pp. 430–435.

     

  • The Business — May 2014

    The Business section from the May 2014 issue. Download the PDF here.

    Includes: KVH Inertial Aboard UAVs via Geodetics, NovAtel; Trimble UX5 Unmanned Craft Captures Images, Maps; Spirent Enables Multi-GNSS Integration in Consumer Devices; MobileMapper 20 Extends GIS in the Field; Indoor Research Employs Spectra Precision Equipment; NovAtel CORRECT with TerraStar PPP Service Now Available; SatNav Can Make European Rail More Affordable; More Airports Across Europe Add EGNOS Approaches; General Dynamics Awarded $26M for GPS III Communications; Braxton LADO System Supports 10th GPS Satellite Initialization; Briefs; Events

  • On the Edge: Mapping from the Air with a UAV

    On the Edge: Mapping from the Air with a UAV

    Dave and Arnold Bansemer prepare the X100 for the survey.
    Dave and Arnold Bansemer prepare the X100 for the survey.

    Surveying an open-pit mine can be a hazardous undertaking. To obtain accurate volume measurements, it is necessary to pick up edges, known in the industry as “toes and crests,” as well as heaps. These are important features, since they provide a way to verify the current shape of a mine; but in light of increasingly stringent safety regulations and penalties, some companies refuse to let the surveyor get too close to such areas. Surveying the site from the air is an effective solution to this challenge.

    It’s also a cost-effective solution. Namibian Mining Survey Services (NMSS) estimates that using an unmanned aerial system (UAS) can save more than 95 percent in mobilization costs, that is, bringing in resources from outside the country to conduct a lidar/photogrammetric survey. Believing UAS to be an important part of the future of surveying, NMSS had been investigating the technology for some time, and a recent project provided the perfect opportunity to try it out.

    NMSS selected the Gatewing X100 for the job based on a demo at a platinum mine, where the results closely tracked those of a previous lidar survey.

    The Project

    The project was to survey a portion of Abenab Mine, a vanadium-lead mine owned by South West Africa Company and located just west of Tsumeb. The mine had been closed in the 1960s, but feasibility studies were underway to see if it would be viable to reopen the operation. Mine management needed to know volumes of all waste and tailings dumps, slimes, dams, and open-pit excavations. The main pit was roughly circular, about 60 meters deep and 120 meters across. Two smaller pits were covered in fairly thick vegetation but had enough ground showing to provide an accurate shape.

    The survey area was approximately 100 hectares. The flying height was set at 150 meters in order to provide a ground separation distance of 5 centimeters. Ground control points (GCPs) were constructed from 1-meter lengths of masonite cut into 10-centimeter-wide strips; painted bright red, the strips were designed to provide 20 x 2 pixel coverage on the images. A total of 10 GCPs were set out in strategic positions covering a wide range of elevations, with points on top of the dumps, on undisturbed ground level, and in the pits. The points were fixed from existing control on the UTM34S coordinate system, by fast static techniques.

    Launching the X100

    The X100 prepares for flight.
    The X100 prepares for flight.

    Based on the Gatewing training received, basic photogrammetry principles and a few trials, NMSS determined that 9 a.m. to 3 p.m. was the best time to fly in order to avoid shadow. The flight area, including a previously surveyed area that would serve as a check, covered 140 hectares. Assuming favorable wind conditions, NMSS expected to cover the area on a single flight.

    Arriving on site at 7 a.m., Dave Bansemer of NMSS started setting out the GCPs while his colleague performed the fast static survey. By 10 a.m., all GCPs had been placed and fixed. Having identified a suitable take-off and landing spot (a farm road), they proceeded through the pre-flight and flight checklist, and then launched the X100 at 11 a.m.. After completing the flight in around 35 minutes, with some turbulence at the 150-meter flying altitude, the X100 landed safely, albeit short of the goal, in an open area.

    Once the data was downloaded, the team returned to Tsumeb to begin the processing. They started with the post-processing of the GCPs, and then moved to the coordinates obtained in the photo-control identification process. NMSS used Gatewing Stretchout Pro software for the photogrammetrical processing.

    After specifying the coordinate system and identifying the GCPs, number-crunching began; the processing ran for around seven hours before the final point cloud and orthomosaics were created. The mean horizontal error was 3 centimeters and the vertical error was 9 centimeters, well within the error budget.

    Results

    Aerial image of the X100 survey.
    Aerial image of the X100 survey.

    The first check was to see if all areas had been covered. NMSS then checked the point cloud against the previous survey. The tie-in was perfect. Some gaps in the point cloud seemed to correspond with tree canopy areas; to ensure complete accuracy, the team resurveyed a few areas using a spatial station.

    NMSS learned some important lessons from using UAV technology for survey, which Bansemer lists for the benefit of future users:

    • Make sure you have enough control. It is sometimes difficult to place your control points exactly in the corners of your flight and one in the center, as the actual flight is influenced by wind direction and the shape of the flight may change accordingly. Put down more points than recommended.
    • Make sure that your ground control point size is relevant to your flying height. You will not be able to identify a 10-centimeter wide strip if you fly at 300 meters.
    • Check the completeness of the job before you leave the area.
    • Make sure there is sufficient area for a safe landing. Bansemer recommends at least a 300-meter strip, taking obstacles into account in the event of a short landing.)

    Manufacturers

    The fast static techniques described were carried out with Trimble R6 GPS systems. Re-survey was done with the Trimble VX spatial station. The Gateway X100 is manufactured by Trimble.

  • The System: GLONASS Fumbles Forward

    The System: GLONASS Fumbles Forward

    GLONASS PLOT from the Roscosmos GLONASS Information-Analytical Centre, showing the 12-hour outage, with full service eventually restored on April 2.
    GLONASS PLOT from the Roscosmos GLONASS Information-Analytical Centre, showing the 12-hour outage, with full service eventually restored on April 2.

    Two April Disruptions Furnish Fodder for Multi-GNSS Receivers and Alternative PNT

    In an unprecedented total disruption of a fully operational GNSS constellation, all satellites in the Russian GLONASS broadcast corrupt information for 11 hours, from just past midnight until noon Russian time (UTC+4) on April 2 (or 5 p.m. on April 1 to 4  a.m. April 2, U.S. Eastern time). This rendered the system completely unusable to all worldwide GLONASS receivers. Full service was subsequently restored.

    “Bad ephemerides were uploaded to satellites. Those bad ephemerides became active at 1:00 a.m. Moscow time,” reported one knowledgeable source. GLONASS navigation messages contain, as they do for every GNSS in orbit, ephemeris data used to calculate the position of each satellite in orbit, and information about the time and status of the entire satellite constellation (almanac); user receivers on the ground processed this data to compute their precise position.

    The GLONASS fix could not take effect until each satellite in turn could be reset, during its pass over control stations in Russian territory, in the Northern Hemisphere, thus taking nearly 12 hours.

    During the outage, CEO Neil Vancans of Altus Positioning Systems reported “We are currently experiencing calls from customers all over the world who are experiencing GLONASS ‘outages’ and we have advised customers to switch GLONASS tracking off on our receivers.”

    Such a — possibly human, possibly computer-generated — error could conceivably occur with GPS, Galileo, or BeiDou. “Another reason to have backups,” mused Richard Langley of the University of New Brunswick. “And not just other GNSS.”

    Trouble Chronolog. The constellation suffered a second failure two weeks later. On April 14, eight GLONASS satellites were simultaneously set unhealthy for about half an hour, meaning that most GLONASS or multi-constellation receivers would have ignored those satellites in positioning computations. In addition, one other satellite in the fleet was out of commission undergoing maintenance. This might have left too few healthy satellites to compute GLONASS-only receiver positions in some locations.

    The two blackouts followed two other high-profile disasters: the destruction-upon-launch of three new GLONASS satellites in July 2013, and the Pacific drowning-upon-launch of three satellites in December 2010.

    Internal Dialog. The semi-official Russian news daily Izvestia (“Truth”) reported that the loss of service was inconsequential for Russian users. Loose translation courtesy of Google:

    “Temporary GLONASS failure has not led to tangible consequences for consumers of services because chip manufacturing exclusively with GLONASS for the mass market is practically nil: there are chips that work only with the GPS signals, and there are those that see both GPS and GLONASS.”

    In other words, there are practically no mass-market devices, even in Russia, that use exclusively GLONASS.

    “In any case, the failure of the entire system for a long period is a serious blow to the image of GLONASS, especially in a situation where Russia has made efforts to promote domestic navigation system to external markets. Plus in 2012, the Russian government officially promised to maintain the characteristics of the international community GLONASS at the proper level for 15 years.”

    Industry View, Multi-GNSS. During the first outage, chip company Broadcom was conicidentally conducting multi-constellation receiver tests in Asia. Frank van Diggelen, the company’s chief GNSS scientist, stated, “We have definitive data to show how a multi-constellation receiver survives such an outage. Test data coincident with the GLONASS ephemeris disruption show how a GPS/GLONASS/QZSS/BeiDou receiver survives the complete disruption of one of the constellations.”

    A Broadcom 47531receiver tracking GPS/GLONASS/QZSS/BeiDou signals simultaneously and using logic to analyze redundant measurements to check the validity of all measurements successfully identified and removed the bad GLONASS ephemeris, maintaining position continuity and accuracy. Another receiver under test at the same time, tracking only GPS and GLONASS, wandered significantly in its position reports.

    Industry View, Back Up PNT. Calling it an “unprecedented and deeply worrying total disruption…[that] shook the industry,” Locata Corporation reiterated its call for redundant terrestrial systems to back up GNSS in the wake of the outage.

    Nunzio Gambale, Locata CEO, said “We have been telling the industry for years that you cannot have a critically important capability like GPS without also having a backup! What is Plan B if the satellite systems fail? What replaces the space signal when there is a problem? This event should terrify every nation, government, and company that depends on navigation satellites for their business or, in some cases, their very lives.”

    GNSS navigation and timing functions underpin the world’s banking systems, stock exchanges, digital TV and Internet, cell-phone networks, and, in some cases, the national electricity supply, Locata pointed out. GPS, in particular, plays a crucial role in transportation, shipping, and logistics, serving as the enabling technology for critical functions like air traffic control. Reliability is therefore not just important; it is essential across all applications.

    “We ignore the possibility of these ‘Black Swan’ events at our own peril,” added Chris Rizos of the University of New South Wales.

    eLoran Authorization in Progress

    Russia’s April 1 GLONASS blackout occurred, ironically, only hours after the U.S. House of Representatives passed legislation to preserve infrastructure that could support a backup system for GPS that could be used for critical infrastructure and applications in the event of a similar disaster occurring in the United States.

    The 2014 Coast Guard Authorization Act requires the Department of Homeland Security (DHS) to halt dismantling and disposal of infrastructure that could be used for a terrestrial system during times and in places where GPS is not available.

    DHS had announced in 2008 that it would build such a backup system, but it never did so, and actually began dismantling, destroying, and divesting itself of Loran equipment and properties. The equipment, facilities, and sites could be used to implement a new generation eLoran system for GPS backup, among other applications. Despite strong recommendations to the contrary by its own panel of experts, the Obama administration, DHS, and the Coast Guard moved in 2009 to kill the Loran program.

    Watchdogs. Congress has lately become more visibly concerned about the vulnerability of the nation’s space systems. The 2014 National Defense Authorization Act tasked the administration with reporting on how it was going to provide necessary national security capabilities when space systems were disrupted. More recently, Congressmen Duncan Hunter (Republican, California), chair of the House Coast Guard and Marine Transportation Subcommittee, held a hearing at which he expressed his concern that the nation has no backup for GPS. He also expressed his frustration with the Department of Homeland Security, reporting that “They said they need to do a study about their study.”

    Congressman John Garamendi (Democrat, California), commented “GPS will go down one day. The question is, is there a backup?”

    The legislation passed by the House authorizes DHS to partner with public or private entities to build a system that would not only back up GPS, but also work indoors, underground and underwater — all characteristics of long-wave Loran technology.

    Resilient PNT. Dana Goward, president of the Resilient Navigation and Timing Foundation, said such a project would be relatively inexpensive. “If the existing equipment and infrastructure are preserved and reused, the system could be restored and put into operation for less than half the cost to dispose of it.”

    “It isn’t an issue of money,” Goward continued. “It is a question of the government taking this problem seriously and acting on it.”

    The foundation has as offered to partner with the government to build the system.

    “Our government has known about this issue for a long time,” Goward said. “At least since 2001. And there has been a standing presidential direction to obtain backup capability since 2004. But for some reason, it hasn’t yet happened.”

    The government’s official website about GPS (www.gps.gov) has recently updated its page on eLoran and Loran-C with a tracking log for Coast Guard and Maritime Transportation Act of 2014, which now goes to the Senate.

    IRNSS’s Second of Seven

    India’s Space Research Organisation launched a navigation satellite on April 4. IRNSS-1B is the second of seven that will comprise the first-generation Indian Regional Navigation Satellite System (IRNSS). It joins IRNSS-1A, already in orbit.

    IRNSS will consist of three geostationary satellites and two pairs in inclined geosynchronous orbits. Each IRNSS satellite uses a rubidium-based atomic clock to keep time, transmitting signals on L and S-band frequencies at 1176.45 and 2492.028 megahertz respectively.

    Lag in Recent GPS IIF’s Health Status

    By Richard Langley

    The GPS Block IIF satellite, IIF-5 or SVN64 (PRN30), launched on February 21, had not as of press time been set healthy. Typically, GPS satellites are checked out and made operational within about a month after launch.

    The delay is due to an extended navigation test being performed by the GPS master control station. A navigation upload for SVN64 was performed in March with ephemeris and clock data as usual streching weeks in advance. However, unlike with operational satellites, no further updated uploads have been performed. The aging ephermis and clock data gradually becomes less and less accurate as time goes by, but should degrade gracefully.

    Some observers will have noticed that the received navigation data from SNV64 changes infrequently. Currently, the navigation data changes once per day with an epoch of 13:00 GPS Time, unlike every two hours with operational satellites. And the data fit interval is 26 hours, compared to four hours.

    The test is scheduled to run until mid-May.

     

  • Raven Industries Acquires SBG Innovatie BV and Navtronics BVBA

    Raven Industries, Inc. announces that its Applied Technology Division (ATD) has acquired SBG Innovatie BV and its affiliate, Navtronics BVBA. Headquartered in Middenmeer, Netherlands, SBG manufactures advanced GPS steering systems for a variety of agricultural applications. The acquisition broadens Raven’s guided steering system product line by adding high-accuracy implement steering applications. Additionally, SBG’s headquarters will become the new home office for Raven in Europe, expanding the company’s global presence and reach into key European markets. The purchase is not expected to have a material impact on Raven’s fiscal 2015 results.

    “SBG specializes in very precise, real time kinematic, or RTK, GPS steering systems with a focus on high-value crops. Their highly accurate implement steering technology broadens Raven’s existing product line and integrates well into the Raven product portfolio.” said Matt Burkhart, ATD’s Division Vice President and General Manager. “We are proud to welcome the SBG organization into the Raven family. Our innovative cultures align very well, and SBG’s leading technology and strong team members will be a great compliment to further Raven’s position as a leader in the precision ag market.”

    “Our priorities within ATD are to drive growth through international market expansion, new products and broadening OEM relationships,” said Daniel A. Rykhus, Raven’s president and chief executive officer. “We believe SBG can help further these strategies and position us for success in new markets.”

    “We’re excited to join Raven so that, together, we can expand Raven’s footprint in key geographies and augment their expertise and product line,” said Rik van Bruggen, managing director of SBG.  “In turn, Raven’s scale and resources will allow SBG to realize our dream of growing the business and helping customers increase yields and efficiencies. Raven is a good partner for us because they are committed to increasing their presence in Europe and providing additional opportunities for our team.”

    Effective immediately, SBG’s products will be offered as a part of Raven’s lineup of precision ag products. Sales team members from both companies will be offering the combined product lines.

  • Trimble Irrigate-IQ Solution Now Available in North America

    Trimble is making available the Trimble Irrigate-IQ precision irrigation solution in North America. Along with the North American launch, Trimble also introduced the Connected Farm Irrigate app, which provides farmers with real-time status and control of their pivot irrigation systems using a smartphone or tablet.

    The Irrigate-IQ GPS-controlled solution, which is installed on the pivot, enables farmers to remotely control their irrigators via the Internet, including performing variable rate irrigation, and receive reports about where water or fertilizer has been applied. With the solution, farmers can apply the optimal amount of water, fertigation or effluent where needed, which can improve crop quality and yield, while minimizing nutrient and chemical runoff. The solution enables farmers to conserve water use and improve efficiency, reduce energy costs for fuel and electricity, minimize input costs, comply with environmental regulations, and safely dispose of effluent. In addition, Trimble’s brand-agnostic strategy allows farmers to use the solution with most irrigator makes and models. Irrigate-IQ is also available in New Zealand.

    In addition, Trimble introduced the Connected Farm Irrigate app for use on an iPhone, iPad, Android smartphone or tablet. The app allows farmers to see the status of their pivots, including whether they are operating or not operating, in which direction they are traveling, the heading, pump pressure, pivot voltage and type of material being dispersed (water, fertigation, or effluent). It also gives farmers the ability to remotely start or stop their pivots, choose the direction (forward or reverse), turn the pump on or off or switch the type of material being dispersed. This new functionality comes in addition to farmers’ ability to remotely control their irrigators by accessing the Irrigate-IQ software on a desktop or laptop computer, rugged mobile computer or tablet.

    “Now that Trimble has expanded availability of its Irrigate-IQ solution, and launched the Connected Farm Irrigate app, farmers across North America and New Zealand will be able to monitor and control their irrigators from virtually any location,” said David Fitzpatrick, business area director of Trimble’s Agriculture Division. “Irrigate-IQ allows farmers to be more strategic with their irrigation planning, while the app creates time savings and increased efficiencies by allowing farmers to respond to weather changes or faulty equipment on the fly without being on site.”

    The Irrigate-IQ solution and Connected Farm Irrigate app are both part of Trimble’s Connected Farm solution, which includes a robust suite of recently announced features including soil analysis, rainfall totals, weather forecasts, commodity tracking, and now irrigation monitoring and control.

  • Tracker for Children, Pets Integrates u-blox GNSS, Cellular Technologies

    Tracker for Children, Pets Integrates u-blox GNSS, Cellular Technologies

    The Trax personal tracker for children and pets uses a u-blox receiver.
    The Trax personal tracker for children and pets uses a u-blox receiver.

    Swedish WTS (Wonder Technology Solutions) has launched Trax, a personal tracking device for children and pets. Based on a u-blox GNSS receiver module with integrated antenna and cellular module, the tiny tracker can be located anywhere, anytime via a free Android or iPhone mobile phone app.

    In addition to real time tracking, Trax provides flexible geofence alerts, and can monitor how fast your teenager is driving. It also works indoors, thanks to a proprietary dead-reckoning algorithm that delivers a position even when satellites are out of sight. Accurate down to 1.5 meters, the robust, water resistant device also provides an “augmented reality” mode that helps users locate their trackers using a smartphone’s built-in camera view.

    To achieve the smallest possible size, Trax uses a u-blox’ CAM-M8Q GNSS receiver module with a built-in antenna. CAM-M8Q (chip antenna module) provides both small size (9.6 x 14.0 x 1.95 mm) and multi-GNSS capability. It is based on a u-blox M8 chip and includes an integrated chip antenna plus SAW filter, LNA, TCXO, RTC crystal and passives. The surface-mount module is also extremely low in height making very thin customer designs possible.

    “Trax is the world’s smallest and most versatile personal tracking device available, packed with features designed to provide peace of mind to parents and pet owners almost anywhere in the world,” said Fredrik Danelius, Managing Director at WTS, “By combining the leading GNSS and cellular technologies from u blox, we have designed a tiny, reliable, low-cost device that protects our most valuable family members: children and pets.”

    Trax comes with an integrated SIM-card and two years of free data and roaming in 33 countries. It is charged via USB and typically lasts between two and four days on a full battery. For wireless connectivity, device integrates a u blox SARA-G3 GSM/GPRS module which is footprint compatible with the SARA-U2 UMTS/HSPA module for easy 2G to 3G upgrade.

    “Trax is an elegant and sophisticated example of our embedded GNSS and cellular modules combined to protect people’s loved ones,” said Pasi Alajoki, Area Sales Manager at u-blox, “It is an extremely important application of our mobile communications and global positioning technology where performance, size and power consumption play a critical role. We are proud WTS chose u-blox for Trax.”

  • Trimble and Infinicon Offer Surface Methane Monitoring Solution

    Trimble and Infinicon, Inc., are offering a portable Infinicon DataFID Flame Ionization Detector and Trimble SEMonitor software as a solution for surface methane monitoring of landfills. The landfill gas solution provides a more streamlined workflow for the environmental services professional, improving efficiency and reducing rework.

    The announcement was made at WasteExpo 2014, a North American solid waste and recycling tradeshow.

    In the past, field technicians had to carry notebooks, maps, plans and the sensor to locate, mitigate and audit trouble points on site. The Trimble SEMonitor and Infinicon DataFID solution combine to eliminate the need for pen and paper, increase communication capabilities in the field and improve work flow demands for field technicians, maximizing productivity and streamlining the workflow.

    By leveraging Trimble’s innovation and expertise in geospatial software solutions with Infinicon’s proficiency in portable, intrinsic safe detection equipment, environmental professionals can see increased efficiency in conducting their landfill methane surface emission monitoring, analysis and reporting.

    “Collaborating with Infinicon allows us to offer a more robust software solution while simultaneously addressing the entire environmental workflow from data collection through analysis for compliance and operational optimization,” said John Rice, general manager of Trimble’s Environmental Solutions Division. “Together, we offer the environmental industry a complete end-to-end solution for landfill methane monitoring.”

    “The Trimble SEMonitor adds a unique dimension to traditional monitoring practices by streamlining surface landfill gas data collection, analysis and compliance reporting. Communicating via the Bluetooth connection, the hardware and software solutions combine to improve efficiency, accuracy and effectiveness of surface methane monitoring,” said ChingYue Yeung, product manager for the INFICON DataFID.

  • DeCarta Search Engine for LBS Expands to 120 Countries

    deCarta, Inc., an independent LBS platform company, has expanded coverage of its advanced local search technology, the L2 Geospatial Search Engine, to 120 countries including Europe, North America, and most major countries around the world.

    L2 is a high-performance, scalable local search engine with single line input to enable a more intuitive user interface, the company said. deCarta sources and indexes premium map and POI (Points of Interest) content but also enables customers to index and control their own content using the L2 Index tools.

    deCarta’s L2 has advantages over most other search engines in that it can be used as a pure geocoder for address search, or for POI search….or simultaneously as a combination of the two mixed in a single line search query – with the additional ability to tune this behavior at runtime. This gives developers maximum flexibility and creativity in producing their mobile and desktop applications. The new expanded country coverage now enables deCarta customers to offer truly global services.

    The L2 Search engine is an integral component of deCarta’s LBS platform which provides specialized geospatial technologies for maps, routing, navigation, geocoding, local search and geo-data integration and processing. deCarta offers two deployment models for its LBS platform: a Hosted LBS Platform Service (PaaS) or, alternatively, customers can self-host the platform either on-premise or in a cloud service such as Amazon’s AWS. Both approaches utilize deCarta’s advanced REST API architecture and can scale to support billions of maps and searches and millions of users per month.

    L2 enables deCarta’s customers to offer flexible, advanced local search capabilities that are on par with Google Maps but beyond other search engines, deCarta said. Examples include:

    • Single line entry of POI or address or both
    • Fast typeahead, predictive entry – ideal for mobile devices and web interfaces
    • High tolerance for misspellings and partial entries
    • Random ordering of address parameters
    • Search for a POI near a POI, such as:
      • “Coffee near XYZ company”
      • “Restaurants on Main Street”
      • “ATMs near AMC Theater”
    • Search for POI near a specific address, such as “Parking near 1234 Main Street”

    Furthermore, the ability to integrate L2 with deCarta’s patented “Search Along A Route” technology gives automotive OEMs and Telematics Service Providers the ability to offer more advanced and helpful “driver-centric” connected car services.

    “We are excited by the market reaction to L2 since its introduction last year,” said J. Kim Fennell, CEO of deCarta. “We’re winning business competing with, and in some cases replacing, major local search engines such as Google Maps based on the merits of L2’s technology advantages, customization capabilities, flexible content offerings, less restrictive license terms and our superior customer service – all of which creates a more satisfied customer experience.”

    deCarta offers a “house blend” of premium map and POI content with L2. It works closely with worldwide and regional map data providers including TomTom, Nokia/HERE, OpenStreetMap (OSM), AND, Sensis, IPC, Nav2 and eMapgo; as well as leading POI providers and other content sources (traffic, parking, weather, speed cameras, etc). deCarta integrates and de-duplicates multiple content sources for optimum search results.

    deCarta provides the tools to let companies index and search on their own content for maximum control and commercial advantage. This content can stand alone or be merged with industry map and POI content. Customers can “boost” content and control rankings to suit their needs. These capabilities provide huge benefits for local search companies, Automotive OEMs and telematics service providers seeking to offer their users the best customer care and connected car services.

    For more information on L2, please visit deCarta’s web site at www.decarta.com or go straight to the demo. Developers can find more technical details at deCarta’s DevZone.

  • deCarta Expands L2 Geospatial Search to 120 Countries

    deCarta, Inc., an independent LBS platform company, has expanded coverage of its advanced local search technology, the L2 Geospatial Search Engine, to 120 countries including Europe, North America, and most major countries around the world.

    L2 is a high-performance, scalable local search engine with single line input to enable a more intuitive user interface, the company said. deCarta sources and indexes premium map and POI (Points of Interest) content but also enables customers to  index and control their own content using the L2 Index tools.

    deCarta’s L2  has advantages over most other search engines in that it can be used as a pure geocoder for address search, or for POI search….or simultaneously as a combination of the two mixed in a single line search query – with the additional ability to tune this behavior at runtime. This gives developers maximum flexibility and creativity in producing their mobile and desktop applications. The new expanded country coverage now enables deCarta customers to offer truly global services.

    The L2 Search engine is an integral component of deCarta’s LBS platform which provides specialized geospatial technologies for maps, routing, navigation, geocoding, local search and geo-data integration and processing. deCarta offers two deployment models for its LBS platform: a Hosted LBS Platform Service (PaaS) or, alternatively, customers can self-host the platform either on-premise or in a cloud service such as Amazon’s AWS.  Both approaches utilize deCarta’s advanced REST API architecture and can scale to support billions of maps and searches and millions of users per month.

    L2 enables deCarta’s customers to offer flexible, advanced local search capabilities that are on par with Google Maps but beyond other search engines, deCarta said. Examples include:

    • Single line entry of POI or address or both
    • Fast typeahead, predictive entry – ideal for mobile devices and web interfaces
    • High tolerance for misspellings and partial entries
    • Random ordering of address parameters
    • Search for a POI near a POI, such as:
      • “Coffee near XYZ company”
      • “Restaurants on Main Street”
      • “ATMs near AMC Theater”
    • Search for POI near a specific address, such as “Parking near 1234 Main Street”

    Furthermore, the ability to integrate L2 with deCarta’s patented “Search Along A Route” technology gives automotive OEMs and Telematics Service Providers the ability to offer more advanced and helpful “driver-centric” connected car services.

    “We are excited by the market reaction to L2 since its introduction last year,” said J. Kim Fennell, CEO of deCarta. “We’re winning business competing with, and in some cases replacing, major local search engines such as Google Maps based on the merits of L2’s technology advantages, customization capabilities, flexible content offerings, less restrictive license terms and our superior customer service – all of which creates a more satisfied customer experience.”

    deCarta offers a “house blend” of premium map and POI content with L2. It works closely with worldwide and regional map data providers including TomTom, Nokia/HERE, OpenStreetMap (OSM), AND, Sensis, IPC, Nav2 and eMapgo; as well as leading POI providers and other content sources (traffic, parking, weather, speed cameras, etc). deCarta integrates and de-duplicates multiple content sources for optimum search results.

    deCarta provides the tools to let companies index and search on their own content for maximum control and commercial advantage. This content can stand alone or be merged with industry map and POI content. Customers can “boost” content and control rankings to suit their needs. These capabilities provide huge benefits for local search companies, Automotive OEMs and telematics service providers seeking to offer their users the best customer care and connected car services.

    For more information on L2, please visit deCarta’s web site at www.decarta.com or go straight to the demo. Developers can find more technical details at deCarta’s DevZone.

  • Expert Advice: The Range of UAVs Across Civil Applications

    Expert Advice: The Range of UAVs Across Civil Applications

    Peter Cosyn, Trimble
    Peter Cosyn, Trimble

    By Peter Cosyn, Trimble

    Unmanned aerial vehicles (UAVs), or as most civil aviation authorities now call them, unmanned aircraft systems (UASs), are attracting a lot of attention lately from geospatial professionals. Common questions in their minds are:

    • What applications can I use it in?
    • What benefits can it provide to my organization or my clients (or data users)?
    • How do I implement such a system in my organization?

    This article will cover the first two questions, while addressing some of the third as well.

    High-Level System

    Unmanned aircraft are either a fixed-wing (plane) or a multi-rotor (helicopter) design. Typical fixed-wing UAS available today are equipped with wide-angle cameras that fly about 100 meters, more or less, above the ground. Multi-rotors, with their ability to hover, move vertically — and even fly in reverse — may sometimes be operated at lower heights above ground. A greater diversity of sensors are being developed and offered specifically for small UAS platforms. Some of these include near-infrared cameras, miniaturized laser imaging detection and ranging (LiDAR) scanners, and even sensors that enable hyper-spectral or multi-spectral capabilities. The typical system runs on electrical power, and flights last between 30 and 60 minutes, often less for multi-rotors because of the greater amount of energy needed to achieve a mission. Depending on the endurance and speed of fixed-wing aircraft, typical coverage is around 1 to 1.5 square kilometers (100–150 hectares). For multi-rotors the area covered is much less; it could be as little as 10 percent to as much as 30 percent of what can be achieved with a fixed-wing UAS.

    UAS image-processing is usually done using close-range photogrammetric techniques adapted to exposures taken in flight. This allows accurate construction of photogrammetric models that approach the quality achievable with much more sophisticated manned aerial systems flying at much higher altitudes.

    With these technologies, photomosaic, orthophotographs, digital terrain models (DTMs), digital surface models (DSMs), and point clouds can be output. Without ground control, the models have decimeter-level internal consistency in X, Y, and Z. With much sparser ground control than is typically required for conventional photogrammetry, good-quality models with centimeter-level accuracy registered to the ground control can be rapidly generated at much lower costs than most other methods of achieving similar results. That, however, doesn’t make today’s UASs a solution for all aerial surveying and mapping situations; but where their application is appropriate, they bring benefits that are sometimes unique.

    Some of the more common applications of UAS-based mapping appear in the two-part table here, with a limited set of users and data consumers for each type, and special benefits that may be unique to UAS aerial imaging.

     

     

    Superior Adaptability. UAS aerial imaging can provide flexibility unsurpassed by other technologies. Portable equipment that can function in a wider variety of adverse weather means that mapping can be done closer to the time of need. Because mobilization and flight cycles are short, flights can be done hourly or more frequently in urgent situations such as floodwater or fire tracking. Cloud cover is rarely a problem as unmanned aircraft typically fly below the clouds.

    In fact, in some parts of the world it is being considered as the only mapping tool for aerial mapping as the weather, availability of aircraft, other equipment and trained personnel rarely coincide to allow opportunities for conventional aerial mapping. When focused areas need to be mapped with timely generation of data products under conditions — weather, hazard limitations, or closely spaced visitations — that test the capabilities of other tools, the selection and successful use of UAS in such situations is only limited by the solution-provider’s creativity.

    Regulatory Framework. Operational issues and working within a nation’s civil aviation regulatory framework must be examined in detail before an organization decides to acquire and fly UAS for geospatial applications. UAS flying is highly process-oriented. It involves much more planning and preparation than the typical use of ground-based technologies involves. Training of flight crews and data processing teams is more than just an up-front investment. It is necessary for flight crews to maintain current skill levels through non-revenue flights if the revenue flight schedule is widely spaced in time.

    The state of regulations vary from country to country, but fliers in any locality must also be aware of the restrictions on flying in the national airspace that may have been imposed by the civil aviation authority that covers sub-sections of the airspace or that restrict how or where an UAS may be flown. This includes restrictions on flights near airports and aircraft routes, flights over populated or urban areas and maximum and minimum flying heights over ground level. A common limitation is to restrict flights to areas that are within visible line-of-sight of the UAS pilot.

    UAS are not a panacea for all mapping problems. Satellites, high-altitude photogrammetry, fixed-ground, mobile terrestrial and manned aircraft LiDAR, and ground-based techniques all have their place, especially when large areas are to be mapped at widely spaced time intervals. But geospatial data managers will be surprised to see how nagging problems — as well as some they didn’t recognize as problems — can be solved with UAS-based mapping.


    Peter Cosyn is site manager and director of research and development of Gatewing, a Trimble company. He is a co-founder of Gatewing, which was launched in 2008. Cosyn earned a Ph.D. in electromechanical engineering from Ghent University. He has more than 10 years of experience in unmanned aircraft system design.