The Federal Aviation Administration (FAA) has awarded Raytheon Company a two-year contract extension to continue to provide services for the Wide Area Augmentation System (WAAS), a safety system that provides satellite-based navigation in the continental United States, Alaska, Canada and Mexico. The $30.1 million contract extends the period of performance through Sept. 24, 2013.
“Raytheon has been the FAA’s prime contractor for WAAS since the system was commissioned for operational use in the United States in 2003,” said Michael Prout, vice president of Security and Transportation Systems for Raytheon’s Network Centric Systems business. “The contract marks another milestone in the continuing partnership between Raytheon and the FAA to improve safety and efficiency for pilots.”
According to the announcement, Raytheon will provide life-cycle support and other services to improve service reliability and availability, and increase the coverage area through system enhancements. WAAS enables GPS to meet air navigation performance and safety requirements for en route, terminal, non-precision approach, and approach with vertical guidance operations.
Accord Technology LLC was recently authorized TSO-C145c for its latest receiver/sensor in the NextNav product family, the NextNav MAX GPS WAAS Class Beta-1, -2, -3. This successful GPS development is a key solution in a series of Accord Technology’s affordable civil aviation GPS receivers and sensor, the company said.
Available as circuit card receivers (CCA) for avionics OEM hosting or as line replaceable sensor units (LRU) for aircraft installations, MAX is affordable and meets the latest standards, worldwide. It supports ADS-B (Automatic Dependent Surveillance-broadcast), all normal GPS procedures, as well as precision approach requirements such as LPV (Localizer Performance with Vertical Guidance) and RNP AR (Required Navigation Performance with Authorization Required).
The NextNav MAX GPS technology is the most advanced in the world and is compatible with Satellite Based Augmentation System (SBAS) solutions such as the United States’ WAAS, the European EGNOS, Japan’s MTSAT, and GAGAN in India.
“This TSO authorization for the NextNav MAX gives us greater flexibility to serve our customers with WAAS Beta 1 Only or Beta 1, 2, 3 LRU’s and CCA’s to fit their need,” commented Hal Adams, chief operating officer for Accord Technology. “The NextNav MAX is another important first for Accord Technology and we are anxious to move forward now with our AC 20-165 approved GPS sensor,” added Adams.
Accord Technology received TSO-C145c for its NextNav mini in 2010. The NexNav mini GPS technology was the first GPS WAAS sensor to be authorized by the Federal Aviation Administration TSO-C145c WAAS Class Beta-1 Only. The NexNav mini solution is a market-breaking hybrid of automotive technology and aviation requirements providing an affordable solution for ADS-B GPS source.
NextNav MAX’s DO-229D compliant aerospace GPS-SBAS receiver is certified by the FAA for TSO-C145c Class Beta-3 operation and is the enabling technology for several key applications, including:
Primary means of navigation
Localizer Precision with Vertical guidance approaches (LPV)
Airborne spacing assurance
Airborne Collision Avoidance (Non-TCAS System)
Constant descent approaches
Surface area movement management
Current and advanced Terrain Avoidance Warning System (TAWS)
“Whether it’s a need for LP/LPV approach precision or highly reliable PVT, NextNav MAX offers standard and custom solutions according to what our customers need,” Adams said. Designed around a small form-factor, the NextNav MAX CCA is delivered ready to integrate into host avionics systems, an LRU sensor or as a standalone module to ARINC 743 requirements. “We can even package the CCA in a module, tailored to your application,” Adams said.
By Hans-Georg Büsing, Ulrich Haak, and Peter Hecker
Future safety-relevant driver assistant systems demand vehicle state estimations accurate enough to match the position within a road lane, which cannot be provided by standalone GPS. A promising approach to meet the requirements is the fusion of standalone or differential GNSS measurements with vehicle sensor data like odometers or accelerometers. To achieve deeper sensor integration, a software GNSS receiver was developed at the Institute of Flight Guidance (IFF) that is able to use dead reckoning sensors to support its signal acquisition. This article presents an approach to estimate the signal states during outages based on the tightly coupled vehicle state, which reduces the reacquisition time and significantly increases the signal availability.
GNSS-based navigation is a key enabler for future advanced driver assistance systems (ADAS). Car manufacturers have identified automotive assistance systems as core devices to propose their uniqueness mainly in the luxury and upper-class market segments. While the precision and availability of loosely coupled single-frequency GPS navigation satisfies the requirements of typical route guidance systems, future automotive systems — especially those that enhance driving safety — are more demanding on positioning system performance.
The Institute of Flight Guidance (IFF) of the Technische Universität, Braunschweig, Germany, is involved in two research projects evaluating the performance of unaided traditional GNSS receivers coupled with vehicle sensor measurements such as odometers in a tightly coupled architecture. Besides these involvements, the IFF has developed a general-purpose software-based GNSS receiver allowing full access to signal processing routines.
The benefits of the tight sensor fusion are reliable state estimations even during total signal outages that are common in the automotive sector due to tunnels, parking decks, or urban canyons. In this architecture, the GNSS receiver works autonomously to deliver raw GNSS-measurements only. Additional knowledge provided by the vehicle sensors cannot be used to support the receiver in any way. Besides other beneficial aspects in the tracking channels, additional external knowledge about the vehicle state has the potential to reduce acquisition times and improve the measurement availability significantly.
The Institute of Flight Guidance uses a software environment called “Automotive Data and Time-Triggered Framework” (ADTF) for research in the field of ADAS and automotive navigation. In this software framework, the overall system architecture is assembled with independent modules. These modules are implemented as libraries and loaded into ADTF. Data is exchanged via pins that are defined as public variables. The framework also attaches timestamps to the individual measurements and adds a data recording and playback functionality.
From a general-purpose software GNSS receiver, presented at the ION GNSS 2010, we have derived an automotive-specific ADTF software receiver module. The software framework adds the flexibility to synchronously process measurements from vehicle sensors additionally to the IF data from the front end. This gives us the opportunity to aid signal processing in the software GNSS receiver with additional external sensors.
For positioning, a tightly coupled positioning filter based on GPS raw data measurements and the rear-wheel odometers is implemented. The vehicle’s motion is modeled using a kinematic relationship between the vehicle sensors and the GNSS measurements.
Based on the tightly coupled vehicle state estimation, an acquisition state is processed during signal outages that enables the software GNSS receiver to reacquire the satellite signal instantaneously with high precision.
In this article, the constituent parts of the system are presented and the estimation of the acquisition state derived. The system was tested in an urban scenario, and the state estimations validated with the recorded measurements.
System Architecture
The software-defined GNSS receiver developed by the IFF was designed to process the computationally expensive signal correlation on an Nvidia graphics board using the vast parallel processing capability of graphics processing units (GPUs). With the use of common graphics boards, an entire receiver can be implemented on an ordinary PC, needing only a front-end to receive digital GNSS signals in an intermediate frequency (IF) band.
For research in the field of vehicle state estimation, a derivate of the software receiver of the Institute of Flight Guidance has been implemented in the “Automotive Data and Time-Triggered Framework” (ADTF). The software is commonly used in the automotive industry for the development of ADAS. Figure 1 shows a typical system layout in ADTF. A central component of the framework is the ability to record and play back measurement data, which is indicated by the buttons on the left of the screenshot.
Figure 1. System Architecture in ADTF. (Click to enlarge.)
Within ADTF, the systems are assembled from modules that are shown as blocks within the graphical configuration editor. Standard modules such as the connection of common hardware are provided with the framework. Custom modules can be implemented in C++ by the user. Every module is implemented as a dynamic library (DLL) and interpreted by the framework. Modules can be featured with input and output pins.
These pins are implemented by using specific data types from the framework. The communication and data exchange between the modules is handled via these pins. They can be connected by graphically drawing connector lines in the configuration editor.
ADTF provides the user with classes for timing and threading. Processes can thereby be linked to the ADTF system time, which is especially important as the data replay can be slowed down or sped up for debugging.
The instantaneous reacquisition algorithm is based on a traditional approach of tightly coupling GNSS raw data with vehicle sensor measurements. The fusion is based on a kinematic model following the Ackermann geometry establishing the relationship between the vehicle’s motion and the respective measurements.
At each time step of an arriving measurement, the vehicle’s motion is predicted based on the last estimated state with an extended Kalman filter. The prediction is then corrected using either measurements from the vehicle sensors or GNSS raw measurements. The range and Doppler measurements are calculated in the tracking channels of the ADTF software GNSS receiver. The corrected vehicle state is then fed back into the kinematic model for the next update cycle.
In case the GNSS signal is lost in a tracking channel, a virtual tracking channel is initialized with the last calculated channel states. The change in the channel output is then predicted utilizing the change in the vehicle state and the current evaluation of the ephemeris. The schematic implementation of the channel state prediction is shown in Figure 2.
Figure 2. Schematic of Channel State Prediction. (Click to enlarge.)
Signal State Estimation
Using the tightly coupled architecture presented above, an estimated position and velocity can even be provided during total signal outages. Assuming that the last valid observation of a satellite signal is stored together with its respective time to and position, an estimation of the signal state (that is, Doppler frequency, code- and carrier-phase) based on the estimation of the vehicle state during the signal outage at time t1 can be used for an instantaneous signal reacquisition. Using the ephemeris data provided by the respective GPS satellite the range between a user position xu and the satellite xsv can be calculated using the following terms (1)
and (2)
with |…| indicating the Euclidian distance.
Therefore the change of the range can be obtained with equations (1) and (2): (3)
Assuming an unbiased Gaussian error distribution of the measurements, the tightly coupled system provides an estimation of the covariance matrix of the vehicle state. Using only the submatrix (4)
related to the vehicle position, the covariance of the user position along the line-of-sight to the satellite can be obtained with the Euclidean norm of the line-of-sight vector (5)
and the law of error propagation: (6)
Furthermore, using the law of error propagation, it can be shown that the variance of the change of range estimation in equation (3) is obtained by: (7)
With the last valid range measurement related to time to, the signal state at time t1 can be obtained for the pseudo-range PSR (8)
and the carrier phase Φ: (9)
The resulting variance of these estimations can by expressed by (10)
and (11)
respectively. The estimate of the Doppler and the related variance can be obtained analogous.
Considering the variances of the estimation, it can be decided if the signal can be reacquired instantaneously or if the receiver has to find the signal using standard acquisition routines in a limited search space.
Experimental Validation
The Volkswagen Passat station wagon operated by the Institute of Flight Guidance was used to evaluate the performance of the proposed algorithm (see PHOTO.) The test vehicle is customized from the standard by adding an additional generator to meet the power requirements of the measurement and processing hardware. In addition, the Controller Area Network (CAN) is mirrored and open to access the data collected by the sensors of the vehicle. The relevant sensors include a longitudinal accelerometer, a gyro for measuring the yaw rate as well as the odometers of all four wheels. The test vehicle is equipped with a GNSS front-end developed by the Fraunhofer Institute for Integrated Circuits. It is capable of streaming L1, L2, and L5 RF samples via two USB ports. The sampling rate of L1 is 40.96 MHz at an intermediate frequency of 12.82 MHz.
Test Vehicle. A customized Volkswagen Passat was used to evaluate performance of the algorithm.
The vehicle sensor data is streamed via CAN to an automotive PC from Spectra. It is equipped with an Intel quadcore CPU, 8 GB RAM, a Vector PCI CAN device and 256 GB SATA solid state disk allowing up to 195 MB/s writing speed. Additionally, it has been equipped with an Nvidia GeForce GT 440 graphics board that is used for processing the GNSS RF data. This specific graphics board was chosen because it offers a comparably high performance of the GPU at relatively low power consumption.
Both GNSS RF data and data from the vehicle sensor network are streamed to an ADTF hard disk recorder. Due to the setup of the data acquisition, several challenges have to be solved. The first challenge is that the front-end needs to be used as hardware-in-the-loop. It is by itself not equipped with an automated gain control. Therefore, it is not possible to just stream the RF data but it has to be decoded, processed for adjusting the gain, and then stored to the hard drive.
Secondly, the recording setup needs to cover high data rates. The GNSS front-end streams approximately 20 MB/s. As the data needs to be decoded and processed for gain control, the expanded data rate for recording is ~40 MB/s. In total including vehicle sensor measurements, >2000 data packets per second are streamed to the recorder. Because this could not be done using mechanical hard drives, we used solid state disks that also allow data storage during times of high vibration.
Related to the before-mentioned challenges, an efficient thread management needed to be implemented. The software framework’s threading classes are utilized to parallelize the receiver processes. Additionally, it has arisen that a significant part of the processing time is taken by the data transfer to the memory of the GPU.
In order to prove the advantages of an odometer-aided reacquisition, an applicable testing scenario was chosen. To distinguish an odometer-based aquisition approach from a model-based approach, a trajectory was chosen that features a right turn of 90 degrees immediately after cutting off the GNSS signal. A model-based kinematic prediction would project the trajectory in the direction of the latest known heading derived by the GNSS solution. Only a sensor-based state estimation is able to resolve the right turn. The driven trajectory is shown in Figure 3.
The GNSS signal has been cut off for approximately 10 seconds, which is equivalent of a 75-meter drive on dead reckoning sensors only after the right turn.
Figure 3. Trajectory of test drive includes a 90-degree turn. (Click to enlarge.)
Results
The following plots in Figure 4 show the performance of the virtual tracking channels. The plots in the upper row show the pseudorange output over time. For vividness they have been corrected for the motion of the respective satellite that is dominant due to their high speeds. Over a short period of time the satellites’ motion relative to the receiver can be linearly approximated. The pseudorange measurements over time were fit using a linear regression. The respective value of the linear regression was then subtracted from the pseudorange and plot over time as shown in the figures in the second row, leaving only the approximated influence of the vehicle’s motion.
Figure 4. Modified pseudorange and Doppler results of the virtual tracking channels. (Click to enlarge.)
The Doppler measurements have been similarly compensated by just subtracting the minimum measurement. These modifications of the pseudorange and Doppler measurements allow a direct comparison of each other as the Doppler can be understood as the first derivate of the pseudorange over time.
The results of PRN 6 show that the Doppler estimate during the GPS outage smoothly fits into the surrounding measurements without any major outliers. The plot of the pseudorange shows a similar behavior. The pseudorange could have potentially been modeled using a dynamic prediction that is not based on vehicle sensors due to the limited dynamics on the pseudorange measurements.
The Doppler plot of PRN 16 shows a strong change in the relative velocity between satellite and receiver. If a further projection of the Doppler using a linear dynamic model would have been used instead of predicting with vehicle sensors, it would likely have misled the reacquisition by ~ 50 Hz. The trend in the pseudorange measurements is comparable to PRN 6 at a higher rate of change.
The plots of PRN 21 probably show the advantages of using vehicle sensors for reacquisition best as the dynamics on pseudorange and Doppler are the most significant in the group. Both pseudorange and Doppler show a turning point during the GNSS outage. Especially, the pseudorange would have been mismodeled using a kinematic predicion that is not relying on additional sensors.
Conclusion
In this article, a tightly coupled positioning system implemented in the automotive-specific framework ADTF was presented that is based on the fusion of standard automotive sensor data and software receiver measurements. We showed that, using the tightly coupled solution, an acquisition state during signal outages can be estimated that allows the tracking channels to reacquire the signal instantaneously without the need of computationally expensive acquisition routines.
Under the assumption of a tightly coupled RTK position and small outage times, a reacquisition of the carrier phase without loosing the information about the phase ambiguity seems possible.
In the next version of the automotive GNSS receiver, the authors are planning to integrate the vehicle sensors to aid the tracking loops, which is likely to further improve tracking continuity especially in scenarios with high vegetation. Additionally, we plan to show that the implementation is capable of working in real time. Improvements of the initialization of the virtual tracking loops are also intended.
Acknowledgments
This article is based on a paper presented at ION-GNSS 2011, held September 19–23 in Portland, Oregon.
This work was funded by the Federal State of Lower Saxony, Germany. Project: Galileo – Laboratory for the research airport Braunschweig.
The authors would like to thank their colleagues working in the automotive navigation group for continuous support with the ADTF framework.
Hans-Georg Büsing holds a Dipl.-Ing. in aerospace engineering from the Technische Universität Braunschweig and has been a research engineer at IFF since 2008. He works in the area of applied satellite navigation, especially in the field of vehicle positioning.
Ulrich Haak holds a Dipl.-Ing. in mechanical engineering from the Technische Universität Braunschweig and joined IFF in 2008 as a research engineer. He works in the areas of receiver design and positioning algorithms.
Peter Hecker joined IFF in 1989 as research scientist. Initial focus of his scientific work was in the field of automated situation assessment for flight guidance. From 2000 until 2005, he was head of the DLR Pilot Assistance department. Since April 2005, he has been director of IFF. He is managing research activities in the areas of air/ground cooperative air traffic management, airborne measurement technologies and services, satellite navigation, human factors in aviation, and safety in air transport systems.
Figure 1. Autonomous air refuleing operational view.
By Alison K. Brown, Dien Nguyen, and Paige Felker, NAVSYS Corporation, Glenn Colby and Frank Allen, PMA-268 NAVAIR
An alternative precision GPS architecture, Precision RELNAV, enables an airborne tanker plane and a Navy unmanned combat aircraft to navigate independently to a high degree of precision without requiring carrier-cycle ambiguity resolution using precision GPS ephemeris updates to a tightly coupled GPS/inertial solution onboard each aircraft. The solution rivals that of conventional relative kinematic techniques while providing more robust positioning that reduces message traffic between aircraft and does not require a long filtering time.
Naval Unmanned Combat Air System (N-UCAS) is the U.S. Navy’s program to demonstrate technologies and reduce risk for unmanned, carrier based strike and surveillance aircraft. The Unmanned Combat Air System Carrier Demonstration (UCAS-D) program is specifically maturing technologies for unmanned carrier operations and Autonomous Aerial Refueling (AAR). Successful demonstration of UCAS-D technologies provides for transition and risk reduction to future unmanned and manned programs.
A key enabler for N-UCAS is the ability to perform AAR so that the N-UCAS can support long duration missions. As shown in Figure 1, the intent is for AAR operations to mirror current manned Aerial Refueling operations as much as possible and to operate using existing Navy probe and drogue and US Air Force boom receptacle refueling methods.
The planned refueling architecture for probe and drogue and boom-receptacle refueling developed by PMA-268 is shown in Figure 2 and Figure 3. For both of these architectures, the GPS/inertial navigation system on the UAS and tanker are used to calculate a precise relative position to be used by the UAS to approach the tanker from astern. For drogue systems, the final connection to the basket is performed using aiding from a laser-based drogue positioning system. In addition, an optional machine vision system is used to aid both methods of refueling from the receiver. Under the UCAS-D demonstration program testing is being conducted with surrogate aircraft to verify the CONOPS procedures and performance of the precision GPS/inertial navigation solution alternatives being evaluated. NAVSYS is supporting this program through a Small Business Innovation Research (SBIR) contract and is demonstrating a Precision-RELNAV (P-RELNAV) tightly coupled GPS/inertial solution that improves the robustness of the relative navigation solution as described in the following sections.
The first method that PMA-268 implemented for computing a relative GPS solution used the GPS/inertial integration approach illustrated in Figure 4. The inertial navigation solution from both aircraft was used to calculate the relative inertial vector e that is used for the real-time AAR guidance. The tanker’s raw GPS observations are also passed over the data link to the UAS where a relative kinematic solution is calculated to derive the carrier-phase based relative position between the aircraft, a. This approach relies on solving for the integer carrier cycle ambiguities on the observations from the two aircraft using the same algorithms that were previously developed for use in performing GPS precision approach and landings on the carrier. The precise GPS relative position is then applied to calibrate the inertial derived relative position and the resulting GPS/inertial solution is used to calculate an offset to the center of the refueling envelope (u) for guidance of the UAS to connect to the receptacle.
Figure 4. Precision-GPS relative GPS positioning.
With the P-RELNAV approach shown in Figure 5, Precision GPS Ephemeris data is provided to both aircraft across the tactical data links using the NAMATH system. As shown in Figure 6, NAMATH provides global services across military tactical data links through the Joint Range Extension (JRE) to provide real-time corrections to the GPS system errors using Zero-Age Precision GPS Ephemeris data, which is refreshed by the GPS Control Segment every 15 minutes. The NAMATH system is currently being used operationally by the U.S. military to improve navigation accuracy and also precision weapons delivery.
Using the PGE corrections significantly reduces the errors on the GPS observations allowing the GPS/inertial solution to rapidly converge and not exhibit step changes during satellite transitions from the GPS system bias errors. The GPS/inertial Kalman Filter on the tanker is used to observe the residual errors from the GPS satellites being tracked, and these residuals (δf) are sent from the tanker to the UAS which applies these as an update to its internal GPS/inertial Kalman Filter. As shown below, this final correction sets both the tanker and the UAS on a precise common reference frame resulting in a high accuracy relative position being derived from the vector difference of the two tightly-coupled GPS/inertial solutions (e*).
Figure 7 shows the difference in the GPS position that is calculated using the Precision GPS Ephemeris as opposed to the Broadcast Ephemeris. This shows that over a month, there can be peak position excursions as high as 5 meters in the horizontal and 10 meters in the vertical based on the GPS broadcast ephemeris. With a GPS/inertial solution, these bias offsets will cause the solution to “trend” between different position bias offsets whenever the satellite selected set changes. This trending introduces significant errors into the relative inertial vector between two aircraft (e).
Figure 7. GPS Peak Position Errors from Broadcast Ephemeris Offsets (March 2010).
P-RELNAV Flight Test Set-Up
The P-RELNAV performance was tested using data collected on a UH-1 helicopter at Eglin AFB. Two independent GPS/inertial systems were mounted on the equipment plate below the aircraft (Figure 8) and a GPS reference receiver on the ground was used to calculate a kinematic position post-test using a Magellan ZXW receiver on the aircraft as a truth system. The PGE corrections were uplinked to the aircraft through EPLRS for use in calculating a PGE-corrected navigation solution. NAVSYS used recorded GPS and inertial data from a Kearfott KN4073 and a NovAtel/LN-200 inertial system provided by Dahlgren NSWC. The raw GPS (Pseudo-range and carrier phase) and IMU (high rate acceleration and angular rate) data was processed using our InterNav solution and also recorded for post-processing. This data was then played back through InterNav to calculate independent GPS/inertial tightly coupled solutions from the two inertial systems with and without the PGE corrections and to compare the performance of the absolute and relative solutions against the kinematic positioning truth data.
Figure 8. Flight test equipment.
P-RELNAV Flight Test Results
The P-RELNAV algorithms were implemented in our InterNav software package. This has been previously used to generate very high accuracy relative kinematic solutions for providing high-rate Time Space Position Information (TSPI) for instrumenting F-16 aircraft. The InterNav software was upgraded to apply the tightly-coupled GPS updates to the inertial solution using the PGE Zero-Age Differential GPS (ZDGPS) corrections, and also to apply the GPS residual updates (δf) in the UAS Kalman Filter to compute the P-RELNAV relative position solution.
Dual-frequency observations from the GPS receivers were used to correct for the ionospheric group delays in the solution.
The performance of the P-RELNAV solution was evaluated by comparing the results from the two independent inertial solutions for the same location on the UH-1 aircraft. Tests were conducted over multiple flights with the GPS antennas at different locations on the UH-1.
The results from the first flight test are shown in Figure 9 through Figure 13. Figure 9 shows the GPS/inertial results during the flight with a tightly-coupled solution but without PGE corrections. Figure 10 shows the GPS/inertial results during the flight with a tightly-coupled solution but with PGE enabled. Figure 11 shows the satellite visibility during the flight test. These plots show that the satellite geometry changes, dramatically affecting the inertial position covariance, whenever the satellites used in the solution change. The inertial filters these errors, but the relative solution is biased and drifts resulting in over 2 meter errors. In Figure 12 the same plot is shown when the PGE corrections are applied. This shows that the relative position error has been reduced to better than 1 m per axis and 35 cm 1-sigma. For flight critical operations, such as AAR, minimizing position excursions is essential. Figure 13 and Figure 14 show a statistical measure of the percentage of time that the data exceeds a horizontal or vertical threshold. This shows the benefit of the PGE corrections in removing GPS excursions caused by satellite ephemeris errors from the navigation solution. (See the Appendix for a definition of the Inverse Circular Error Probable (ICEP) metric and its comparison with other statistical measures).
Figure 9. Flight 1: Relative position of KN and NovAtel/LN200 GPS/INS solutions.Figure 10. Flight 1: Relative position of KN and NovAtel/LN200 PGE enabled GPS/INS solutions.Figure 11. Flight 1: Valid PRNs used in KN GPS/INS solution.Figure 12. Flight 1: Relative Position of KN and NovAtel/LN200 PGE enabled GPS/INS solutions.Figure 13. Flight 1: Horizontal ICEP comparison for PGE enabled GPS/INS and GPS/INS solutions.Figure 14. Flight 1: Vertical ICEP comparison for PGE enabled GPS/INS and GPS/INS solutions.
Since both GPS receivers used in the test had a reasonably clear view of the sky, they were both tracking the same satellites. In the AAR CONOPS, the UAS approaches the tanker from below and so will have some satellites obscured from view by the tanker (see Figure 4). In this case, the use of different satellites can significantly increase the relative position error when PGE corrections are not available. In the case shown where one satellite was forced as a drop-out, the non PGE corrected vertical error grew to 4 meters for the relative solution.
Further improvements in the P-RELNAV performance will be achieved using the residual (δf) update mode in the InterNav Kalman Filter to set the estimated observation residuals for the common satellites to the same values for the UAS and Tanker GPS/inertial filters. This mode is currently being tested and the results will be presented in a follow-on paper.
Figure 15. Flight 1: Horizontal ICEP plot for PGE enabled GPS/INS and GPS/INS solutions. Different satellites tracked by the receivers.Figure 16. Flight 1: Vertical ICEP comparison for PGE enabled GPS/INS and GPS/INS solutions. Different satellites tracked by the receivers.
Conclusion
The P-RELNAV solution has the following advantages over using a conventional relative kinematic positioning solution in meeting the Automated Aerial Refueling precision positioning requirements.
Fast initialization — does not require time for carrier ambiguity cycles to be resolved.
Robust operation during satellite obscuration by the tanker — is not dependent on common satellites being maintained in view between platforms.
Insensitive to loss of carrier lock — does not require cycle ambiguity reinitialization if carrier lock is lost during the UAS approach to the tanker.
Work is proceeding on testing the P-RELNAV solution. Additional test data is being collected for performance evaluation under the UCAS-D demonstration program using dual aircraft as surrogates to demonstrate the P-RELNAV performance and compare the benefits of the P-RELNAV tightly coupled approach with the PGPS kinematic solution.
This work was sponsored under NAVAIR contract N68335-10-C-0094. The authors gratefully acknowledge the support of PMA-268 and the assistance of NSWC Dahlgren in collecting the flight test data and providing the truth reference for the P-RELNAV analysis.
Appendix: Inverse Circular Error Probable (ICEP)
For safety-of-life applications, the statistic of the excursion events, for example when a horizontal error is outside the safe error bound, is often more important than the knowledge of the percentage of points that are within a smaller error bound, such as CEP or DRMS. These excursion, or low probability, statistics can be examined with the Inverse Circular Error Probability (ICEP) function. The ICEP provides the horizontal position error (HPE) with a specified probability that a result could be outside this value. An optional input to the function is a filtering time constant, with the filter applied to the time-series horizontal error data before calculating the ICEP. This separates the effect of bias errors from short term noise errors that could be filtered (for example with an inertial unit) from the HPE.
HPE = ICEP (P%, τ)
Where
HPE= Horizontal Position Error value [m]
P% = Percent of total horizontal errors (x) that are larger than HPE
τ = filter time constant to reduce short term white noise
Note that the Circular Error Probable (CEP) which is the radial value that encloses 50% of the positioning results is closely related to ICEP, with
CEP = ICEP(50%, 0)
Also the R95 which is the radial value that encloses 95% of the positioning results is related to ICEP, with
R95=ICEP(5%,0)
Other common statistics used are the DRMS and 2DRMS values which are defined below, are also related to ICEP through the following equations.
For a Gaussian, uncorrelated error distributions with sigma of one meter in the range and azimuth axes, the ICEP is shown in Figure A-1 in blue. For each horizontal position error value, the ICEP gives the percentage of the distribution that has larger errors. Also shown on this plot are the CEP, DRMS, 2DRMS and R95 values which match the 1-sigma scale factors shown in the table above. Figure A-2 is the same data with a log10 plot. In this plot the y-axis is probability rather than percent. This plot is useful for examination of outlier behavior, as it shows low probability events more clearly.
Figure A-1. ICEP(P,0) for a Gaussian Distribution with 1 m 1-sigma.Figure A-2. Log Scale ICEP(P,0) for a Gaussian Distribution with 1 m 1-sigma.
Alison Brown is president and chief executive officer of NAVSYS Corporation, which she founded in 1986. NAVSYS Corporation specializes in developing next generation Global Positioning System (GPS) technology. She has a Ph.D. in mechanics, aerospace, and nuclear engineering from UCLA.
Dien Nguyen works for NAVSYS Corporation as a research engineer specializing in Kalman filtering estimations, kinematic positioning, and related navigational optimization techniques. He holds an M.S. in electrical engineering from Clemson University.
Paige Felker is a research engineer in the Algorithms and Analysis group at NAVSYS Corporation. She holds an M.S. in aerospace engineering from the University of Texas at Austin.
Glenn Colby is the chief architect for the Navy Unmanned Combat Air System at the Naval Air Systems Command in Patuxent River, Maryland. He has led the research, development, and testing of advanced aircraft, navigation and communications systems for more than 26 years. He received his B.S. in aerospace engineering with honors at the University of Virginia in 1984.
Frank Allen is the technology manager for the Navy Unmanned Combat Air System at the Naval Air Systems Command. In the last 16 years he has worked in management of research and development of advanced aircraft navigation and communications systems. Frank received his M.S. in physics from Northeastern University.
By Jordan Britt, David Bevly, and Christopher Rose
Nearly half of all highway fatalities occur from unintended lane departures, which comprise approximately 20,000 deaths annually in the United States. Studies have shown great promise in reducing unintended lane departures by alerting the driver when they are drifting out of the lane. At the core of these systems is a lane detection method typically based around the use of a vision sensor, such as a lidar (light detection and ranging) or a camera, which attempts to detect the lane markings and determine the position of the vehicle in the lane. Lidar-based lane detection attempts to detect the lane markings based on an increase in reflectivity of the lane markings when compared to the road surface reflectivity. Cameras, however, attempt to detect lane markings by detecting the edges of the lane markings in the image. This project seeks to compare two different lane detection techniques-one using a lidar and the other using a camera. Specifically, this project will analyze the two sensors’ ability to detect lane markings in varying weather scenarios, assess which sensor is best suited for lane detection, and determine scenarios where a camera or a lidar is better suited so that some optimal blending of the two sensors can improve the estimate of the position of the vehicle over a single sensor.
Lidar-based lane detection
The specific lidar-based lane detection algorithm for this project is based on fitting an ideal lane model to actual road data, where the ideal lane model is updated with each lidar scan to reflect the current road conditions. Ideally, a lane takes on a profile similar to the 100-averaged lidar reflectivity scans seen in Figure 1 with the corresponding segment. Figure 1. Lidar reflectivity scan with corresponding lane markings.
Note that this profile has a relatively constant area bordered by peaks in the data, where the peaks represent the lane markings and the constant area represents the surface of the road. An ideal lane model is generated with each lidar scan to mimic this averaged data, where averaging the reflectivity directly in front of the vehicle generates the constant portion and increasing the average road surface reflectivity by 75 percent mimics the lane markings. This model is then stretched over a range of some minimum expected lane width to some maximum expected lane width, and the minimum RMSE between the ideal lane and the lidar data is assumed to be the area where the lane resides. For additional information on this method, see Britt, Rose & Levy, September 2011.
Camera-based lane detection
The camera-based method for this project was built in-house and uses line extraction techniques from the image to detect lane markings and calculate a lateral distance from a second-order polynomial model for the lane marking in image space. A threshold is chosen from the histogram of the image to compensate for differences in lighting, weather, or other non-ideal scenarios for extracting the lane markings. The thresholding operation converts the image into a binary image, which is followed by Canny edge detection. The Hough transform is then used to extract the lines from the image, fill in holes in the lane marking edges, and exclude erroneous edges. Using the slope of the lines, the lines are divided into left or right lane markings. Two criteria based on the assumption that the lane markings do not move significantly within the image from frame to frame are used to further exclude non-lane marking lines in the image. The first test checks that the slope of the line is within a threshold of the slope of the near region of the last frame’s second-order polynomial model. The second test uses boundary lines from the last frame’s second-order polynomial to exclude lines that are not near the current estimate of the polynomial. second-order polynomial interpolation is used on the selected lines’ midpoint and endpoints to determine the coefficients of the polynomial model, and a Kalman filter is used to filter the model to decrease the effect of erroneous polynomial coefficient estimates. Finally, the lateral distance is calculated using the polynomial model on the lowest measurable row of the image (for greater resolution) and a real-distance-to-pixel factor. For more information on this camera-based method, see Britt, et al.
Figure 2. Camera-based lane detection (green-detected lanes,blue-extracted lane lines, red-rejected lines).
Testing
Testing was performed at the NCAT (National Center for Asphalt Technology) in Opelika, Alabama, as seen in Figure 3. This test track is very representative of highway driving and consists of two lanes bordered by solid lane markings and divided by dashed lane markings. The 1.7-mile track is divided into 200-foot segments of differing types of asphalt with some areas of missing lane markings and other areas where the lanes are additionally divided by patches of different types and colors of asphalt.
Figure 3. NCAT Test Facility in Opelika, Alabama.
A precision survey of each lane marking of the test track as well as precise vehicle positions using RTK GPS were used in order to have a highly accurate measurement of the ability of the lidar and camera to determine the position of the vehicle in the lane. Testing occurred only on the straights, and the performance was analyzed on the ability of the lidar and camera to determine the position of the lane using metrics of mean absolute error (MAE), mean square error (MSE), standard deviation of error (σerror), and detection rate. The specific scenarios analyzed included varying speeds, varying lighting conditions (noon and dusk/ dawn), rain, and oncoming traffic. Table 1 summarizes the results for these scenarios. For additional results, please see [8].
Scenario
MAE(m)
MSE(m)
σerror (m)
%Det
Lidar
Noon Weaving
0.1818
0.1108
0.3076
98
Camera
Noon Weaving
0.1077
0.0511
0.2246
80
Lidar
Dusk 45mph
0.0967
0.0176
0.1245
100
Camera
Dusk 45mph
0.2021
0.0592
0.2433
57
Lidar
Medium Rain
0.1046
0.0177
0.1314
65
Camera
Medium Rain
0.0885
0.0101
0.0635
91
Lidar
Low Beam, Night
0.0966
0.0159
0.1215
99
Camera
Low Beam, Night
0.1182
0.0185
0.0762
84
Table 1. Lidar and camera results for various environments.
Additional testing on the effects of oncoming traffic at night was examined by parking a vehicle on the test track at a known location with the headlights on. Figure 4 shows the lateral error with respect to closing distance where a positive closing distance indicates driving at the parked vehicle, and a negative closing distance indicates driving away from the vehicle. Note that the camera does not report a solution at -200 m, which is due to track conditions and not the parked vehicle.
Figure 4. Error vs. Closing Distance.
Based on these findings it would appear that the camera provided slightly more accurate measurements than the lidar while having a decrease in detection rate. Additionally the camera performed well in the rain where the lidar experienced decreased detection rates.
References
Frank S. Barickman. Lane departure warning system research and test development. Transportation Research Center Inc., (07-0495), 2007.
J. Kibbel, W. Justus, and K. Furstenberg. using multilayer laserscanner. In Proc. Lane estimation and departure warning Proc. IEEE Intelligent Transportation Systems, pages 607 611, September 13 15, 2005.
P. Lindner, E. Richter, G. Wanielik, K. Takagi, and A. Isogai. Multi-channel lidar processing for lane detection and estimation. In Proc. 12th International IEEE Conference on Intelligent Transportation Systems ITSC ’09, pages 1 6, October 4 7, 2009.
K. Dietmayer, N. Kämpchen, K. Fürstenberg, J. Kibbel, W. Justus, and R. Schulz. Advanced Microsystems for Automotive Applications 2005. Heidelberg, 2005.
C. R. Jung and C. R. Kelber, “A lane departure warning system based on a linear-parabolic lane model,” in Proc. IEEE Intelligent Vehicles Symp, 2004, pp. 891–895.
C. Jung and C. Kelber, “A lane departure warning system using lateral offset with uncalibrated camera,” in Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, sept. 2005, pp. 102 – 107.
A. Takahashi and Y. Ninomiya, “Model-based lane recognition,” in Proc. IEEE Intelligent Vehicles Symp., 1996, pp. 201–206.
Jordan Britt, C. Rose, & D. Bevly, “A Comparative Study of Lidar and Camera-based Lane Departure Warning Systems,” Proceedings of ION GNSS 2011, Portland, OR, September 2011.
As the GNSS world starts to appreciate the era of multiple global constellations, it’s probably worth considering the impact on aircraft navigation, GNSS airborne receivers and what these changes might soon bring to those who develop and use GNSS for airborne en-route navigation and approaches. Design and manufacture of new breeds of receivers are only the first of many steps along the road to full-fledged use of new capabilities. In aviation in particular, and in other high-precision fields to a lesser extent, many stages of study, regulation, development, test, and certification must be undertaken to eventually reach the promised land. The following capsule history of our progress to date also sheds light on what we may have to undertake in the future.
When GPS first came along, it was seen as a godsend for aviation. We’d been struggling with long-range Omega, which gave us, wow, close to a mile accuracy, and trying to get affordable but highly accurate Dopplers out of the military world. And those expensive inertials which still drifted pretty fast. GPS let us get to meters of position accuracy, was affordable, and could be used together with inertials to give us both bounded long-term accuracy and inertial azimuth, elevation and roll. Autopilots loved this stuff!
But, hey, when you put electronics onto an aircraft, you need standards and you need regulations. The International Civil Aviation Organization (ICAO), to which virtually all air-faring nations subscribe, sets up international standards for airborne system performance. ICAO quickly understood that GPS would be the navigation system that the whole world would come to rely on. So we got top-level marching orders from ICAO that set out the basic performance that we should expect from an airborne GPS system — Minimum Aviation System Performance Standards (MASPS).
The Federal Aviation Administration (FAA) in the U.S. as the world’s leading aviation agency took up the challenge and turned to the Radio Technical Commission for Aeronautics (a volunteer organization that develops technical guidance for use by government regulatory authorities and by industry — RTCA) to put together the Minimum Operational Performance Standards (MOPS). MOPS provide standards for specific equipment(s) used by designers, manufacturers, installers and users of the equipment. In fact, when we got MOPS for GPS, it allowed manufacturers of receivers and avionics to figure out what to build, what it should do, and how to qualify it. MOPS allowed the FAA to move on to develop the Technical Standard Order (TSO), which allowed FAA staff to verify compliance and ultimately install GPS avionics on aircraft via a Supplemental Type Certificate (STC) or, for new aircraft, via a Type Certificate. There are other ways onto aircraft too, but these are probably the most commonly used.
So we don’t just write our own specs and just develop and test it and sell it to aviation — there are a host of regulations you say…? That’s correct.
Around the same time GPS was hitting the streets, the FAA and other agencies recognized that these gizmos were basically software widgits, and that nerds could even be developing them in their garages or on their kitchen tables. We wrote software then that got the job done, and it worked at least as well as Microsoft’s did at the time, but our design and testing might have benefited from more structure. So along came RTCA DO-178 and subsequent revisions that set out the sequence of steps you should take to ensure a low probability of undetected error in your software. We started spending a whole lot more time upfront figuring out what the requirements were before we designed anything, and we had to wait what seemed like eons to get to code the stuff. So although we got process and we were able to cross-check between each step that we were still doing what we set out to do, the level and intensity of what came to be known as “verification” went through the roof. What a couple of guys in jeans had previously been able to cobble together in a couple of months now began to take a group of ten people working as an exceptionally well-coordinated team, maybe over several years. And the bright creative guys in the garages and kitchen tables were out of the aviation business. And a good proportion of the software engineers in the industry actually began to shun the rigor of the airborne software qualification/certification process, and prefer the less regimented receiver development associated with commercial receivers, especially challenging complex dual-frequency and RTK applications. So engineers with the persistence of airborne software qualification specialists are a rare and desired breed for airborne receiver manufacturers.
And then FAA put the same processes in place for hardware, especially ASICS, and gave us RTCA DO-254. Luckily we were already implementing ASICS using tools that applied thoroughness and intensity to the process, so DO-254 didn’t hurt quite as much as software development changes had hurt.
But we persevered, and eventually we got the bugs out of the receivers and got people flying with them, and there was great rejoicing in the aviation community. The General Aviation (GA) guys were a little impatient and couldn’t wait for the lengthy aviation development wheel to turn, so they bought handhelds in the meantime and duct-taped them to their yokes. But the FAA caught up with them, and these receivers evolved to the point where today they are buried behind square feet of glass Electronic Flight Instrument Systems (EFIS) and all you get is an icon which says your navigation source is GPS.
Avidyne Entegra Release 9 glass cockpit.
All these GPS receivers were, of course, single-frequency L1 only. Why, when there are perfectly good high-precision dual-frequency L1/L2 receivers out there? Well, turns out that only L1 is within the protected Aeronautical Radio Navigation Service (ARNS) band. The International Telecommunications Union (ITU) is an agency of the United Nations that manages worldwide allocation and use of radio frequencies — every country subscribes and plays by the ITU rules. So L1 is protected for GPS navigation use, but L2 isn’t. You can blast away with X-band radars in the L2 band and you’ll interfere with GPS L2 quite effectively. LightSquared assaults on the GPS bands excepted, the regulations currently protect you and your L1 GPS aviation receiver from intended and unintended jamming while your flight management system using GPS flies you safely from LaGuardia to Boston Logan.
So after several million dollars of engineering sweat and tears by receiver manufacturers, we eventually got ourselves to a point where we could fly GPS enroute from place to place, and the FAA even constructed and approved GPS approaches so we can get into airports. Then along comes WAAS, and we have new MOPS and new TSOs and many more millions of R&D required to implement WAAS LPV (Localizer Performance with Vertical guidance) approaches into the already certified L1 GPS receiver population. Crank up the machine and ensure each step is verified and the checks and balances of the aviation certification process ensure that we have the safety level needed to fly plane-loads of people through clouds and down to 200 feet with a half-mile visibility to the runway. It’s taken a lot of people a long time to work up these safety levels to use GPS like this, and guess what — it works!
CMC airborne GPS receiver.
And Automatic Dependent Surveillance – Broadcast (ADS-B) is rolling out across the U.S. and other nations as we use GPS on-board aircraft to tell us where they are and to allow agencies to monitor air traffic with less radar tracking. Another leap in safety levels, which requires airborne receivers qualified to new MOPS.
Even though the accuracy might be there, the element we’ve struggled with all this time is integrity. And we get integrity by adding signals and ensuring there are always more signals than we have currently. So now we have L5 coming, and even Galileo and the prospect of multi-frequency, multi-constellation airborne receivers that add safety, raise integrity and get us closer to the runway on approach.
The FAA is mulling over the ground rules for the new L5 MOPS, which they would like to have done and receivers ready for when the next generation of WAAS is ready to go. Over in Europe, Eurocae (the European equivalent to RTCA in the U.S.) has already started to think about Galileo, and even L5. Over the coming years, as the U.S. GPS constellation adds L5 capability, maybe even at the same rate that Europe puts Galileo in place, aviation receiver manufacturers — a rare breed of specialists indeed — are trying to figure out how to finance the impending investments needed to make and certify this new generation of aviation receivers.
Some people have already started down this torturous development path, so they will be ready as the L5 and Galileo MOPS committees work through the intricacies of adding capability to the already highly structured spider’s web of regulations and requirements. As concepts are thought out, it’s always great to be able to test if they work on prototype hardware and provide validation back into the requirements process, so those who have working receivers will play a key role as these new capabilities are brought on line.
And let’s assume that Europe will want Galileo in its future aviation system, and that the pace of fielding L5 into the GPS constellation is unlikely to pick up given the economic restrictions under which the DoD will have to operate in the next few years.
So, combined GPS L1/L5 Galileo E1/E5a receivers are favorite candidates for the next generation of airborne receivers. Rockwell Collins, Honeywell, CMC, Septentrio (at right, the Septentrio AiRx2 L1/L5 GPS E1/E5a airborne receiver), Accord, Garmin, and potentially others have their work cut out as we move out into a new generation of aviation satellite navigation capability. And yes, it’s very hard to do, folks.
Consider two notable developments in 2011 that will influence the development of consumer transportation:
China became the largest manufacturer of automobiles, producing more than 18 million vehicles, easily overtaking Europe and North America.
Smartphone volume shipments surpassed the volume of laptops and desktop PCs combined.
Reflecting these two rising economic rockets, the November Munich Telematics show drew its largest attendance yet, 500-plus participants, and a greatly expanded exhibit area.
The rising dominance of smartphones — one participant observed that they are taking over the world —will have a big impact on how users expect to access or view their telematics data; that is, any wireless information accessed by them while in their car. Developers and manufactures used to have a problem regarding which system to support, but with Android now at more than 50 percent of smartphones share, it is becoming the de facto first-choice standard and will probably become the user interface model.
eCall. Also in 2011, the European Union finally mandated eCall, the emergency call system in automobiles that sends vehicle position to emergency services after a crash. Unfortunately, the mandate is for 2015. I guess this gives them a chance to use the European satnav system Galileo, which hopefully may have something to offer hopefully by then.
This year the Russians leapfrogged the Western Europeans and mandated their own version of eCall, known as ERA, for 2013. It will use GLONASS, the Russian satnav system, which unlike Galileo is operational now. Of course, GPS is still employed, and the real benefit today is using GLONASS plus GPS in a multi-constellation fix mode for higher reliability especially in urban areas compared to GPS alone.
Emergency call in progress, triggered by SOS button in PSA Peugeot Citroen’s roof panel (bottom photo).
At the Munich Telematics show it was clear that the Russian mandate has put wind into the telematics emergency call market’s sails. From the Russian company Cesar’s presentation, we learned that following road accidents in Russia, 14 percent of car occupants die, compared to 2 percent in the United States. Getting emergency support to the scene more quickly is critical to reducing fatalities, and on this basis Russia has got some catching up to do.
You would think that everyone would be rushing to get more safety, and as one market research presenter said, it comes high on the user wish list. Another presenter stated that while people may desire it, they seem reluctant to pay for it at first. As an historical example, initially when people had the option of paying for airbags as an extra, it was practically never taken as an option. Now it is standard in all cars for drivers and passengers.Think about it — would you now buy a car without an airbag?
PSA Peugeot Citroen, the big French car company, shows the way with a version of eCall in their cars that doesn’t lose money! There is a big debate about who gets called when a crash happens. Is it the public service access points (PSAPs) or third-party services (TPS). Peugeot favours the TPS model, which can filter the more common breakdown and false alarms from true crash calls to be forwarded to the emergency services at PSAPs. While eCall initially favoured PSAP, the trend seems to support Peugeot’s decision and TPS.
The PSA eCall also does not support the so-called in-band modem, which allows crash-position data to be sent over a voice call on the eCall box by encoding the data into a speech-like signal. The modem theory is, you need to keep the voice call open to keep talking to the person in the automobile. According to PSA, apart from the issue of patents with the in-band modem, it seems that 30 percent of the data is lost, and 40 percent of the PSAPs in Germany cannot handle it.
GPRS is the best way of sending crash-position data with SMS text message as a back-up. As for voice, most people get out of their car after an accident and do not speak on the eCall box. I guess if people are unconscious and are not able to get out of the car, they won’t speak either.
While smartphones dominate in many areas, they have been ruled out for eCall safety apps in cars, as no one can guarantee a smartphone will work after an accident. As for crash detection, that can only work if a device is bolted down to the car frame. Only that way can you sense the high-G forces during a crash.
Insurance. Until the mandates kick in for eCall/ERA, you can understand why an automobile manufacturer’s marketing imagery does not include one of their car crashing or breaking down. So selling the eCall feature in this mindset is hard. On the other side are guys that do have the image of helping you after a crash: the insurance companies. And true to form, the big business has become insurance telematics.
Octo Telematics has taken a pole position in this area and had an impressive crashing-car demo that you could sit in at the show. The insurance telematics box then becomes an aftermarket product that is cross-subsidized by the insurance company. In return they receive crash data and get to monitor you to help you improve driving habits to reduce crashes.
Octo Telematics crash simulator. Show attendees were taken for a ride! The telematics box sends crash data to the insurance company to help drivers improve driving habits.
A last word on safety: most accidents now seem to occur when people are texting while driving. Apparently when the Blackberry message service was down for three days in Dubai, there were 20 percent fewer accidents.
Apart from eCall and insurance telematics, the other famous perennial telematic application is the connected car. As we all expected, we saw a lot of presentations on this. In simple terms, via telematics, a car is connected to the Internet. As the definition of telematics The branch of information technology that deals with long-distance transmission of computerized information, this might seem a no-brainer. But exactly how the car is connected and what value that offers constitute the two key questions for any application and market segment. Today a car buyer will almost certainly be an internet user.
How Is It Connected? For basic telematic apps like eCall and stolen vehicle recovery, it suffices to connect to the 2G GSM/GPRS wireless network that gives worldwide coverage. Operators like Telenor offer a so called global subscriber identity module (SIM) model that supports worldwide access at a price that makes business real.
For the so-called infotainment connectivity, the trend is 4G LTE, which offers the high data rates that the car companies dream about and flat-rate smartphone users expect. LTE is a packet mobile phone network already at Verizon and in European trial that is ideal for data. It appears that in the future, the best mobile phone network will be a combo of 4G LTE for infotainment data with 2G GSM for speech and 2G GPRS for global coverage telematic data.
What Value Does It Offer? The blanket answer is, unless it offers a useful service, it won’t really be used. Today most connected car services drop to a poor 10–20 percent retention after the free trial period. The key is really to look for helpful services. For instance, the connected heater or rather the ability to switch your car heater remotely on in cold winters of Sweden increased Volvo connected usage 50 percent. Saving fuel in this energy conscious low CO2 emission days would seem a useful application. Couple that with a connected car, traffic information, best routes, good driving-habit rewards, social network to let you post your good driving score, and ….
Fiat showed its eco:Drive solution, helping people save 6 percent on fuel consumption on average. That’s a start.
At the end of the day, more efficient cars are the answer to that. Getting people to use more efficient small cars for short trips is one of the ideas behind the BMW car-sharing model. Based on the BMW One series and the Minis made by BMW, it offers a service in Munich and Berlin (I have to admit I live in Munich and haven’t tried it yet). When you register, you present your driving license and the service add an RFID. You can use this RFID as a keyless entry into a car share. Of course the cars are connected, and a smartphone app helps you find the next free car. You can pick it up and drop it off where you want. Because they are new, more efficient small cars than your average old gas guzzler, they have done a deal to get free parking in town. It costs a flat 29 cents (Euro cents) per minute to drive, which includes the fuel price. I can remember when a mobile phone call cost that much before!
Moni Malek is CEO of ML-C MobileLocation-Company GmbH, based in Munich, Germany.
Telmap announced that Telmap will use INRIX’s real-time, historical and predictive traffic information in its Mobile Location Companion service worldwide.
According to the announcement, the partnership is expected to enhance the navigation experience and increase usage by allowing Telmap users to enjoy best in class routing through better alternative routes that take into consideration real-time traffic and will result in reduced travel time and more accurate estimated time of arrival (ETA). These improvements will be driven by INRIX’s breakthrough traffic analytics that accurately measure the speed of travel and estimate travel times for routes covering both major motorways and secondary roads, which is powered by the largest crowd-sourced traffic community in the world. INRIX’s traffic data coverage combined with the coverage of recently acquired ITIS Holdings offers Telmap users unprecedented coverage in 30 countries.
“Telmap’s goal is to provide its millions of users with an excellent and the quickest possible navigation experience. We evaluated several traffic data providers and INRIX’s excellent aggregation and technological capabilities, extensive coverage, and focus on traffic as their main product, will enable us to present the best traffic available in the world today”, said Motti Kushnir, Telmap Chief Marketing Officer.
“Telmap are a strategically important customer to INRIX,” said Stuart Marks, Senior Vice President of INRIX Europe. “Our efforts to combine the immediacy of community traffic reporting with our existing data will result in the delivery of critical information to the driver in a much more timely manner than available from other services today.”
Telmap reports that INRIX traffic information will be integrated into the Telmap Mobile Location Companion by the end of the year.
By Roland Bauernfeind, Thomas Kraus, Dominik Dötterböck, Bernd Eissfeller, Erwin Loehnert, and Elmar Wittmann
Open-field tests of jamming signals from widely available in-car jammers, measured with an experimental software receiver that records the intermediate frequency (IF) samples, enable a detailed analysis of interference effects from these looming threats.
In-car GNSS jammers, openly advertised online as personal protection devices, constitute the most serious threat of all the GNSS interference sources. Such jammers are relatively easy to purchase from abroad over the Internet and to operate by plugging into the cigarette lighter of a vehicle.
Their usage may be motivated by criminal intention such as disabling a vehicle theft-protection system, a fraud attempt against a distance-based road-user charging system or distance-based vehicle insurance, or by privacy concerns, to escape monitoring by a fleet-management or other tracking system. Since most current GNSS receivers carry a communication link, it is difficult to keep full control of the data flow. Further concerns arise from reports of companies storing user location data, as was the case with Apple. Concerns about privacy issues will grow with the widespread introduction of intelligent transport systems (ITSs), vehicles and transport infrastructure that apply information and communications technology to improve transportation efficiency, sustainability, and safety. The primary information source is GNSS for location enabled applications like eCall, a pan-European location based emergency call, which shall be in place and installed in every new car from 2015 on.
Cooperative ITSs, which are currently undergoing standardization, are transport systems that communicate their positions such that each vehicle has a virtual picture of the real world in its vicinity. The cooperative ITS network connects the vehicles with the transportation infrastructure. Vehicles establish a wireless vehicular ad-hoc network (VANET), based on their geographical position. In a VANET the position is communicated to be used at the application layer but is also required at the physical layer to enable geographical routing and addressing. This emerging vehicular communication is an enabling technology many novel and innovative driver assistance systems and location-based services. The result of using an in-car jammer is the complete destruction of GNSS signals not only in the vehicle it is operated in, but also within vehicles in the vicinity. This creates a serious threat to ITS’ future.
To counter the interference threat by in-car jammers, the University of Federal Armed Forces (FAF) Munich purchased some jammers offered online, for analysis in a laboratory environment and in open-field tests in the GAlileo TEst range (GATE). Measurements were taken with an experimental software receiver developed at the Institute of Space Technology and Space Applications. This receiver enables recording of intermediate frequency (IF) samples and detailed analysis of the interference effects on the receiver.
Jammer Interference Signals
First, we analyzed the purchased jammers shown in the Opening Photo. It is always better to understand the signal structure of undesired signals well, before starting development of applicable countermeasures and mitigation technologies. Therefore, the jammers were analyzed in the frequency domain with a spectrum analyzer, and the analyses were extended by a time-domain analysis by recording the signal with a software radio-defined card.
The first results showed that the majority of low-cost in-car jammers transmit a chirp signal with a bandwidth between 9.4 to 44.9 MHz in the E1/L1 band (other frequency bands haven’t been considered yet). The others are sine-wave oscillators with a 3-dB bandwidth of around 0.92 kHz and have a temperature-dependent center frequency around the Galileo/GPS center frequency, but they are not considered further in this article. Both jammer types belong to the category of narrowband interference, however the chirp jammers are much more effective in jamming the signal within the GNSS receivers.
The construction of an in-car jammer chirp signal is usually done by a voltage controlled oscillator (VCO) with an input voltage of a saw-tooth function. In general, it is a sine function with a frequency change over time, which can be described by
(1)
For a unidirectional linear chirp signal the instantaneous frequency f(t) varies linearly over time as
(2)
where f0 is the starting frequency and k is the chirp rate. The amplitude a(t) is usually constant. The corresponding time domain function for a sinusoidal unidirectional linear chirp is
. (3)
All in-car chirp jammers are linear with a positive uni- or bidirectional sweep. The negative slope is so high that we can neglect them for modeling and can describe jammer 1 with the equation (3)
. (4)
Tsw = sweep time.
The frequency spectrum of jammer 1 and jammer 3 is given in Figure 1 and Figure 4, respectively, where we can extract the bandwidth and the peak power from the graph. For measuring the peak power of the jammer it is important to take the max-function mode of the spectrum analyzer, because the internal sweep of the jammer and the spectrum analyzer is never synchronized. Table 1 shows the important parameters of the jammers.
Table 1. Chirp jammer parameters.Figure 1. Power spectrum of jammer No. 1.
To get the timing information of the signal, the analysis must be done in the time-domain. Therefore, we converted the jammer signal into an intermediate frequency and recorded the signal with a SDR card. The further processing has been done with Matlab, where we could extract the frequency change over time for jammers 1, 2, and 3, given in Figure 2, Figure 3, and Figure 5, respectively. Finally, these functions are exactly the same, which were generated for the VCO within the jammers.
Figure 2. Frequency over time at jammer No. 1.Figure 3. Frequency over time at jammer No. 2.Figure 4. Power spectrum of jammer No. 3.Figure 5. Frequency over time at jammer No. 3.
If we compare the jammers, we can see how the complexity increases from one to the other. For jammer 1, a standard saw-tooth generator with a rising slope has been used only for the input of the VCO. Jammer 2 uses two generators. Compared to jammer 1, a second saw-tooth generator with a falling slope and a four-times longer sweep time is added. In the most complex case, jammer 3, we find four generators in total. Jammer 3 causes a frequency burst every 1.12, 1.35, or 2.28 milliseconds. These frequency bursts can be seen also in the power spectrum in Figure 6.
Interference Tests in GATE
Various static and dynamic interference tests were performed in the Galileo Test Range (GATE) in Berchtes-gaden, Germany, where the impact of the jammer signals on both GPS and Galileo RF signals could be evaluated in a realistic manner. GATE is a unique outdoor test and development environment for Galileo and GPS satellite navigation. Consisting of eight virtual Galileo satellites located atop several mountains around the test area in Berchtesgaden, GATE provides a topology to support different testing scenarios. The Galileo signals are transmitted simultaneously on all three frequencies. E1, E5ab, and E6, compliant to the Galileo OS ICD specification. GATE’s virtual-satellite mode simulates a realistic moving Galileo satellite constellation and supports commercial Galileo receivers without any modification. Two monitoring stations within the test area receive and process these signals. A central processing facility steers and controls the signals transmitted.
Figure 6 gives an overview of the test range with its transmit and monitoring stations as well as the GATE central point. The interference tests with the GNSS jammers were performed in the area close to this central point.
With respect to the testing of RF jamming scenarios including GPS as well as real over-the-air Galileo signals in the GATE test area, some requirements have to be taken into account.
Transmission of any interference signals on the GPS and Galileo frequency bands requires an official license from the responsible authority in Germany (Bundesnetzagentur). An appropriate permission for trial radio transmission was available in the framework of the jamming tests. The disturbance of other GPS receivers in the vicinity has to be minimized in any case. Therefore the transmission power of the jammers must be limited so that a distinct impact on the GPS L1 signal reception is restricted to a radius of a few hundred meters at the most. Furthermore, the interference signal source must be placed at an adequate distance from the GATE monitoring station antennas in order not to affect the processing and steering process for the GATE signals.
Finally, in the case of performing GATE tests with a dynamic test user receiver, a severe degradation of the user reference position must be avoided. As the steering of GATE signals in the virtual-satellite mode is based on accurate and reliable user position information transferred in near-real-time to the GATE processing facility. a combined GPS-RTK and inertial measurement unit (IMU) solution is applied. Thanks to the use of the IMU, a GPS signal outage can be well compensated for a certain time period. In order to meet the GATE accuracy requirements, the jammer transmission was restricted to time intervals of about 30 seconds.
Ipex Software Receiver
The Institute of Space Technology and Applications PC-based Experimental Software Receiver (ipexSR) is a multi-frequency GNSS receiver realized completely in software (Visual C++/assembler), capable of tracking GPS and other GNSS signals in real time or post-processing.
For signal analysis, IF samples were recorded and analyzed in post-processing, using two front ends that can be operated in different modes depending on required frequency bands. For the interference analysis, only L1 was recorded with the front end parameters summarized in Table 2.
Table 2. Front-end parameters.
The front-end gain is set once for the measurement in the receiver’s configuration menu. The front end uses no automatic gain control. All the tracking loops settings can be set in the receiver’s configuration menu. For the phase lock loop (PLL), we used a non-coherent (Costas) dot-product discriminator and for the delay lock loop (DLL) an early-minus-late discriminator with the settings in Table 3.
Table 3. Tracking loop settings.
Jammer Effect on Receiver
To analyze the interference effect on the receiver, we took measurements with static receivers and different jammers approaching the receivers, starting from a distance of 1,200 meters. Both commercial receivers, capable of recording the carrier-to-noise density ratio, and the Ipex software receiver, capable of recording IF samples, were set up. Receiver antennas were mounted on the car roof. For jammer reference trajectory, we used an odometer with a GPS receiver providing initial position and reference time.
A measurement for the degradation in the receiver is the carrier-to-noise density ratio. The theoretical effective carrier-to-noise density ratio is defined as
where Q is the spectral separation gain adjustment factor. While moving the jammer towards the receivers, the received interference power Preceived(r) increases relative the distance according to the free-space loss as
where Pjammer is the jammer transmission power. Figures 7 to 10 give the C/N0 degradation for the four different receivers interfered with by the three different jammers in respect to the distance. The measurements have been taken at different times so the undisturbed C/N0 is varying.
Figure 7. Carrier-to-noise ratio for IpexSR.Figure 8. Carrier-to-noise density ratio for BeeLine receiver.Figure 9.Carrier-to-noise density ratio for NAVILoc receiver.Figure 10. Carrier-to-noise density ratio for Garmin receiver.
Comparing the professional receivers with professional antenna to the mass-market receivers with patch antenna, it is evident that the professional receivers are interfered with at a later point but lose lock on the signal earlier.
The degradation of the C/N0 for ipexSR compared with the theoretical curve as introduced before is given in Figure 11. The measured curves follow the theoretical one as long as the front end is not saturated. As soon as the front-end analog-to-digital converter (ADC) is saturated, it causes severe degradation of the signal which exceeds the pure degradation caused by the increased interference power until loss of lock on the signal.
Figure 11. Carrier-to-noise ratio for IpexSR (Jammer 1).
Saturation is caused because the amplitude of the received interference power exceeds the range of the ADC. The comparison between the theoretical and actual received signal strength in respect of distance for the measurements of jammer 1 is shown in Figure 12. With an effective jammer transmission power of –40 dBW, the curves show good alignment for the interval where the received interference power is noticeable above the noise floor, until the front
end goes into saturation and the received signal strength converges to an upper limit.
Figure 12. Received signal strength (Jammer 1).Figure 13. Sample distribution over 8-bit ADC (Jammer 1).
The rising received interference power drives the IF samples to the outer limit of the ADC and changes the distribution of the IF samples over the bins of the ADC as shown in Figure 13. For these measurements, the gain of the front end was set to have the samples without interference distributed over all the ADC bins. This setting with low remaining dynamic range is optimal when no interference is present, whereas with interference the ADC goes immediately into saturation. The red line shows the distribution of the samples where 0.2 percent of the samples are at the outer boundary.
Figure 14. Punctual correlator output (Jammer 1).
Until saturation of the front end, the interference degrades the correlation process by raising the noise floor. When the dynamic range of the front end can no longer occupy the received interference power, the degradation by saturation dominates. For the undisturbed signal, all the signal power is in the I-channel as seen at the punctual correlator output in Figure 14. The correlation is degraded until loss of lock on the PLL occurs.
Degradation of the correlator output has a direct effect on the performance of the tracking loops and their discriminator outputs, as shown in Figure 15. The discriminator error rises until it is out of the discriminator function’s pull-in range. When the PLL error is outside the pull-in range, the tracking loop loses lock on the signal.
Figure 15. DLL and PLL discriminator outputs (Jammer 1).
The degradation of DLL performance causes a position error as shown in Figure 16.
The measurements show that currently available in-car jammers degrade the receiver performance in an radius of about 1 kilometer around the interference source and disable position determination within a radius of about 200 meters.
Interference Detection
Jammers constitute a serious threat to the future of intelligent transport systems. Their use is forbidden by law, and their illegal use must be prosecuted. To have awareness of the actual number of jammers in use requires deploying jammer detectors at dedicated points and recording interference events. Promising points for initial measurements would be highway interchanges or highly frequented border crossings. Reliable numbers on the actual use of GNSS jammers would be required to support government decision-making regarding further actions, and to support the final goal of an comprehensive GNSS interference monitoring network.
For the interference detection test, we recorded were recorded with five static receivers deployed in the GATE core area as shown in Figure 17, with jammer trajectory in red.
Detection of the interference source is based on monitoring the jammer-signal-to-noise ratio (JNR). To prosecute malicious intentional jamming, it is necessary to assign the detected interference signal to the jamming device. Therefore, the signal was analyzed in the time-frequency domain for the characteristic chirp signal of a jammer. The gain of the front end was set to the minimum so that the front end could cover high interference power levels
First, signals were recorded with the chirp jammer located at the central point. The jammer is located outside the car, with line-of-sight to position 1. The measurements at position 1 at about 200 meters from the jammer are shown in Figure 18. Short-time Fourier transformations of the signals in Figure 19 and Figure 20 clearly show the presence of the chirp signal.
Figure 18. JNR at Position 1.Figure 19. STFT of Jammer 1 at Position 1.Figure 20. STFT of Jammer 3 at Position 1.
For the second measurement, the jammer was used inside a car. The car started at position 1, where it switched on the jammer and drove along the main street, passing position 3. The car then turned and drove back the same way. The measured JNR at the five positions is illustrated in Figure 21.
Figure 21. JNR with jammer 1 moving.The resulting degradation in C/N0 is presented for GPS PRN 9 in Figure 22 and for GATE PRN 46 in Figure 23. The measurements show that the jammer can be detected and identified within the distributed receiver network.Figure 22. C/N0 of GPS PRN9 with jammer 1 moving.Figure 23. C/N0 of GATE PRN46 with jammer 1 moving.
The next step in developing a comprehensive interference-monitoring network would be to have automotive GNSS receivers enabled to detect and report interference events. For this scenario, a jammer was operated in a moving car and measurements with the ipexSR driving in another car on the same road were made.
Both cars started at the same position. The pattern in Figure 24 corresponds to the following events. The jammer started first, followed by the receiver with a random car in between. After 170 seconds, the jammer parked at the roadside, and the receiver passed by, indicated by the single spike. At about 240 seconds, the receiver turned and passed by the parked jammer again, as indicated by the second spike at 310 seconds. After the receiver passed by the jammer, the jammer started again, approached the receiver from behind and overtook the receiver at 450 seconds.
During this measurement, neither of the two cars could track or re-acquire a signal. Reporting of the loss of lock on all satellites could therfore be used for a coarse localization of jammers.
Figure 24. JNR in a traffic environment with jammer 1.
Conclusion
The analysis has shown that the interference range of a jammer is very dependent on the receiver architecture. In every scenario, the jammers had severe effects. After detecting interference events, the next step is to mitigate their effect within the receiver. Mitigation techniques based on time-frequency transformations like short-time Fourier transform or wavelet packets are envisaged. With the ipexSR IF Sample API, Figure 25, it is possible to implement and test these algorithms in real time.
Figure 25. IF sample API.
Also the possibility of localizing the interference source based on the JNR and C/N0 measurements will be e
valuated.
Steps against the use of in-car jammers must be taken. To prosecute the use of jammers, detector units must be deployed. This would also help to gather reliable numbers on the use of jammers and would support and justify future actions. Clearly, degrading the integrity of GNSS positioning is a threat for all safety-relevant ITS applications. Therefore, avoidance and mitigation of interference signals should be subject of safety-related vehicular communication, and its standards should be able to handle this in the same way as other safety-related issues. We propose discussion of the GNSS jammer threat within the working groups for cooperative ITS standardization: GNSS interference should be handled in the same way as any other road hazard.
Acknowledgments
These results were developed during the InCarITS Project (Analysis, Detection and Mitigation of In-car GNSS Jammer Interference in Intelligent Transport Systems), founded by the Bundesministerium für Wirtschaft und Technologie and administered by the Project Management Agency for Aeronautics Research of the DLR in Bonn (FKZ 50 NA 1001).
Manufacturers
Jammers were analyzed with a Will’tek 9102B spectrum analyzer and signals recorded with a GE ICS-572B software-defined radio card. The two front ends were developed by Fraunhofer Gesellschaft (FhG). Receivers used for jamming testing were ipexSR with NovAtel GPS-704-X antenna and FhGIII front end, a NovAtel BEELINE with the same antenna, a NAVILock NL-302U Sirf3, and a Garmin GPSMap 76, the latter two both with patch antennae. Only the IpexSR was used for tests to locate jammers, using an FHGIII front end and NovAtel GPS 511 antenna (Position 1, 5), the same antenna with an FHGII front end (Position 2, 3), and an FHGIII front end with SensorSystems S67-1575-96 antenna (Position 4). The two-car driving test used the IpexSR with Novatel GPS-704-X antenna and FHGII front end. IFEN GmbH developed and installed the test range and is GATE operator at least until end of 2013.
Roland Bauernfeind works at the Institute of Space Technology and Space Applications at the University FAF Munich. He received a diploma in aerospace engineering from University of Stuttgart.
Thomas Kraus is a research associate of the Institute of Space Technology and Space Applications at University FAF Munich.
Dominik Dötterböck is a research associate of the Institute. He received his diploma in electrical engineering and information technology from Technical University Munich.
Bernd Eisfeller is director of the Institute of Space Technology and Space Applications at the University FAF Munich. He is responsible for teaching and research in the field of navigation and signal processing.
Erwin Loehnert received a diploma in aerospace engineering in from the Munich University of Technology. He is head of the Mobile Solutions department at IFEN GmbH, and GATE manager.
Elmar Wittman received a Dipl.-Ing. degree in geodesy from the Munich University of Technology. He works as a systems engineer in the field of GPS/Galileo satellite navigation for IFEN GmbH.
Many maritime users today believe that GPS will always be available. This is simply not the case.
By Alan Grant, Paul Williams, George Shaw, Michelle De Voy, and Nick Ward, The General Lighthouse Authorities of the United Kingdom and Ireland
GNSS availability can be affected in many ways, through events or conditions that affect constellation health, the signal-in-space, or the reception of that signal. The primary means of positioning, navigation, and timing (PNT) employed in maritime applications, whether stand-alone or augmented, has well known vulnerabilities.
This article considers three specific threats and reports on how they may affect maritime safety: GNSS interference and jamming; constellation availability; and space weather events.
Interference and Jamming
There has been a marked increase in both the use and the availability of GPS jamming equipment in recent years. The implications are that jamming units may find their way onto ferries and around ports or harbors where they will interfere with the many systems utilizing GPS, thus affecting maritime safety.
GPS jamming units are widely available on the Internet, with current models already capable of jamming L1, L2, and L5 signals. While we report here on the jamming of GPS, all GNSS constellations would be affected in a similar manner.
To understand the effects of jamming and GPS service denial on maritime safety, the General Lighthouse Authorities of the United Kingdom and Ireland (GLAs) conducted two jamming trials, in collaboration with the UK Government’s Ministry of Defence (MOD), who provided and operated the GPS jamming units. For the safety of all GPS users, and in line with MOD regulations for the peacetime use of GPS jamming units, notice was given to all national bodies. In addition, the GLAs issued notices to mariners explaining that aids to navigation (AtoNs) using GPS in the vicinity of the trials location would be unreliable during the jamming periods.
Flamborough Head. The first jamming trial was conducted off the East coast of the United Kingdom near Flamborough Head. The aim of this trial was to understand the effect GPS jamming may have on ship-borne and shore-based equipment, GLA AtoNs, and also on the crew.
The Northern Lighthouse Board vessel Pole Star steamed between two known waypoints, through an area affected by the jamming signal. Data was recorded from two typical marine-grade GPS receivers installed on the vessel, along with an eLoran receiver that provided the true position throughout the trial.
The results identified three distinct states (Table 1) corresponding to the manner in which GPS-fed equipment responded to jamming conditions. When the jamming signal was sufficiently strong to prevent reception of GPS signals, a large number of alarms sounded on the bridge almost simultaneously, providing a potentially disconcerting and confusing environment for the mariner. However, the effect that represented the highest risk was the provision of erroneous data from some GPS receivers.
Table 1. Effects observed for the three states identified from Flamborough Head trials.
Figure 1 compares an erroneous position reported by a typical marine-grade GPS receiver with the vessel’s true location. In this figure, the light blue line shows the path taken between the two waypoints.
The colors of the plotted position points indicate vessel speed. The three states described in Table 1 can be seen.
State 1 is observed at either end of the passage where the solid blue line occurs; this is where the jamming signal strength is much lower than the GPS signal strength, and the GPS-fed systems are operating normally.
As the vessel approached the main lobe of the jamming signal, indicated by the red lines, it reached an area where the jamming signal was comparable with the received GPS signals, leading to State 2. During this state, erroneous data can be observed with the receiver reporting the vessel on land traveling at high speed.
As the vessel entered the main lobe of the jamming signal, State 3 was observed: the GPS signals were swamped by the jamming signal, and the receivers failed to provide an output. Then, as the vessel continued the passage out of the jamming area, one can observe the change in states as the ratios of jamming to GPS satellite signals decrease, and GPS is reacquired.
In the worst case, the GPS receiver reported a position some 22 kilometers away from the true location. The GPS receiver nevertheless declared the position valid. This position was made worse by the fact it was reported inland at a speed of more than 100 knots, while the trial vessel steamed steadily at 10 knots. Depending on how the resulting GPS positioning data is used, it could feasibly result in vessels changing course, through the use of an autopilot, and it could also affect the vessel’s reported position to the outside world. This would then not only affect the vessel’s situational awareness but also the situational awareness of vessels in the vicinity.
The errors observed in Figure 1 were also seen on the vessel equipment fed by the onboard GPS receivers. Erroneous positions were observed on the vessel’s electronic chart display and information system (ECDIS), on the automatic identification system (AIS) positions (where loss of position prevents the unit from calculating a range or bearing to nearby vessels, greatly affecting the crew’s situational awareness), and on the vessel’s radar (Figure 2).
The results observed during these trials gave an important example of what can happen to onboard equipment as well as the impact it can have on the mariner during periods of GPS jamming and service denial. It is clear that GPS denial caused by jamming can not only prevent PNT information from being calculated, it can also result in erroneous data being presented to the mariner.
Newcastle. A second series of demonstrations was conducted off Newcastle-upon-Tyne, on the North East coast of England, to communicate the importance of resilient PNT to a selected audience. The audience included a number of key decision-makers from European and UK governments, maritime industry, mariners, and other aids-to-navigation service providers. The demonstrations took place onboard the Trinity House vessel Galatea.
For this trial, the GPS jamming unit was installed onboard the Galatea and configured to jam GPS within a small
area around the vessel. As before, two typical marine-grade GPS receivers were installed along with an eLoran receiver; for this trial, a modified electronic chart display was also installed and altered to enable two position inputs to be displayed at the same time, to compare the reported GPS and eLoran positions in real-time.
Throughout the demonstrations differential Loran (dLoran) corrections were provided using a transportable reference station installed on the shore at South Shields, to mitigate the impact of temporal variations on the eLoran position. Differential-Loran corrections were generated by the reference station and sent to the GLAs’ eLoran transmitter in Cumbria for inclusion in the eLoran Loran Data Channel (LDC) broadcast. The eLoran receiver on the vessel received the broadcast and was able to extract and apply the corrections in order to obtain an eLoran position within 9 meters (95 percent).
One demonstration scenario showed the sudden effect of a strong jamming signal, designed to simulate a jamming unit being brought onto a ferry or other vessel. This took the vessel’s equipment directly to State 3: complete loss of GPS information with a large number of alarms sounding on the bridge. The loss of GPS data prevented the Galatea’s AIS and VHF units, among other systems, from operating correctly.
Before the second scenario was conducted, the jamming unit was stopped, and all of the GPS receivers integrated into the bridge equipment were allowed to reacquire satellites and fully recover. The second scenario was designed to reflect a vessel steaming towards a jamming source. The field strength of the jamming signal was slowly increased until State 2 was observed, with erroneous and often hazardously misleading information reported.
As with the Flamborough trials, erroneous GPS positions reporting unfeasibly high speeds were observed as shown in the OPENING Figure. However, significantly more subtle errors were seen: errors where the vessel’s reported position differed only very slightly from the true location and wandered around slowly. These subtle changes produce believable positions but hazardously misleading information (HMI). While the overall result of GPS jamming on Galatea was consistent with that observed on Pole Star, there were a few marked exceptions.
The effect of GPS jamming can be seen (Figure 3) on the erroneous positions reported by the trial vessel NLB Pole Star (center right) and also on the vessel DutchProgress (top left).
The ECDIS onboard the Pole Star reported erroneous positions and ultimately failed with the complete denial of GPS. However the ECDIS on the Galatea continued to track the vessel’s position due to an additional position feed from the vessel’s gyro, making it more resilient to jamming, but only in the short term until the gyro requires re-calibration. This is carried out with its built-in GPS receiver! In addition, the AIS transceiver on the Pole Star reported the vessel’s position erroneously due to jamming, and this was observed at shore-based traffic monitoring stations.
During the demonstrations on the Galatea, the AIS transceiver did not provide any erroneous position information, as can be seen in Figure 4. These differences show that the impact of GPS jamming will be different for each vessel and depends on the model, installation, and configuration of the onboard systems.
Effect of Jamming on Safe Navigation
To navigate safely, the mariner needs reliable, clear and trusted information about where the ship is and what is going on around it, so that any threat can be located and identified. While consideration is often given to threats such as areas of shallow water, obstacles, or other vessels; consideration is not generally given to the loss of positional information, timing, or situational awareness.
Loss of GPS-derived PNT information at sea results in the loss of the vessel’s ECDIS, AIS, GPS, and DGPS receivers, preventing the mariner from being able to position the ship and others around it through what are nowadays regarded as the normal means. In addition, the systems one would normally expect to be independent from GPS, and as such available for use in GPS-denied conditions, are also affected; namely the vessel’s radar and gyro-compass.
The radar takes a GPS input to provide a “North-up” setting and the gyro-compass uses GPS to stabilize drift error. Under GPS-denial conditions these units also enter an alarm state and should not therefore be used in that condition.
Clearly GPS jamming can significantly affect the safety of mariners. From these trials it can be seen that the extent of the impact varies from vessel to vessel depending on the equipment installed and the configuration selected.
Satellite Constellation. From the users’ perspective, GNSS availability is the percentage of time they can receive usable data from sufficient satellites in order to calculate their position. The reduction in the number of available satellites in the constellation will have a direct impact on the system’s availability.
A report from the U.S. Government Accountability Office (GAO) in 2009 predicted “significant challenges in sustaining and upgrading widely used [GPS] capabilities” due to delays in launching modernized GPS satellites. The GAO reported the probability of maintaining a constellation of at least 24 usable GPS satellites could reduce to 80 percent or less by 2011, and not return to 95 percent probability consistently until 2015. This could lead to reduced satellite numbers causing coverage “windows” where less than four satellites could be observed and as such reduced GPS availability.
A later report by the GAO indicates that the probability of maintaining a constellation of at least 24 operational GPS satellites is now expected to be 95 percent for the foreseeable future. This figure is based on the current launch schedule, and although the U.S. Air Force Space Command (AFSPC) has provided reassurances, the satellite launch program has in recent years experienced delays, and therefore the risk of reduced satellite availability still remains.
Following the 2009 report, the GLAs commissioned a study to investigate the impact a reduced GPS constellation would have on users in their waters. This study was conducted by the GNSS Research and Applications Centre of Excellence (GRACE) and was split into two parts. The first part was to analyze the impact theoretically and found that with a 21-satellite constellation, GPS coverage “windows” (for example, fewer than four satellites) could last for several minutes and cover a large proportion of the UK and Ireland (Figure 5). This can cause reduced GPS availability and therefore increased likelihood of position errors affecting maritime safety.
The second part of the study investigated the effects further through a dynamic simulation, investigating the effects should a vessel be position
ed off the coast of Belfast during one of the coverage windows. For this a marine-grade GPS receiver and a simulator were used to observe the effects. The study found that the number of available satellites fell below four for several minutes and the reported position data from the receiver appeared to freeze for up to 10 minutes.
If a mariner was traveling at a speed of 35 knots when the position input froze, his reported position would be in error by 10 kilometers from an outage lasting 10 minutes. These outages are significant, and mariners need to be informed of such risks to GPS (and GNSS in the future) before they occur, so they are prepared for any disruptions.
Space Weather. Space-weather events are a particular concern to GNSS availability due to their random nature. It is known that GNSS signals are delayed proportionally to the number of free ions as they propagate through the Earth’s atmosphere enroute to the receiver. The amount of ions in the ionosphere, the total electron count (TEC), is dependant on time of day, latitude, and solar activity, among other factors. During high solar activity, the number of ions in the atmosphere is much higher than at any other time. The greater the signal delay, the larger the errors are in the satellite’s pseudo range and hence the position error can be significant.
Variation in electron density along the GNSS signal path causes signal refraction that produces phase scintillation, introducing group delay that may cause large errors in the pseudorange measurement. Diffraction of the signal wave front induces amplitude scintillation — variations in signal amplitude — with strong fades possible, leading to a GNSS receiver losing signal tracking, and at worst the GNSS navigation solution may be lost.
Solar activity is cyclical, peaking at a maximum approximately every 11 years, during which periods GNSS performance can be severely degraded, especially at equatorial, auroral and polar latitudes. The next solar maximum is predicted to occur during 2013.
During quiescent periods of solar activity, ionospheric effects on GNSS can be managed such that the residual errors caused by the ionosphere do not generally pose a problem to maritime navigation performance.
The GLAs’ DGPS corrections significantly reduce common mode errors, including the effects of the ionosphere. However, at the peak of the solar cycle with high levels of sunspot activity, solar storms and flares, the application of ionospheric models and differential corrections may be less effective, and this could increase position errors and introduce an integrity risk to maritime navigation.
Maritime navigation systems and services that rely on GNSS are at greatest risk of disruption from the ionosphere during the period from 2011 to 2015. Even during a quiet solar maximum, the occurrence of individual sun spots could produce significant effects for discrete events. The effects vary with latitude, season, and time of day (the hours soon after sunset being most affected).
Space weather events have the potential to affect GNSS availability, either by affecting the performance of the satellites themselves or by preventing signal reception.
Mitigation. In general, a number of steps can be taken to help reduce the impact of these threats:
Increase awareness of GNSS vulnerabilities.
Detect incidents and warn the mariner when they occur.
Prevent incidents from occurring, where possible, through legislation and enforcement.
Reduce as much as possible the effects of incidents when they occur, through the hardening of GNSS technology.
Have alternative means of PNT, independent of GNSS.
Understanding that these threats exist and knowing what disruption they may cause is the first step to mitigating their effects, but this does not stop them happening. Being able to identify that an event is occurring and that the data being received from the receiver may not be true is an important part of mitigating the effects.
For jamming issues specifically, the use of GPS jamming units is illegal in the UK and Ireland; however, preventing them from being used is very difficult to achieve. Jamming units are small and easily hidden; however, port-side security and vessel security procedures should prevent jamming units from being used in these locations.
It is a different case, however, to prevent a jamming unit from being used at a coastal location or headland due to the remote nature of these areas.
Mitigating the effect of jamming can be achieved in a number of ways: by limiting the effect within the receiver by using anti-jamming techniques, or by hardening GNSS receivers. Ultimately the best mitigating activity is to not rely on GNSS PNT once the integrity of the data has been compromised.
For space weather events or cases of reduced satellite numbers, there is very little action the mariner can take to remedy the problem or stop it happening. The mitigating action here is one of awareness — information forewarning the mariner that such a condition is imminent, for example.
Monitoring and detection networks can assist in providing such notifications and real-time information on GNSS problems. The need for such a network across the UK and Ireland is the subject of a different GLA publication, but the GLAs support the discussion on a body to monitor GNSS performance and to take the lead in the dissemination of key information.
For periods where GNSS availability has been affected by mutual interference, jamming, space weather events or constellation issues, the best mitigating action is to use PNT information from a second source, one with dissimilar failure modes.
Mariners need to be prepared for GNSS failures and have access to PNT information through dissimilar systems. In addition, procedures covering what to do in the case of GNSS unavailability should also be provided and rehearsed. It is with this view that the GLAs firmly promote the use of all available means of navigation.
Conclusions
All three threats to GNSS availability reviewed here could affect maritime safety. The two trials observed presentation to the mariner of erroneous data, some of which could be considered hazardously misleading, along with the degradation of crews’ situational awareness. The main effects observed were:
The presentation of random errors leading to hazardously misleading information that could, depending on installation, cause a vessel to move off course.
The presentation of erroneous and potentially misleading data to other vessels and shore-based infrastructure.
The sheer number of alarms on the bridge of the vessel could be disconcerting and distracting for the mariner.
The loss of GPS-fed systems, which can create an unfamiliar bridge situation and remove safety-critical systems from operation.
A large number of bridge systems are integrated with GPS and enter an alarm state during periods of GPS outage.
The loss of GPS or a lack of integrity in the reported information leads to an unfamiliar situation on the bridge.
The crews of the Pole Star and the Galatea were expecting to lose GPS, were well-trained, and had primed other systems so they could navigate safely. In real life, there would be no advance notice, and the impact on the crew would be more severe.
The impact of low satellite numbers, as predicted in the 2008 GAO report, could produce poor constellation availability and a loss of PNT information for a considerable period of time. This could result in the same outcome as observed in the GPS jamming trials when entering State 3, where many systems on the bridge failed and entered an alarm condition.
Space weather events are difficult to predict both in terms of when they may occur and their severity. Events could affe
ct satellite positions, their operation, and the reception of their signals by the user, and are clearly a threat.
The GLAs strongly support the need for a resilient PNT solution, one that could continue to provide reliable information during such threats for the safety and benefit of all mariners.
Acknowledgment
This article is based on a paper given at the Institute of Navigation’s 2011 International Technical Meeting.
Alan Grant is a principal engineer for the Research and Radionavigation Directorate of the GLAs of the UK and Ireland, technical lead and project manager for all GNSS projects there. He has a Ph.D. from the University of Wales.
Paul Williams is a principal development engineer with the Directorate and currently technical lead of the GLAs’ eLoran Work Programme. He has a Ph.D. in electronic engineering from the University of Wales.
George Shaw is an engineer at the Directorate and holds a master’s degree in mathematics from the University of Cambridge.
Michelle De Voy is a development engineer for the Directorate, with an MSc in oceanography from the University of Southampton and an MSc in satellite positioning from the University of Nottingham.
Nick Ward is research director of the General Lighthouse Authorities of the UK and Ireland, with responsibility for strategy and planning of research and development.
“GPS can no longer evolve fast enough. Satellite-based systems cannot maintain the speed of development now required for the hyper-fast evolutionary pace of modern applications and devices. For positioning for the future, it has become exceedingly clear that GPS now needs a terrestrial component.” — from a Locata Corporation prospectus
A large number of companies and engineers have thrown billions of dollars at trying to improve GPS in urban and indoor applications,” states Locata Corporation co-founder Nunzio Gambale. “From a technological perspective, Locata has created something completely new: the capability to autonomously create a GPS-style system on the ground.”
Members of the GNSS community can see for themselves at Locata’s coming-out party at ION-GNSS 2011, including release of a Locata signal interface control document (ICD). GPS World took an advance look at the technology in a June trial of the demo that all ION attendees can see. This article presents these reports, after an outline of the technology.
The key to Locata’s positioning system is the signal generated by the Locata transceiver, or LocataLite, to synchronize its time to other LocataLites in a network. Locata creates a network that, according to the company, “is in almost perfect synchronization” without using atomic clocks. Each transmitter dynamically synchronizes with other Locata transmitters using a patented method called Time-Loc. Gambale says that a Locata network currently locks to about 2 nanoseconds.
Each LocataLite base station has an uninterrupted range of approximately 10 kilometers, with indoor signal penetration similar to that of a mobile phone tower.
The company emphasizes that its transceivers are not pseudolites, but devices that create TimeLoc synchronization, and thereby enable an autonomous synchronized network that, locally, looks like GPS. The local constellation is under local control, and can therefore be designed for deployment at any power, any frequency, or any density required by an application.
The networks can scale easily. The term “local” can mean a room or warehouse (100s of m2), a campus or open-cut mine (10s of km2), an airport terminal area with approach and landing routes (100s of km2), or a wide area, range, or city (1,000s of km2)
Gambale sees markets for Locata’s technology in defense, mining, emergency services, construction, and security. Locata is designed to integrate with existing GPS technology, as simply another constellation. This means an approprieate GPS-Locata receiver can use the satellite signal when outside the range of a Locata network. To a combined GPS-Locata chip, the LocataLite will appear as another satellite.
The company sold its first Locata network in July 2005. Locata has signed partnership agreements of various kinds with Leica Geosystems and Newmont Corporation (mining), the U.S. Air Force, the Advanced Navigation Technology Center of the Air Force Institute of Technology, and several other firms under non-disclosure terms. There was an initial test deployment at Holloman Air Force Base in May 2008, as a truth reference system spanning a test area of about 52 by 15 kilometers.
For high-multipath environments such as indoors and warehousing, the company’s latest development is a new antenna called a TimeTenna, which it will demonstrate at ION-GNSS.
Future research and development will focus on the miniaturization of the Locata receiver. Work has begun on a combined GPS-GLONASS-Locata chip that can be integrated initially into professional and industrial devices, and eventually into consumer devices such as mobile phones.
Locata plans to work with integrators only, not with end users, making the technology available to qualified partners developing receivers and applications. The ICD is the first step in Locata’s technology rollout.
LocataLites awaiting boards. Each LocataLite transmits four PRN signals.
A Long Time Coming
Eric Gakstatter, Survey editor
You may have heard the Locata name pop up over the past several years. It would be in the news, then back underground into stealth mode. About five years ago, I heard some interesting rumors about its technology but I decided not to take them seriously until I saw some real products.
Two years ago, I sat down with Nunzio Gambale, Locata CEO, at the ION-GNSS conference. At last year’s ION, I talked with him again. At that point, I understood the potential impact of Locata’s technology — if it worked as advertised. I again told myself that before I spent more time on it, I wanted to see a product introduced to the market based on Locata technology. In January of this year, it happened.
Leica Geosystems introduced its terrestrial GPS Augmentation Network for the mining industry, based on Locata technology. To me, that was a pivotal point. Leica is a reputable company and wouldn’t introduce a product without a thorough vetting.
I contacted Nunzio and we had further discussions. I wanted to see the technology in action — hard to do since Locata is based in Australia, I’m in Portland, Oregon, and an early installation occurred in South Africa. Fortunately, the company’s need to do a real run-through of its demo on site, prior to ION, meant that I got what I wanted to see, right on my doorstep: a Locata preview at the Oregon Convention Center in June.
The Technology
Essentially, Locata has developed a system that is very much GPS-like in that one has a network of reference stations (LocataLites) that interface to an unlimited number of rovers. One major difference is that there is no space segment. It doesn’t need or use satellites. Essentially, each reference station behaves like a satellite on the ground, with the rover moving around inside the polygon formed by the reference stations. The rover position is accurate to the centimeter level.
The value of the Locata receivers is that they don’t need a clear view of the sky to operate like a GPS receiver does. Yes, that means centimeter-level positioning indoors, where RTK GPS doesn’t work due to satellite visibility constraints, as well as outdoors.
Sound cool? It is. I saw it working indoors at the Oregon Convention Center. Locata staff set up a large room with Locata reference stations around the perimeter. They had two different rovers: one mounted on a small push cart and the other on a golf cart. We were able to move the rovers around the room freely and view the updated coordinates at 1 Hz intervals (although it’s capable of much faster update rates).
The Challenges
The new TimeTenna (see facing page) is large. Today that form factor is required to handle the high-multipath indoor environment. Locata is working on a scaled-down version, although it’s not unreasonable to envision the current model being mounted on a forklift or other vehicle if it was mechanically hardened. The antenna for Locata’s outdoor systems (for mining and other less hostile environments) is much lower profile and similar to a standard GPS antenna.
The Locata system requires that you manage a network of Locata reference stations. Similar to an RTK network, the Locata system is based on a network of reference stations around the project area. The baseline distances can be quite long (tens of miles), but nevertheless, one must install and manage the network much as one would a GPS RTK network, albeit with much less IT department involvement than a GPS RTK network.
Lastly, Nunzio Gambale wholeheartedly agrees that Locata’s technology is still developing. He likens it
to where GPS was in 1990. I tend to agree. The antenna technology needs to reduce in size and the system architecture needs to be vetted for reliability in production environments. But keep in mind that Leica and the U.S. Air Force’s 746 Test Squadron have already bought into Locata’s technology in a big way.
Although I don’t pretend to have the technical understanding that some of the others in the room possessed during the June demo, I did hear one of the sharper engineers exclaim “genius” at one point, referring to the design.
It’s certainly worth a close look as Locata’s technology continues to develop and be deployed. I think the day isn’t far away when we will see a system from Locata that will allow a user to transition seamlessly from centimeter-level positioning outdoors using RTK GPS to centimeter-level positioning indoors without breaking a step.
Now I’m a Believer
Tony Murfin, Professional OEM editor
I was invited to Portland in late June to preview an operational system which promises to help GPS in tough signal situations and work well indoors. While Europe, China, India, Japan, and of course Russia are all working to get more operational satellites in space, Locata in Australia has quietly been perfecting its terrestrial navigation system. I say perfecting because skeptics and naysayers have criticized Locata and what was seen as a pseudolite system with a rather lengthy development cycle. But nothing speaks as loudly as an operational system adopted and fielded by Leica Geosystems or a contract with the U.S. Air Force to get people’s attention back in the right place, even though Locata would claim it is only just getting started.
As I walked into the Portland Convention Center I was certainly apprehensive as to how any GPS-like system could function well within the massive concrete and steel building. When I found the smiling Locata group tucked away in one of the side ballrooms, it didn’t take long before I became a believer. Those wall dividers that allow the Convention Center to reconfigure rooms are apparently referred to as Acousti-Seal 931 Steel Operable Wall panels — yep, perfect multipath reflectors. So to see totally repeatable few centimeter positions in this cavern was not what I was anticipating.
The ballroom’s carpeted floor had been carefully laser-surveyed with a matrix of 5-meter squares, with a high-precision dot marking each grid intersection. LocataLite stations were set up at each corner and one in the middle at the far end, each with three antennas. A master station at the left corner of the entry wall originated the TimeLoc signal, and on each station one antenna pointed to an adjacent station, over which TimeLoc synchronization was cascaded around the network. This is a key feature of the ground network, allowing it to become fully synchronized and also to be extended or reconfigured at will.
Of course, when you run your own ground network it helps to be able to run at power levels significantly higher than GPS, so it’s easy for each station to communicate with another, provided they roughly have line-of-sight of each other — kind of like having to actually see a GPS satellite to get it into your GPS position solution. If you have some buildings or bushes or trees to contend with, having higher power available makes things easier, especially if you want an RTK carrier solution.
The secret to working indoors appears to be the TimeTenna phased-array antenna that Locata demonstrated in the steel-clad ballroom. With this top-hat-like antenna mounted on a wheeled cart along with a receiver and laptop, and positioned over one of those surveyed locations on the carpet, we could easily see that positions within less than 5 centimeters were consistent and solid. As a truth system, the company also had a motorized laser scanner pumping out centimeter-level positions on a parallel measurement system, and it was clear that there was excellent centimeter-level correlation.
But don’t take my word for it. Come to Portland for the ION-GNSS conference, September 20–23, and see the Locata demo for yourself — you’ll be impressed too!
Then there is the sole-source U.S. Air Force contract that has Locata updating an existing network to provide independent reference positions over 2,500 square miles of the White Sands Missile Range in New Mexico. The Air Force apparently needs to know how its navigation systems work when it turns on localized GPS jamming. The Locata system is designed to give the Air Force better than the specified <18-centimeter position accuracy in GPS-denied environments.
In August, Locata cleared the final USAF critical design review milestone for the wide-area White Sands Missile Range deployment. This is clearly a good sign that Air Force wants to continue with the next-generation Locata system. With GPS denied on this range, test vehicles will likely be constrained to inertial-only navigation, but with a LocataLite receiver onboard pumping out high-accuracy position measurements, the Air Force will no doubt have plenty of location data to track dynamic performance under GPS jamming conditions.
Another application that Locata has been investigating involves airborne trials in Australia, where initial results indicate position accuracy of less than 3 meters at up to 50 kilometers. The trials have involved a ground network with six base stations spread over a roughly square area of 1,500 square kilometers.
A University of New South Wales test aircraft equipped with precision GPS, inertial reference system, and laser scanner for truth reference use flew to within 3 to 49 kilometers of the reference stations at around 7,000 feet, producing the reported <3-meter code solution. Trials data is still being analyzed to produce a higher accuracy carrier solution, and Locata expects to issue these results at ION.
Airborne Reference Equipment
Leica has apparently been working with Locata for some time. The proof-of-concept installation at a 300-foot deep diamond mine in South Africa and a production set-up at a gold mine in Western Australia are going strong.
The gold-mine installation has now been extended to two pit sites using 15 LocataLite transmitters in total. LocataLite receivers are mounted on vehicles, atop drills and shovels, and all run off the multi-pit Locata network. The mobile units not only carry LocataLite receivers, but also precision Leica GNSS receivers running off side-by-side antennas. As time progresses, the ultimate solution will use integrated multi-constellation/LocataLite receivers: the Locata signals integrated into a combined satellite+terrestrial receiver position solution, using a single integrated antenna.
It’s easy to envisage such an integrated receiver and antenna where the Locata ground-network signals are used as just another local constellation. The investment to get to such a receiver would of course have to be justified by a whole proliferation of Locata networks. This would seem to be on the way, given the significant progress that Locata has now unveiled.
Will It Fly — Literally?
William Shears, aviation engineer
If you are an aviation satellite navigation enthusiast, you probably noticed this hasn’t been an auspicious year for aviation GNSS or for GNSS applied to any other user segment that needs highly reliable GNSS service. Between personal privacy jammers, instances of accidental interference, and the big chill sent through the community by the LightSquared debacle, many are asking if GNSS is now or ever will be reliable enough to be a sole means of position and time for safety-of-life applications.
A few years ago, the very idea that ordinary people would want to own GPS jamming devices and that they would be easily obtainable on the Internet would have been considered absurd. Similarly, the idea that the U.S. government would not vigorously protect GPS from interference was just not credible. But here we are in mid-2011 and the vulnerability of GNSS to interference has come home to roost, in several very big ways. This new awareness of the weaknesses of GNSS has led the U.S. Federal Aviation Administration (FAA) and civil aviation authorities of other countries to start rethinking their long-term strategies with respect to satellite navigation.
Even well before LightSquared crept into the consciousness of the GPS community and then burst forth as the apocalyptic specter that threatens to virtually end the utility of GPS in North America, the FAA had begun a study to consider the need for an alternate positioning, navigation, and timing (APNT) system to support critical aviation needs. The idea being that as the U.S. air traffic management system transitions to become increasingly dependent on management of traffic via four-dimensional trajectories, reversion to a non-trajectory based mode (for example, controllers vectoring aircraft as they do today) would become unfeasible. Hence, airplanes will need a very reliable source of 4D positioning and outages for any extended period of time due to interference, or anything else will be unacceptable. The FAA set about studying what level of performance would be required for a system intended to back up GNSS in the future. Other countries began to follow suit, and whereas the concept of an APNT was obscure a year and a half ago, it has become a significant point of discussion at the International Civil Aviation Administration (ICAO) as well as within various countries, including the United States, Australia, and several in Europe.
At first blush, the Locata system would seem to be a ready-made solution poised to fulfill aviation’s need for a GNSS backup system. In fact, acting as an independent backup (and/or an augmentation to) GNSS is one of the main motivations in Locata’s development. The technology seems to have promise in meeting the aviation community’s needs for an APNT. Locata is relatively mature technology that has demonstrated accuracies well in excess of what is required of an APNT meant to back up GNSS for enroute, terminal, and non-precision approach operations. Perhaps even precision approach and landing could be supported. Also, the system is very flexible, which suggests that service coverage could be tailored as needed around important airports. The system has significant redundancies built in, including multiple frequencies, multiple antennas for path diversity, and the ability for the network to reconfigure which LocataLite uses which other LocataLite for time synchronization.
Given this flexibility and redundancy, it should be possible to configure a system that provides highly reliable service where it is needed. Another major advantage of the Locata technology for aviation is the higher signal power level that comes from using terrestrial signals rather than signals from space. In theory, a Locata system would be more robust to interference than space-based GNSS signals.
Some people are indeed thinking about Locata for aviation use. Locata has conducted flight trials in Australia using a prototype demonstration network of six LocataLites covering an area of more than 1,500 square kilometers around Cooma airport in Australia. Locata has reported code positioning solutions of better than 3 meters at ranges up to 50 kilometers, and will present higher accuracy carrier-phase solutions at ION. The U.S. Air Force is also preparing to use Locata in an aviation environment as an independent truth reference.
At the ICAO Navigation Systems Panel (NSP) meeting in May 2011, the Australian panel member presented a paper outlining the general need for an APNT. The paper included a description of Locata as an example of what an APNT solution might look like. However, it is interesting that the paper fell short of proposing that the panel pursue Locata as the solution or to suggest that any standardization of a solution for APNT begin immediately. In spite of all the potential advantages discussed above, the Locata system faces a major obstacle before it can practically be used in aviation applications: standardization.
The first aspect of standardization that is likely to be a huge impediment for Locata (or any other APNT proposal, for that matter) is spectrum. The Locata systems implemented to date have been designed to operate in the 2.4 GHz unlicensed industrial applications band. For Locata to support safety-of-life applications, national aviation authorities will require that an APNT system use spectrum that is properly allocated for use in a safety-critical aeronautical navigation system, that is, spectrum allocated for Aeronautical Route Navigation Services (ARNS). Spectrum allocated as ARNS is afforded special protection from interference. Coordination of services in or near ARNS spectrum is often difficult, time-consuming, and expensive. For example, coordination between civil aviation use of the 108–118 MHz band (used for instrument landing systems, or ILS, and VHF omnidirectional range, or VOR) and FM broadcasting in the 88.1–107.9 MHz band produces real costs and restrictions to be borne by the FM broadcasters. Consequently, any proposal to convert non-ARNS allocated spectrum to ARNS is likely to be met with significant opposition.
Spectrum is a finite resource, and virtually all spectrum is already in use by someone. So, the reality is that a future APNT will likely have to be implemented in some existing ARNS spectrum, since a new global allocation of spectrum for ARNS is an unlikely proposition.
The current allocations for ARNS include:
108–118 MHz (ILS/VOR),
960–1215 MHz (DME/Mode-S/ADS-B/SSR/JTIDS/MIDS),
1556–1626 MHz, and
5.1–5.25 GHz.
All indications are that a Locata system could be could be operated at these frequencies. However, services that already exist in those bands will continue for the foreseeable future. So, to be viable, a Locata system would have to coexist in one of these bands with other existing systems, that is, not interfere with the operation of those other systems. Such coexistence has yet to be demonstrated either by analysis or test.
After suitable spectrum has been identified, the next major hurdle for Locata is standardization of the signal-in-space to the degree that supports interoperability of equipment produced by different manufacturers in different countries. The Locata ICD released at ION-GNSS 2011 is a good step in the right direction. But for an aviation application, a great deal more would need to be specified, including details about the waveform (spectral mask, out-of-band emissions, and so on), the protocols for producing the signals, and the standard protocols for the application of data to derive a position solution. A clear allocation of responsibility between the ground processing and airborne processing will need to be defined so that system integrity can be analyzed and assured.
At the international level, such standardization activities can take a decade or more. The length of time required depends on the maturity of the system that is proposed for standardization. The existence of a similar standard, with perhaps a significant user base and operational experience also helps (for example, an IEEE standard or RTCM standards). So, again, the ICD is a good start.
Beyond the technical aspects of standardization, there are political and institutional aspects that can often be more formidable barriers. Issues with spectrum have already been mentioned. Beyond that, there are issues with intellectual property. Creating aviation standards based on proprietary technology is unpopular although not unprecedented. Proposals for standardization are more likely to be successful the fewer strings, such as licensing agreements or fees, that are attached. This is a challenge since companies that have worked hard to develop cool new technology are often reticent to give away their intellectual property in the name of standardization.
Given all the barriers, how does new technology ever get implemented in civil aviation? Typically, applications begin in one of two ways:
in support of war.
in support non-safety related industrial applications.
The military has historically pioneered many technologies (radar, DME/TACAN, GPS) that would probably not have been developed otherwise. Even after the initial military experience, there is typically a period of time when the new technology is used in a non-safety-critical capacity to support some commercial objective. In the case of Locata, some potential applications would be flight-test position-reference systems, high-precision photogrammetry, high-precision positioning for crop dusting, and any other applications that require a highly robust, high-accuracy position solution in a well-defined region where interoperability and certification are not issues. Those are relatively small niche applications, which may provide some valuable operational experience.
However, serious movement towards adopting Locata as a standard for APNT is unlikely to happen without the support of at least a couple of large countries. Even a large user base with equipage does not guarantee that countries will adopt the technology or that air navigation service providers will authorize the use of the technology for safety-critical applications. For example, many carriers are equipping with broadband Internet equipment to provide service to the passenger cabin. Yet, there is no serious discussion of using that datalink capability for safety-related communications. Similarly, a very large number of aircraft are equipped with Aircraft Condition and Reporting System (ACARS) datalink, yet use of that system is largely limited to non-essential Airline Operational Communication (AOC) applications.
So will Locata fly? I believe that is entirely up to Locata and other companies that work with Locata to address the initial military and niche airborne positing markets. Operational experience gained by such early adopters will be critical in laying the groundwork for the support that will be needed from large states like the United States, Australia, China, and those in Europe, if Locata is to be a player in the longer-term international standardization of APNT.
In the near term, Locata is already serving the aviation community by demonstrating the art of the possible relative to what a ground-based navigation system based on modern technology could be.
Most GPS devices in cars today give the driver two choices: shortest route or fastest route. GreenGPS provides a third option: most fuel-efficient route.
With gas prices skyrocketing, many drivers would be happy to spend a few more minutes on the road, or take a different route, if it meant burning less gas.
The answer could be the GreenGPS navigation service, now being developed by researchers at the University of Illinois at Urbana Champaign (UIUC), which finds the most fuel-efficient route for your vehicle.
“The most fuel-efficient route may be different from the shortest route because the latter may pass through downtown stop-and-go traffic,” explained Tarek Abdelzaher, project lead and computer science professor. “It may also be different from the fastest route because vehicles are not as fuel efficient at higher speeds.”
All cars manufactured in the U.S. since 1996 come with a standard interface to their internal gauges and engine measurements called the On-Board Diagnostics Interface, or OBD-II. GreenGPS runs on the driver’s GPS-enabled cell phone and uses an off-the-shelf wireless adaptor plugged into the vehicle’s OBD-II port to receive engine readings via Bluetooth.
The cell phone collects the readings and connects to a server that models the engine’s fuel efficiency and customizes navigation advice to the particular vehicle, Abdelzaher explained.
The best route computed by GreenGPS to the same destination may differ from one vehicle to another. “For example, my vehicle uses about 20-25 percent more gas in stop-and-go traffic compared to free-flowing traffic, whereas my wife’s car uses closer to 40 percent more,” Abdelzaher said. “GreenGPS may recommend to her a path that is longer but has no traffic, whereas it might recommend to me a path that incurs some traffic but is shorter.”
To users, GreenGPS looks like a regular navigation service. The driver specifies a destination, then ask the service to find a route. “It runs on your cell phone, except that in addition to the fastest and shortest route options, it offers the ‘least-fuel route’ option,” Abdelzaher said. “If the driver chooses that option, they receive the GreenGPS-recommended fuel-efficient route.”
The program works best with a small hardware addition to collect readings specific to the vehicle. “In order for the advice to be customized to the performance of your specific vehicle, the driver should invest in buying the OBD-II adaptor. It costs about $50 and is a one-time investment,” Abdelzaher said.
“If the driver does not wish to buy the adaptor, they can still use GreenGPS and supply the make, model, and year of their vehicle. In this case, GreenGPS will use data from other vehicles of the same make, model, and year, or vehicles as close to them as possible to compute the navigation advice,” Abdelzaher said. This social networking component is also being developed as part of the project.
The system pulls the GPS data from the driver’s cellphone. “If you use a GPS phone (and most smartphones have GPS), the system simply finds out your current location from your phone. Otherwise, you would need to supply both source and destination addresses (like you would when you get directions from Google Maps) and the system will show you the route on a map.”
Gas-Saving Pilots. In the first stage of testing, the team solicited volunteers to drive in the area of their university, in Urbana-Champaign, a city of 170,000. In all, 1,000 miles were driven by 16 different cars. Results demonstrated that following the fuel-efficient route saves on average 6 percent over the shortest route, and 13 percent over the fastest. “This was done on flat terrain and in the absence of significant congestion,” Abdelzaher said. “We expect that testing in higher traffic and richer topology will increase the variability in fuel consumption among different routes, resulting in even more potential savings when following the most fuel-efficient route. Verifying this conjecture is currently a topic of investigation for our ongoing research project.”
Abdelzaher said his team has just started the second stage. “In the second stage of testing, we will deploy GreenGPS on the UIUC Facilities and Services fleet (about 100 cars) and monitor performance over a longer period of time. Preparations for this deployment are currently under way. We also expect to offer GreenGPS publicly to any other volunteers who wish to help with testing.”
Impressed by early findings of gas savings, the second phase is being funded in part by a $300,000 grant from the National Science Foundation. Abdelzaher and Robin Kravets, another UIUC computer science faculty member, were awarded the grant this spring. It will help deploy GreenGPS among the campus fleet cars, and track and analyze the results.
“By sharing data on speed, fuel efficiency, and location of vehicles, better real-time navigation services can be developed that guide drivers to routes that are maximally fuel-efficient for their cars, hence reducing transportation carbon footprint,” the grant reads. “This project helps usher in a new era of sensing applications with more integration of humans, networks, and the physical world, which may have a significant impact on the economy, energy, and the environment by reducing transportation energy cost and carbon footprint.”
Other grant providers are the Office of Naval Research, which is funding research on the technology’s networking component, and IBM through its Smarter Planet initiative. As a part of this project, 200 or more cars in the Urbana-Champaign area of various makes and models will be fitted with GreenGPS. Through a social network of drivers, data and routes collected can be shared and used by those who don’t have the OBM-II adaptor installed.
Fuel consumptions for the various roundtrips between different landmarks.The landmarks and corresponding shortest and fastest routes.
