EGNOS Service Provision Workshop 2015 will be held in Copenhagen September 29-30. The workshop is sponsored by the European Satellite Services Provider (ESSP).
The agenda, now available online, includes program and status updates on EGNOS on Day 1, as well as a focus on aviation. Included are an update on the EGNOS Safety-of-Life Service for aviation and several sessions focused on successful EGNOS implementation stories in aviation.
On Day 2, sessions include EGNOS market status and the adoption plan, EDAS for added value applications, E-GNSS benefits in the environmental domain, EGNOS in the maritime application domain and EGNOS in land application domain.
To learn more or to register, go to the ESSP website.
A Vision-Aided Integrity Monitor for Precision Relative Navigation Systems
By Sean M. Calhoun, John Raquet and Gilbert L. Peterson
INNOVATION INSIGHTS by Richard Langley
TO MEET THE ACCURACY, availability, continuity and integrity requirements for many navigation applications, multiple-sensor systems are commonly used. For example, a GPS receiver might be combined with an inertial measurement unit, electronic compass and an altimeter to permit enhanced navigation accuracy, availability and continuity in obstructed or otherwise difficult environments. The use of arrays of sensors can also help to ensure that systems used in safety-critical navigation applications provide safe information by maintaining a high level of integrity.
An important group of devices that can be used in multi-sensor systems is one whose processes are based on light. These optical or vision-based devices include laser rangefinders and digital cameras. We could even consider our eyes to be in this group. In common with many other animals, we have built-in visual sensors to get around in our daily lives. Together with our memories, we use our eyes to get safely from one place to another. Ancient mariners tended to sail close to shore so that they could use visual cues for navigation. Later on, they learned how to use the light from celestial objects to navigate in the open ocean. And these days, while we could use the so-called “Mark 1 Eyeball” to continuously monitor the performance of a navigation system, this is often impractical, impossible or unwise.
In this month’s column, we’ll take a look at the development of a generalized vision-aided integrity monitor for precision relative navigation applications. The work is based on the concept of using a single-camera vision system, such as a visible-light or infrared electro-optical sensor, to monitor the occurrence of unacceptably large and potentially unsafe relative navigation errors. A vision-aided integrity monitor of this type could be extremely valuable in augmenting existing precision relative navigation systems, such as GPS, for many different safety-critical aerospace applications such as formation flying, aerial refueling, rendezvous/docking systems, and even precision landing.
It is particularly appropriate that such vision-aided systems be discussed at the present time since 2015 is the International Year of Light and Light-based Technologies, or IYL 2015. This United Nations initiative aims to raise awareness of the achievements of light science and its applications, and its importance to humankind. As mentioned on the IYL 2015 website, “[l]ight plays a vital role in our daily lives and is an imperative cross-cutting discipline of science in the 21st century. It has revolutionized medicine, opened up international communication via the Internet, and continues to be central to linking cultural, economic and political aspects of the global society.”
2015 is also an important anniversary year for several notable developments in our understanding of light. It is the 1,000th anniversary of the work of the Arabic scholar Ibn Al-Haytham, which culminated in his Book of Optics. A Latin translation significantly influenced a number of scholars in medieval and renaissance Europe including Leonardo da Vinci, Galileo Galilei, and Johannes Kepler. 2015 is also the 200th anniversary of Augustin-Jean Fresnel’s proposal that light behaves as a wave and the 150th anniversary of the publication of James Clerk Maxwell’s paper describing electromagnetic wave propagation as we discussed in “Insights” this past March. And we should also mention that 2015 is the 100th anniversary of the publication of Albert Einstein’s general theory of relativity, which includes a description of the propagation of light and other electromagnetic waves in the presence of a gravitational field. And where would GPS and the other global navigation satellite systems and their augmentations be without the understanding that general relativity provides? Nowhere.
“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. Email him at lang @ unb.ca.
Recently, there has been an increased recognition of GNSS limitations in terms of robustness, availability and interference. As a result of this recognition, there has been renewed interest in developing non-GNSS-based navigation systems to augment system capability. This has become particularly important with the trend toward autonomous systems, where required navigation performance (RNP) metrics, such as accuracy, integrity, continuity and availability become operational drivers. Because of this trend, there is renewed interest in gaining navigational diversity using imaging or vision-aided navigation approaches. Early research with vision systems used 3-D terrain databases and imaging systems to provide periodic position updates in collaboration with onboard inertial navigation systems (INS), much like radar systems did prior to the wide proliferation of GNSS.
For precision relative navigation applications such as formation flying, aerial refueling, rendezvous and docking systems and even precision landing, there is a significant body of research for the use of vision navigation systems. For example, a vision-based relative navigation solution for aerial refueling with the use of an a priori 3-D tanker model has been developed. Results from flight tests showed that image-rendering relative navigation is a viable precision navigation technique for close formation flight, specifically aerial refueling, and demonstrated 95% relative navigation accuracies on the order of 35 centimeters within the operational envelope.
As the body of vision-aided navigation research continues to grow, consideration of other RNP metrics is required. Ensuring that systems are providing safe information and maintaining a high level of integrity is paramount when considering safety-critical navigation applications, but is largely neglected in current vision-navigation research.
The concept of integrity, particularly for navigation systems, refers to the level of trust that can be placed in a navigation system in terms of detecting gross errors and divergences. Many navigation applications have adopted the use of protection levels, which are real-time navigation system outputs that bound the navigation errors to the required probability of integrity risk. For the case of vertical navigation, the vertical navigation system error (NSE) is bounded by the real-time vertical protection level (VPL), and as the long as the VPL is below the vertical alert limit (VAL), the system can continue its operation. Loss of integrity is defined by the case when the NSE > VAL without an alert or, in other words, when NSE > VAL and VPL ≤ VAL.
One of the richest sources of information for how integrity can be handled for precision relative navigation systems can be found with the Local Area Augmentation System (LAAS), which focused on providing integrity under fault-free and single ground reference receiver failure conditions. LAAS employs several quality monitors such as receiver autonomous integrity monitoring (RAIM).
Much of the vision-aided navigation research to date has focused more on system and algorithmic robustness, rather than quantitative and verifiable integrity, particularly for feature-based processing. One approach has introduced the concept of regional bounding for feature correspondence between time-sequenced image frames, including some feature-unique criteria that can provide some protection from feature correspondence errors. Although this approach does yield some robustness for the algorithms, no quantitative integrity characterization was developed. Another approach introduced a truly quantitative integrity monitor for failures in the mapping of features to pixels, particularly in the presence of a bias. This approach predicts the largest possible position error in the presence of one such bias due to feature mismatch using a GPS RAIM-type approach. The current state of research addressing integrity for vision navigation, using an image-rendering or template-matching approach, is even less mature. In fact, we have not identified any previous integrity-specific work for image-rendering vision navigation.
The research presented in this article generalizes the concept of integrity in terms of operating and alerting regions. Applications that use navigation systems generally have objective operating regions that require a certain navigation performance, whether this be around a glide-slope, a formation flight position or even a flight-path clearance. Navigation integrity becomes critical because large divergences from these operating regions, without an alert, can become safety risks. The alert limit is simply the instantiation of this concept. It is the threshold or measure of how much undetected divergence from the operating region can be tolerated without inducing unacceptably large safety risks.
The remaining sections of this article will describe the development of a rigorous and quantitative vision-aided integrity monitor for precision relative navigation systems. First, an introduction to relative navigation using image rendering will be covered in order to describe the fundamental vision navigation approach. This will be followed by a detailed derivation of the proposed vision-aided integrity monitor and simulation based performance results.
Using Image Rendering
The basis of our research is that vision-aided techniques, specifically image rendering, can be used to construct a high-performance integrity monitor for precision relative navigation systems. Image rendering approaches and/or template matching have been used extensively in vision applications such as machine vision, medical image registration, object detection and pose estimation, and recently as a precision navigation system for applications such as aerial refueling and formation flight. The general concept of image-rendering precision relative navigation was evaluated for an automated aerial refueling application, using the approach illustrated in Figure 1. The image rendering approach is based on comparing image sensors with rendered imagery from high-fidelity models, to estimate a relative location based on the best image correspondence.
The image correspondence process is the most critical aspect of the image-rendering or template-matching navigation approach, but the focus of our research is not to make claims of optimality or performance-difference judgments between these image correspondence techniques, but rather show feasibility in the overall vision-aided integrity approach using some of these techniques. Most image correspondence approaches transform the images into feature space, such as scale-invariant feature transform, silhouette, edges and corners, to name a few, and then compute a distance metric between the feature sets, such as Minkowski or Mahalanobis distance, to determine the degree of matching.
Once the actual sensor image is converted to feature space, rendered images are generated based on the relative navigation state estimate using the model, converted to feature space, and compared to the sensor features. This process is repeated across the navigation state space, computing an image correspondence value for each state estimate. The selected navigation state estimate is based on the “best” image correspondence value across the state space.
An example result of this process is presented in FIGURE 2, which shows correspondence values for an edge-based image-correspondence process. In this case, the minimum correspondence value represents the best estimate of the relative navigation state. These image correspondence values between the sensor image (IS) and the rendered reference images (IR) will form the basis for the integrity monitor detection rule.
FIGURE 2. GRD-based image correspondence illustration as a function of 2-D relative navigation state.
Vision-Aided Integrity Monitor Development
As indicated in the preceding sections, our research is based on defining a vision-aided integrity monitor in terms of detecting when the system navigation state (x) is within a specified operating region (XOR) versus being within the alert region state space (XAR). The integrity monitor can yield four distinct conditions: rejection (PR), misdetection (PMD), detection (PD) and false-alarm (PFA). The performance of this type of binary (H0/H1) detection scheme can be characterized using just two of these metrics, the detection and false-alarm rates, which will be the two primary performance metrics for this research. PD is the primary metric measuring navigation integrity, describing the probability that the monitor successfully detects the condition when x ∈ XAR.
Bayesian, Minimax and Neyman-Pearson are a few of the detection schemes available to solve this type of binary detection problem. These detection schemes rely on the knowledge of the underlying statistics of the H0 and H1 condition, often characterized in terms of the probability density functions (PDFs). The main difference between these approaches is the resulting detection rule value (δ). Once δ has been established, the resulting theoretical performances of the detectors are computed by integrating the underlying PDFs of the H0 and H1 conditions, pH0 and pH1 respectively. The probability of detection (PD) is computed as
(1)
The integrity performance of the monitor can also be described in terms of integrity risk or probability of missed detection
(PMD), which is computed as
(2)
Similarly, the probability of false-alarm (PFA) is computed as
(3)
This is represented graphically in FIGURE 3.
FIGURE 3. Graphical illustration of detection performance.
The PDFs represent the statistical distributions of image correspondence values for the respective H0/H1 condition. The general detection rule premise is such that for a given sensor image, the underlying PDF for the “best” image correspondence with the rendered reference set is sufficiently distinct when the sensor image is in an H0 condition versus H1. The characteristics of the H0/H1 PDFs that dictate the monitor performance are dependent on many factors, including the fidelity and accuracy of the world model, the general observability of the image rendering process and the image correspondence approach for the specific application. For our research, we used two image correspondence techniques to evaluate the overall integrity monitor approach.
The first image correspondence technique evaluated is a simple binary silhouette (SIL). In this approach, both the sensor image IS(x) and reference image set IR() are converted to a silhouette using pre-defined thresholds to first convert the red-green-blue (RGB) images to gray scale and then subsequently to a binary image. An image correspondence function computes the percentage of overlap between the silhouettes.
The resulting image correspondence is based on the ratio of the cardinality of these sets. The navigation state estimate () that yields the maximum image correspondence value from the set of rendered reference images or template database is considered the most likely for that particular image sensor (IS).
The second image correspondence utilizes edge features for the image correspondence process. Under this approach, magnitude of gradient (GRD) processing is used, in which the sensor image and the rendered reference images are preprocessed through a Prewitt filter to determine changes in image intensities between adjacent pixels. This process computes the components of the gradient. The gradient magnitude is computed by root-sum-squaring the x-y components and normalized, resulting in an edge detection. A Gaussian blur filter is then applied to the output of the edge detection.
The application of the Gaussian blurring compensates for the spatial discrepancies between the discrete reference set or template database and the sensor image. Finally, the resulting feature images, including both the reference image (IR_GRD) and the sensor image (IS_GRD), are processed through a sum-squared-difference (SSD) image correspondence.
The resulting PDFs are based on the best image correspondence with the RE reference set, which is the minimum for the GRD processing.
These image correspondences build the basis of the detection metric, utilizing both the sensor image (IS) and the rendered reference set (IR), which is spatially distributed across the operating region, illustrated by FIGURE 4. This illustrated example shows instances of both a H0 and H1 sensor image (blue and red, respectively). The underlying H0/H1 PDFs for establishing the detection threshold are determined by sampling sensor images from XOR and XAR and computing the image correspondence against IR. This can be done through a combination of high-fidelity simulation and/or test data. The overall performance of the integrity monitor will be dictated by these underlying distributions. The following sections show the results of this integrity monitor approach for an aerial refueling application.
FIGURE 4. Simplified example of rendered reference set (IR) illustrating image correspondence process for integrity monitoring.
Simulation Evaluation
To explore the performance of the proposed integrity monitor approach, an aerial refueling (AR) application was modeled within a simulation environment. The AR operation lends itself well to the construct of the proposed integrity monitor and is developed to show that the system (refueling aircraft) is in the refueling envelope (RE) and has not violated the alert limit, which in the AR case is the safety boundary (SB). In this operational case, H0 is defined as the condition when the integrity monitor determines the refueling aircraft is in the RE, and H1 as the case when the integrity monitor determines the refueling aircraft to be within the SB. A validity region is also defined in order to bound the problem, in which it is assumed that the refueling aircraft is always within, under both H0 and H1 conditions, as shown in FIGURE 5.
FIGURE 5. Integrity regions of interest for an aerial refueling application and illustrated example of a rendered H0 image set for the refueling envelope used as the correspondence basis for the integrity detection metric.
To determine the underlying H0/H1 distributions, a set of reference images uniformly sampled from the RE was rendered using the associated tanker and camera models. This rendered image set was used as the common basis for performing the image correspondence with the actual sensor image.
The baseline RE reference set used for this research was developed using 504 rendered images distributed in a spherically uniform manner across the entire RE volume. Then, two random sets of simulated sensor images were generated and drawn from both RE and SB regions. It is assumed that the refueling aircraft and corresponding sensor images are within the validity region in order to bound the simulation. This bounding assumption is an acceptable constraint, given that the system most likely had to pass several operational checks to ensure the refueling aircraft is in the general region of the RE as defined by the validity region. To get detailed statistical representation of the PDFs, particularly at the tails of the distribution, both RE and SB image sets included more than 100,000 simulated sensor images, representing true states of the refueling aircraft. The simulation environment for this analysis uses the same refueling tanker model for the sensor images and the RE reference set, which eliminates the effects of modeling errors. Additionally, variations in the attitude are currently not considered. The resulting PDFs for H0 (blue) and H1 (red) conditions are shown in FIGURE 6.
FIGURE 6. Underlying image correspondence distribution for H0 (blue) and H1 (red) conditions.
Figure 6 shows generally good distinction between the H0 and H1 hypotheses — a necessary condition to achieve good detection performance. Several techniques were evaluated for determining the PDF including histogram, nearest neighbor and kernel with a Gaussian weighting function. These underlying H0 and H1 distributions will be used as the basis for designing the detection thresholds, based on the image correspondence of the sensor image with the RE reference set. These results assume uniform prior distributions across the RE and SB regions; however, it would be relatively straightforward to incorporate non-uniform prior information, based on a particular application, as available.
Detection schemes are often characterized using receiver operating characteristics or ROC curves, which illustrate the detection-monitor trade-off between probability of detection and probability of false alarm. The predicted detection performance for this AR application is a function of these underlying H0/H1 PDFs, and this performance is captured in the ROC curves shown in FIGURE 7. The ROC curves show that 10-3 level integrity-monitor detection performance (PD) is realizable for both SIL and GRD image correspondence approaches, while still maintaining a reasonable probability of false alarm (PFA) of less than 0.05 (5%). The SIL approach demonstrates slightly better performance than GRD under the chosen image resolution and RE reference set density. Normally, theoretical ROC curves would extend through the whole range of values [0,1] for both PD and PFA; however, this assumes unbounded PDFs. Doing so would require an infinite number of simulation cases and is obviously not practical for a simulation evaluation to gain statistics necessary to extend the PDFs near the entire theoretical ranges. Overbounding of the PDF tails could be performed to extrapolate and extend the tails of H0/H1 PDFs to determine the integrity detection performance beyond the current ranges, but this was not performed as part of this research.
FIGURE 7. Predicted integrity detection performance for both SIL and GRD image correspondence techniques.
In most applications, conditions exist that are outside of the nominally defined operational envelope, but yet are not significant enough deviations to be considered safety risks that require alerts and action. Such a case exists for the refueling operation under consideration in this research, where there exists a region outside the RE, but not in the SB, which we will refer to as the operational limit volume (OLV). The current definitions of H0 and H1 for the vision-aided integrity-monitor approaches developed above only consider conditions within the RE or the SB volume, and not within the OLV volume. OLV conditions were omitted since they technically aren’t considered a safety or integrity risk. However, it is possible under certain implementations and operational considerations that integrity monitoring coverage is desired under these OLV conditions.
Using the same analysis process as the original evaluation, an updated simulation was performed, this time considering all points within the validity region, including the OLV points. To construct a detection scheme under this new paradigm, the OLV conditions must be either mapped to the existing H0 or H1 hypotheses, or a new hypothesis must be defined, possibly creating an M-ary hypothesis scenario. The approach taken for this research was to consider OLV conditions as a safety risk, which is a conservative approach, rather than defining any new hypotheses. The resulting image correspondence distributions are shown in FIGURE 8. Subplots (a) and (b) show the difference the OLV points have on the underlying PDF distributions. As expected, when the OLV points are excluded, the PDFs track the original distributions quite well. The impact of including sensor locations from the OLV is clear from these figures, yielding a much bigger overlap between the H0/H1 conditions.
FIGURE 8. Simulation testing results assuming OLV states are a safety risk. The prediction represents expected performance without consideration of the OLV states. (a) SIL image correspondence PDFs,(b) GRD image correspondence PDFs, (c) SIL ROC curve, (d) GRD ROC curve.
Much like the PDFs, the ROC curves align with the previous results quite well when the OLV conditions are omitted, but take a order of magnitude integrity performance hit when OLV is captured under the existing H0/H1 definition and detection thresholds. Even under this conservative assumption, the overall monitor performance still yields a 0.96 (96%) detection rate at a 0.05 (5%) false-alarm rate, as illustrated by the ROC curves shown in subplots (c) and (d) of Figure 8. It is likely that these results could be significantly improved by redefining the terms of the H0 and H1 conditions or defining an H2 condition specifically for the OLV region.
Sensitivity Analysis
In addition to the baseline integrity monitor results, various sensitivity studies were performed to evaluate the integrity monitor performance impacts of environmental and hardware considerations. These sensitivity evaluations focused on common vision-based considerations such as sensor distortions and lighting conditions, and monitor design choices such as pixel resolution and reference image density. The sensitivity aspects that were evaluated under this research included the number of reference images, the effects of image distortion, pixel resolution and lighting conditions.
Reference Set Density. In addition to our standard reference set of 504 RE images, we conducted tests using 288 and 729 images. While a larger number of images improves integrity detection performance, processing speed is decreased. It is possible to trade off processing power for performance as necessary for a particular application and the associated integrity monitor performance requirements.
Image Distortion. We applied radial and tangential distortions to the simulated sensor images (IS) such that they represented a 95% certainty of the residual error to represent an outer envelope case for this type of sensor. The impact on the H0/H1 PDFs is very minimal, and the results demonstrate a potential robustness to this common type of sensor effect.
Pixel Resolution. We evaluated eight different pixel resolutions from 12 × 9 to 1280 × 1024 pixels per image. Our results showed a surprising robustness to pixel resolution, indicating only marginal performance impacts down to extremely limited pixel densities.
Lighting Conditions. To explore the impact of lighting conditions, the simulated sensor images (IS) used as the basis for the sensitivity analysis were regenerated under a secondary lighting condition, intended to emulate a much brighter background environment, and processed against the original RE reference set. The results demonstrate that under these varying lighting conditions, the system again demonstrates a high level of robustness, particularly using the SIL image correspondence approach.
Ratio Test Integrity Test
The initial integrity monitor results discussed thus far only used reference images from the operational region, RE. However, it is also possible to use a reference image set created with rendered images from the alert region, SB, by including an additional image correspondence process between the sensor image and rendered SB reference set. This is done to create a ratio test statistic as the detection metric. We compute the ratio of the highest image correspondence between the RE and SB reference sets. This approach is very analogous to the use of ratio tests for GNSS carrier-phase integer fixing.
The resulting ROC detection performance of the ratio threshold approach showed that, as with the single RE reference set, the SIL image correspondence approach yields the best H1 detection performance, resulting in the best integrity protection.
The GRD ratio detection performance also yields improved performance and is comparable to the SIL image correspondence approach solely with RE reference set.
Conclusions and Future Work
In this article, we have discussed the feasibility of a vision-aided integrity monitor for precision relative navigation systems. The research posed the relative navigation integrity problem within the context of an aerial refueling application. Using image rendering, where an imaging sensor and high-fidelity 3-D model is used, we have shown that 10-3 to 10-5 level of integrity monitoring is attainable for aerial refueling and formation flight applications. Having this level of independent monitoring could provide significant relief to a GPS-based precision relative-navigation system from a system-safety and certification perspective. The research demonstrated the proposed integrity monitor was robust against several degrading imaging effects, including lens distortions, lighting conditions and reductions in pixel resolution. Although more work is required to validate the results of this research, which was based on simulated images, the results show high promise for this type of integrity monitor approach.
Disclaimer
The views expressed in this article are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U.S. Government.
Acknowledgment
This article is based on the paper “Vision-Aided Integrity Monitor for Precision Relative Navigation Systems” presented at ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation held in Dana Point, Calif., Jan. 26–28, 2015.
SEAN CALHOUN is the managing director at CAL Analytics, Columbus, Ohio, and is pursuing his Ph.D. degree at the Air Force Institute of Technology (AFIT), Wright-Paterson Air Force Base, Ohio.
JOHN RAQUET is the director of the Autonomy and Navigation Technology Center at AFIT, where he is also a professor of electrical engineering.
GILBERT L. PETERSON is a professor of computer science at AFIT and vice chair of the International Federation for Information Processing Working Group 11.9, Digital Forensics.
FURTHER READING
Authors’ Conference Paper
“Vision-Aided Integrity Monitor for Precision Relative Navigation Systems” by S.M. Calhoun, J. Raquet and G. Peterson in Proceedings of ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif., Jan. 26–28, 2015.
Image-Sensor Navigation
“Flight Test Evaluation of Image Rendering Navigation for Close-Formation Flight” by S.M. Calhoun, J. Raquet and J. Curro in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tenn., Sept. 17–21, 2012, pp. 826–832.
“Fusing Low-Cost Image and Inertial Sensors for Passive Navigation” by M. Veth and J. Raquet in Navigation: Journal of The Institute of Navigation, Vol. 54, No. 1, Spring 2007, pp. 11–20. doi: 10.1002/j.2161-4296.2007.tb00391.x.
“Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory” by J.D. Mitchell, S.P. Cryan, D. Strack, L.L. Brewster, M.J. Williamson, R.T. Howard and A.S. Johnston in Proceedings of 2007 IEEE Aerospace Conference, Big Sky, Mont., March 3–10, 2007, doi: 10.1109/AERO.2007.352723.
“Performance of Integrated Electro-Optical Navigation Systems” by T. Hoshizaki, D. Andrisani II, A.W. Braun, A.K. Mulyana and J.S. Bethel in Navigation: Journal of The Institute of Navigation, Vol. 51, No. 2, Summer 2004, pp. 101–121, doi: 10.1002/j.2161-4296.2004.tb00344.x.
Simultaneous Localization and Mapping
“A Review of Recent Developments in Simultaneous Localization and Mapping” by G. Dissanayake, S. Huang, Z. Wang and R. Ranasinghe in Proceedings of 6th IEEE International Conference on Industrial and Information Systems, Kandy, Sri Lanka, Aug. 16–19, 2011, pp. 477–482, doi: 10.1109/ICIINFS.2011.6038117.
Navigation Integrity
“Developing a Framework for Image-based Integrity” by C. Larson, J.F. Raquet and M.J. Veth in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division The Institute of Navigation, Savannah, Ga., Sept. 22–25, 2009, pp. 778–789.
“From RAIM to NIOAIM: A New Integrity Approach to Integrated Multi-GNSS Systems” by P.Y. Hwang and R.G. Brown in Inside GNSS, Vol. 3, No. 4, May-June 2008, pp. 24–33.
Minimum Aviation System Performance Standards for Local Area Augmentation System (LAAS), DO-245A, by RTCA SC-159 WG-4, RTCA Inc., Washington, D.C., December 2004.
Camera Calibration
“Flexible Camera Calibration by Viewing a Plane from Unknown Orientations” by Z. Zhang in Proceedings of ICCV’99, the Seventh IEEE International Conference on Computer Vision, Kerkya, Greece, Sept. 20–27, 1999, Vol. 1, pp. 666–673, doi: 10.1109/ICCV.1999.791289.
Digital Image Processing
Digital Image Processing, 4th Ed., by W.K. Pratt, published by John Wiley & Sons, New York, 2007.
Digital Image Processing, 3rd Ed., by R.C. Gonzalez and R.E. Woods, published by Prentice Hall, Upper Saddle River, N.J., 2007.
Signals and Noise
Detection of Signals in Noise, 2nd Ed., by R. N. McDonough and A.D. Whalen, published by Academic Press, Inc., Waltham, Mass., 1995.
An Introduction to Signal Detection and Estimation, 2nd Ed., by H.V. Poor, published by Dowden & Culver, an imprint of Springer, New York. 1994.
Aspen Avionics has acquired Accord Technology LLC from Accord India. Accord Technology will operate as an Aspen Avionics company continuing to supply Federal Aviation Administration (FAA) -approved OEM GPS solutions to the aerospace industry. Support of its current client base will carry on as usual with licensed production.
“Accord’s expertise to design and develop solutions that meet NextGen and other performance-based navigation requirements, coupled with Aspen’s display offering, create the opportunity to provide unique solutions for all aerospace segments,” said John Uczekaj, president and chief executive officer, Aspen Avionics.
“This is a perfect blending of two companies known for their innovative culture. Aspen and Accord share the same passion to develop aviation solutions that improve situational awareness and promote flight safety at an affordable price,” said Shenoy Raghavendra, Accord Technology chief executive officer.
The transaction, announced today, was completed on June 19 using a combination of cash and securities. NEXA Capital Partners provided merger and acquisition financial advisory services to Aspen Avionics. Also acquired was AvValues LLC, also based in Phoenix. Accord Technology LLC is a joint venture of Accord Software & Systems Pvt. Ltd., Bangalore, India, and AvValues.
Hal Adams, founder of AvValues, has been named executive vice president of business development for the combined companies. He will be driving new business to include growing the successful NexNav product line.
“Our combination of innovation and capabilities is unmatched in the aviation industry with the potential to deliver even more affordable, intuitive fight deck and avionics solutions. This translates into meaningful benefits to owner/operators in all areas of manned and unmanned aviation,” said Adams, executive vice president of business development.
Aspen Avionics is a leader in manufacturing glass cockpit displays for general aviation. Founded 10 years ago, more than 9,000 Aspen cockpit systems have been installed worldwide. Aspen Avionics is globally recognized for providing the general aviation marketplace with innovative and affordable products including its Evolution Flight Display System and Connected Panel — the first certified wireless technology that communicates with onboard avionics systems.
Founded in 2008, Accord Technology’s expertise lies in design, manufacture and support of GPS, with Satellite Based Augmentation Systems (SBAS) such as the USA’s Wide Area Augmentation System (WAAS), receivers and sensors for OEMs for all aerospace segments, on manned and unmanned platforms. Its NexNav GPS SBAS WAAS multiple-solutions product line revolves around three key receivers: NexNav Mini, NexNav MAX and the recently introduced NexNav Micro.
Screenshot from GSA video. See full GSA Flight Event 2015 video below.
News from the European GNSS Agency
Since its certification for civil aviation in 2011, EGNOS — the European satellite-based augmentation system — has been making flights in Europe safer, greener and more efficient. To celebrate this achievement and further promote EGNOS, the European GNSS Agency (GSA) in collaboration with the European Commission, invited the media and European aviation stakeholders for a unique EGNOS Flight Event in Toulouse, France, May 6-7.
Today, more than 140 airports in 15 countries across Europe benefit from EGNOS — with many more preparing for implementation. 171 LPV (localizer performance with vertical guidance) and 86 BARO approaches are already certified for use.
To highlight this impact, the EGNOS Flight Event, organized in collaboration with the European Commission, ESSP, ATR and Airbus, brought together aviation media and other sector stakeholders for a comprehensive briefing and demonstration of EGNOS, how it works and its significant benefits for the aviation sector. Along with flight demonstrations, the event assembled a unique array of EGNOS-experienced players — from pilots to operators, service providers and air traffic managers – to discuss how EGNOS is reshaping the future of air transportation in Europe.
Across-the-Board Benefits
Commercial, business and general aviation are all key market segments for EGNOS. For example, business and general aviation operators need to get to meetings as quickly and efficiently as possible, often requiring landing at smaller airports where Instrument Landing System (ILS) or other expensive ground-based navigation aids are simply not feasible. Thus, the implementation of EGNOS-based procedures at these airports significantly improves accessibility. “EGNOS, Europe’s first satellite navigation system, already has a good success story to tell,” says GSA Executive Director Carlo des Dorides. “EGNOS delivers continuous integrity protection in compliance with ICAO standards, allowing Cat I approaches with over 99 % availability. Today, 142 airports across Europe are benefitting from EGNOS — and the number is growing steadily.”
According to GSA Head of Market Development Gian Gherardo Calini, the Agency has the capacity to support airports and operators wanting to benefit from EGNOS. For example, this year the Agency has allotted €6 million to co-fund projects to implement EGNOS in aviation. A similar amount had also been allocated in 2014.
Airborne with EGNOS
Demonstrations of EGNOS included a briefing on EGNOS for rotorcraft and with the presentation of the GARDEN project. The project is using EGNOS to enable increased safety and better access for helicopters, for example, enabling air ambulances to access city centre hospitals. Participants were also given a first-hand look at EGNOS implementation in the cockpit of an Airbus H175 rotorcraft.
EGNOS in action was demonstrated by a series of flights using EGNOS for landing procedures with an ATR 42-600 turboprop, which was equipped with additional avionics in the main cabin so invited media could witness the technology at work. The flight demonstration took off from the Blagnac Airport in Toulouse, the venue for the EGNOS event, for a 15 minute circuit around Toulouse beforedemonstrating an EGNOS LPV approach and landing.
EGNOS for A350
A highlight on the tarmac was the Airbus A350WXB. Participants were given a tour of this new, state-of-the-art wide-bodied airliner — including a simulation of an EGNOS-enabled LPV landing in the cockpit. Airbus test pilot Jean-Christophe Lair described the A350’s new Satellite-based Landing System (SLS) that works with Satellite Based Augmentation Systems (SBAS) such as EGNOS. This is the first time such a system has been installed on a wide body airliner and will be supplied as a standard feature to customers.
According to Lair, EGNOS is fully integrated into a common, harmonised landing system interface on the A350 – the SLS. This allows the pilot to fly precision approaches like an ILS with geometrical vertical guidance down to 200 feet. This new navigation system will provide Airbus operators a wider range of solutions to optimise operations and increase accessibility without any compromise to safety.
EGNOS Expansion
The potential for expansion of EGNOS/SBAS is huge both in terms of global coverage and potential for use in Europe.
GSA Head of EGNOS Exploitation, Jean-Marc Piéplu, outlined the future upgrade of the system from the current Version 2 to EGNOS Version 3. “Version three will feature new capabilities, including dual frequency and dual-constellation with both GPS and Galileo,” he said.
This extension could potentially widen EGNOS/SBAS global coverage for aviation to over 90%. When asked about the timescale for this extension of coverage, Piéplu indicated that if the political will was there to implement, then this could be accomplished in 10 years as there were no outstanding technical issues.
According to International Council of Aircraft Owner and Pilot Association (IAOPA) Senior Vice President Martin Robinson, there is a huge potential for growth in Europe. Currently there are 4,649 aerodromes in Europe and some 50,000 general aviation aircraft operating. Compared to the US, only a fraction of these are SBAS enabled. In the US, the larger uptake of WAAS is due to a deliberate government-led industrial policy.
“Europe still lags behind the United States and there’s definitely room for growth,” said Robinson. “EGNOS will help to provide greater access to aerodromes throughout Europe and improve safety — but we need to be quicker if we are to realize these benefits sooner.”
The twin-engine, wide-body Airbus A350 XWB, seen here at Spain’s Adolfo Suárez Madrid-Barajas airport, comes with EGNOS capability.
News by the European Space Agency
The EGNOS system, developed by the European Space Agency (ESA) for sharpening the accuracy of satnav across Europe, has been adopted by a growing number of airports to enable satellite-guided landing approaches. The new Airbus A350 airliner, currently entering service, comes fitted with it as standard.
“For the first time on the A350 we have a new system called the Satellite Landing System,” explained Jean-Francois Bousquie, an Airbus flight-test engineer focused on avionics. “This allows pilots to perform precision landing approaches guided by EGNOS or its U.S. equivalent, WAAS, offering vertical guidance down to a minimum of 60 meters before the pilot sights the ground to make the go/no-go decision on the final landing descent.”
The A350’s Satellite Landing System allows pilots to perform precision-landing approaches guided by EGNOS or its U.S. equivalent, WAAS. The capability offers vertical landing guidance down to a minimum of 60 miles before the pilot sights the ground to make the go/no-go decision on the final landing descent.
The European Geostationary Navigation Overlay System, or EGNOS, can provide horizontal and vertical guidance to anywhere in Europe, without the need for any additional airport-hosted infrastructure. By using three geostationary satellites and a 40-strong network of ground stations, EGNOS improves the accuracy of GPS signals over European territory, while also providing continuous updates on their integrity.
The result is that the EGNOS-augmented signals are guaranteed to meet the extremely high performance standards set out by the International Civil Aviation Organisation standard, adapted for Europe by Eurocontrol, the European Organisation for the Safety of Air Navigation. The signals from space can therefore be relied on routinely for the safety-critical task of vertically guiding aircraft during landing approaches.
A total of 131 airports in Europe offer some 225 EGNOS-based approach procedures. By 2020, 582 landing procedures are expected across 20 European countries. The largest international airports use Instrument Landing System (ILS) infrastructure, with radio beams offering a truly precision landing capability, including the ability to autoland when visibility is at its worst.
But ILS is expensive to install and maintain, so smaller regional airports often forego it. The same is true of many new or expanding airports. Even with larger airports, in many cases only their busiest runways are equipped with ILS. So EGNOS offers a cost-effective way of safely increasing use of remaining runways, boosting the flexibility of any given airport.
“By reducing the value of the minima — the lowest safely guided altitude — for non-ILS runways, EGNOS increases the efficiency and safety of aircraft landings,” added Bousquie. “The take-up of EGNOS by European airports remains relatively low for now, but this should change over time. And with the A350, we are really designing for the long term — each aircraft will have a working life of 25 to 30 years.”
“Every qualified commercial airline pilot has been trained on ILS, to follow its radio beam,” Bousquie said. “So the Satellite Landing System works by having them follow the same type of cues as much as possible on a ILS ‘look-alike’ basis, employing all available navigation data including EGNOS.”
A pair of onboard Multi Mode Receivers manage the A350’s radio sensors, compute the deviations and ensure interface with display and guidance systems.
EGNOS is Europe’s first venture into satellite navigation. EGNOS broadcasts augmented information through a trio of geostationary satellites linked to a network of monitoring ground stations, to sharpen the accuracy and reliability of GPS signals across the continent. (artist’s concept: ESA)
News from the European Space Agency
The next decade’s aircraft pilots will be able to rely on enhanced, reliable satellite navigation signals on a seamless basis across much of the world, thanks to decisions made at the latest gathering of worldwide satnav augmentation system providers and experts.
The U.S. Wide Area Augmentation System (WAAS) and European Geostationary Navigation Overlay Service (EGNOS) are leading examples of satellite-based augmentation systems (SBAS) that apply additional ground stations and satellite transponders to sharpen the accuracy and reliability of existing satnav services across given geographical regions.
These performance enhancements permit satnav to be employed for safety-of-life services, especially aviation. Such systems are based on the U.S. GPS for now, but plans are being laid to move to a multi-constellation design employing Europe’s Galileo, China’s Beidou and Russia’s GLONASS satnav systems beyond 2020.
The 28th Satellite-based Augmentation Systems Interoperability Working Group (IWG), planning standardization of SBAS systems to come, was hosted at ESA’s ESTEC technical centre at Noordwijk, the Netherlands, on April 1-3.
The ESTEC facility in Noordwijk, The Netherlands. (Photo: ESA)
All participants unanimously endorsed the “message definition” for a new secondary SBAS channel — to be known as L5, along with the current L1 — for the planned second-generation SBAS systems, which will utilize dual-frequency multi-constellation signals.
Using dual frequencies greatly increases the accuracy of navigation systems, by allowing interference from the ionosphere — an electrically active outer layer of Earth’s atmosphere — to be largely subtracted from the final result.
“This definition is presented in what is called the Dual Frequency Multi-Constellation Definition document,” explained Didier Flament, representing ESA. “It represents the outcome of a four-year activity, which started at IWG 19 in Japan, back in 2010, coordinated between all IWG members under the technical leadership of ESA and French space agency CNES on the European side, and the Federal Aviation Authority (FAA) and Stanford University on the U.S. side.
“The formal IWG review loop for the document took six months to conclude, with this IWG 28 then allowing endorsements to be gathered by SBAS project managers, culminating in formal signatures to the document,” Flament said.
SBAS coverage for 2020: Comparing current worldwide SBAS coverage — based on WAAS, EGNOS and MSAS — to the situation envisaged for 2020–25: near-global coverage based on WAAS, EGNOS, MAAS, SDCM and GAGAN, with an expanded network of stations in the southern hemisphere, all based on a common dual-frequency/dual satnav standard being finalized by the SBAS Interoperability Working Group. (Image: ESA)
IWG members now intend to have this document accepted by the official international SBAS standardization bodies: the International Civil Aviation Organisation, the U.S. Radio Technical Commission for Aeronautics (RTCA) and the European Organisation for Civil Aviation Equipment.
“This next step is very important,” added Didier. “Not only for the coming 2016-22 implementation of the European EGNOS v3 but for implementation of other second generation SBAS in other regions of the world.”
The meeting also reported on the state of development of the other global SBAS systems. Along with the four operational systems — the U.S. WAAS, European EGNOS, Japan’s Multi-functional Satellite Augmentation System (MSAS) and India’s GPS-aided geo-augmented navigation or GPS and geo-augmented navigation system (GAGAN) — these comprise South Korea’s KASS, China’s Beidou SBAS, Russia’s System for Differential Corrections and Monitoring (SDCM) and the West African Agency for Aerial Navigation Safety in Africa and Madagascar (ASECNA) SBAS.
The follow-up IWG meeting will take place in October, hosted by the FAA in Washington, D.C., in conjunction with the next RTCA meeting.
A fully autonomous, unmanned aerial vehicle (UAV)-based system for locating GPS jammers, currently under development, seeks to localize a jammer to within 30 meters in less than 15 minutes in an area comparable to that of an airport. Ultimately, the design team targets the ability to locate multiple, simultaneous jammers, and navigate in intermittent GPS and GPS-denied environments using a combination of GPS and alternate navigation aids. The system should be inexpensive and built from commercially available or open-source parts and software.
By James Spicer, Adrien Perkins, Louis Dressel, Mark James, Yu-Hsuan Chen, Sherman Lo , David S. De Lorenzo and Per Enge, Stanford University
The aviation community worries about GPS jamming. Recently, it struggled to find so-called personal privacy devices on Newark’s Liberty International Airport and traveling the nearby New Jersey Turnpike.
A number of unintentional jamming incidents took a long time to resolve. The disruption from an intentional, malicious jamming attack could be far worse. Airport authorities should be prepared to locate and shut down a coordinated attack by numerous jammers capable of disrupting the GPS service over an entire airport.
The closure of a major airport for the many hours or days it would take to locate even a couple of backpack-sized transmitters would be not only be highly disruptive in flights delayed or diverted, it would negatively impact the confidence of the flying public.
Any system in place to mitigate this threat must be inexpensive enough to be deployed at least at the nation’s major commercial airports, autonomous enough to be operable with limited training and certification, and rapid and accurate enough that a jammer can be routinely apprehended by ground-based law enforcement. It must be able to navigate successfully in GPS-denied environments using alternative position, navigation and timing (APNT), and have the range and capacity to search an airport-sized area as well as the approach corridor leading to runway touchdown.
This article describes such a system and device presently in research and development: the Jammer Acquisition with GPS Exploration & Reconnaissance (JAGER).
Vehicle Design and Operation
The JAGER UAV is a based on a commercially available, multi-rotor airframe modified to suit the mission specifications. The 1.2-meter diameter octocopter has a maximum takeoff weight of 11 kilograms (24.2 pounds), a top speed of 20 meters/second (m/s, 45 mph), and can fly unloaded for up to 30 minutes.
We have replaced the battery tray with our own carbon fiber design that allows us to carry 16 Ah of lithium polymer batteries for a maximum power draw of 4 kW. This extra capacity means that even with a 5-kilo experimental payload, the present craft can remain aloft for up to 15 minutes without recharging.
The payload plates are also custom-made from carbon fiber, and it is to these that the UAV’s experimental payloads are mounted (see FIGURES 1 and 2). One payload plate is flown at a time, and is secured on top of the airframe with a quick-release mechanism. This modularity allows for individual experiments to be mounted to their own payload plate and ground-tested before being secured to the UAV. Different experiments can be switched out rapidly for efficient use of battery capacity and flight time.
Figure 1. (A) Diagram of the payload plate showing regularly spaced mounting holes. (B) Plate with APNT experiment mounted. (C) Payload plate / experiment assembly secured atop JAGER UAV.Figure 2. Image of the vehicle showing the battery tray slung beneath the central body, the APNT experiment and payload plate secured on top, and the jammer-hunting antenna mounted at the front.
The plate itself also offers flexibility for component mounting. Regularly spaced, threaded holes across the plate mean components’ positions can be easily changed to find an optimal configuration. This can be particularly useful for minimizing interference between computers and noise-sensitive components such as antennas and magnetometers.
Software. We modified existing, open-source autopilot software to fly the mission. The craft is fully capable of completing a mission autonomously, but also can be taken over by a human pilot if necessary. A ground station also can be used to send commands to the octocopter, but is primarily used to monitor UAV location, battery life, and jammer belief state.
The autopilot software also has been adapted to communicate with various vehicle payloads. Experiments using APNT equipment, for example, pass their data to the autopilot, which will combine these signals with its own GPS data for accurate navigation in areas where the GPS signal might be intermittent or unreliable. In return, the autopilot can be used to pass data to experiments reliant on altitude, attitude, atmospheric pressure or location information.
The ground station monitors instruments’ data and status in real time. This not only allows for control of airborne experiments, but also straightforward ground testing. Synthetic autopilot data can be fed to an experiment to ensure that all systems are performing correctly before they are mounted on the vehicle for flight tests.
APNT Overview
Key to navigating in a GPS-denied environment is the use of signals from APNT networks for location determination. The proposed system should be able to navigate using any or all available APNT signals, and should weight each one according to its strength and reliability in order to formulate the most accurate estimate of both its own and the jammer’s position.
Here we describe the use of the universal access transceiver (UAT) and distance measuring equipment (DME) network for our APNT signals. The UAT signal has been implemented by the Federal Aviation Administration (FAA) in the United States as part of automatic dependent surveillance–broadcast (ADS-B), and is transmitted through a network of terrestrial ground stations.
The ADS-B network was only completed across the contiguous United States in 2014, so it is new compared to established cellphone networks. It is more comprehensive than many other terrestrial systems, so that coverage of most airports is guaranteed. While GPS reception requires an unobstructed view of the sky, UAT reception requires a direct line of sight to a transmitting tower. However, the flatness of terrain surrounding most airports as well as the UAV’s airborne vantage point ensures that UAT signals will probably be visible throughout most jammer-seeking missions.
The APNT equipment used for navigation by the JAGER UAV consists of UAT (978 MHz), DME (982 to 1213 MHz), and GPS (1575.4 MHz) antennas, a multichannel transceiver to combine the two signals, and a computer for data processing (see FIGURE 3). A dedicated lithium-ion battery powers the entire APNT payload. The current system does incorporate GPS to estimate the time offset, but future iterations of the system will derive time from sources other than GNSS so that true GPS-denied navigation is possible.
Figure 3. Schematic of the APNT configuration on board the JAGER UAV. Resulting location information is passed to the autopilot for navigation.
The UAT antenna receives multiple signals from visible ADS-B ground station transmitters. The transceiver combines these with a GPS timestamp, and the data is passed to the APNT computer for analysis. Based on knowledge of the absolute locations of the ADS-B antennas, the range of the vehicle from each antenna can be calculated, which in turn can be used to trilaterate the vehicle’s absolute position. This position is then passed to the autopilot for the UAV’s navigation, while the status of the equipment and signal strength are passed down to the ground for monitoring in real-time.
The necessity of using GPS signals as an accurate timing system is a current limitation, as navigation in GPS-denied conditions is clearly not possible while we are using GPS as a clock. As mentioned eariler, future designs will derive time from non-GNSS sources, such as chip-scale atomic clocks or the terrestrial ranging signals.
Carrying an onboard computer allows for real-time processing of the terrestrial alternative navigation signals. However, there are a few limitations to the use of these signals. First, the vertical position is difficult to calculate due to the geometry of terrestrial signals as well as the sparsity of visible station at low elevation. This is solved by using a baro-altimeter. Second, DME signals do not provide a pseudoranging function. Current work sponsored by the FAA is developing a DME pseudoranging capability. As the technology matures, we will improve the hardware and algorithm that can be integrated into future JAGER designs, resulting in lower weight and power overhead for the APNT payload.
Tracking Overview
GPS jammers do little more than emit signals in the GPS frequency range. Because the signals from GPS satellites are so weak by the time they reach the Earth, ground-based jammers do not have to be especially powerful to overwhelm GPS in their immediate vicinity. A jammer is no more than a ground-based radio-frequency source radiating within the GPS spectrum.
The JAGER system will autonomously locate the nearest beacon emitting electromagnetic signals at the target frequency: the GPS frequency in this scenario. Testing such a system is difficult due to the illegality of jamming the GPS signal within the United States. We instead test the system using a powerful Wi-Fi beacon as a proxy for the overpowering jammer. Excepting the target frequency, the procedure to locate the jammer is identical to the GPS case.
To receive the jamming signal, the front of the craft carries an antenna optimized to receive signals of the target wavelength; the current antenna has a 60° cone of maximum sensitivity. It is angled downward 30° from the horizontal, so that the craft can receive all signals from the horizon to 30° from vertical. This gives the UAV visibility over most of the space in front and underneath it. Like the other payload equipment on the vehicle, the antenna is secured with a fast-release mechanism so that it can be easily swapped out if necessary. For Wi-Fi tracking, we use a Yagi antenna with 60° beamwidth and 9 dBi gain. In upcoming trials, we will test different antenna configurations (such as dual antennas, small antenna arrays, and directional antennas augmented with omni-directional antennas) to determine benefits of these different layouts.
Signals from the antenna are passed into a module that converts the Wi-Fi data to serial, then from serial to USB. A single-board Linux computer with a quad-core processor then analyzes the signal data (see FIGURE 4). The hardware used to locate the jammer weighs 160 grams, so has negligible impact on the vehicle’s flight time or range.
Figure 4. Schematic of the tracking system on board the JAGER UAV. The resulting believed location of the target is passed to the autopilot.
To find the jammer’s location, the UAV performs a controlled yaw spin while recording the strength of the jamming signal. On the basis of the signal landscape surrounding the vehicle, the computer estimates the jammer’s location and sends a message to the autopilot instructing the craft to fly in that direction (or, more accurately, in a direction that optimally improves the ability of JAGER to find the jammer quickly). In return, the autopilot updates the tracking computer and ground station as to the vehicle’s position.
After moving a certain distance towards the jammer’s believed location, the craft repeats the spinning maneuver and starts the process again. Although rotating only the antenna might increase the speed of the operation, the energy required to carry the necessary antenna-rotation mechanisms for the duration of a flight is more than that needed to spin the entire craft.
The tracking algorithm is not as straightforward as gradient ascent or homing, and the vehicle will not always fly in the direction of greatest signal strength. The operational area is uneven, and may include buildings, towers, or airplanes, resulting in a complicated RF environment. Signals are scattered, diffracted and reflected, meaning that an algorithm that simply follows the strongest signal will not always converge on the actual jammer location.
To decide the optimal path from the vehicle’s present location to the jammer’s believed position, the tracking algorithm makes use of partially observable Markov decision processes (POMDPs). POMDPs model decision processes where the underlying state of the system (that is, the location of the jammer) is never completely known, and maintain a probability distribution over the set of all possible states.
The entire deployment area (an airport and its environs, for example) is split up into a square grid. For every possible combination of jammer and vehicle grid square locations, the signal strength and direction that would result is calculated offline prior to deployment and stored in a database on the tracking computer.
During the mission, the UAV records its own position and the sensed jamming signal’s strength and direction. The jammer location that would correspond to this result is retrieved from the database, as well as a measure of the strength of this belief state.
Once the craft has a belief as to the location of the jammer, it moves to a new location in the jammer’s believed direction before taking another measurement of signal strength. The new location and new measurement are combined, and the updated corresponding jammer location is retrieved from the database. This process is repeated until the vehicle believes itself to be right above the jammer, at which point a photograph is taken, the ground station is notified, and the hunting mission is complete.
Having found the jammer, the system can be programmed to execute a wide range of operations. These include reporting coordinates and a live image of the believed jammer location back to the ground station, hovering above and tracking the jammer if it begins to move, landing at the jammer site, or returning to base.
We calculate and store the POMDP decisions in advance of the flight. This strategy has some advantages. First, it allows for almost instantaneous decision-making. This is because the algorithm’s decisions are based solely on the vehicle’s current location and sensory observations and not on any previous states (a defining characteristic of a Markov decision process). The craft needs only to observe its current state in order to look up its next move in the database. This enables rapid tracking in flight.
A second advantage is that safety checks can be pre-programmed into the database in advance of deployment. While JAGER is programmed to move towards the grid square believed to contain the jammer, it can also be programmed to avoid or take special precautions when moving towards or in the vicinity of certain squares in the grid (also called geo-fencing). In an airport situation, for example, the vehicle would avoid moving into the square containing a control tower or ground-based antenna, or would fly at a minimum altitude over buildings and taxiways to avoid collisions.
Finally, the integration between the autopilot and the tracking software can provide other important safeguards: in the proof-of-concept system, any navigation decision taken by the software can be relayed to the ground for human verification before the UAV begins to move. This supervised mode of operation lends itself to a seamless migration path to fully autonomous operation (always overseen by a human operator).
However, one disadvantage of calculating and storing decisions in advance is the storage space needed on the vehicle. Because the result of every possible combination of vehicle and jammer locations within the grid is calculated, the size of the database grows quickly with increasing numbers of possible positions (and states). The larger the grid or the greater the required accuracy, the more space is needed to store the database. With current algorithms, the database needed to locate a jammer to within 30 meters in an area the size of an airport requires 15 gigabytes of storage space, resulting in longer lookup times during flight.
We are considering several strategies to mitigate this disadvantage, including better compression, more effective search algorithms, and uploading from a ground server only the parts of the database that correspond to the vehicle’s current operational area. Another strategy is to use an adaptive mesh that changes in resolution depending on the jammer’s belief state: at low certainty the database resolution is low, but increases in the appropriate area as the jammer’s location becomes more certain.
Another disadvantage of pre-solving the decision-making process is that the system must be reconfigured for every site in which it is deployed. The specifications of the tracking algorithm will change depending on the requirements of the operating area. The grid size, shape and absolute location must change to suit the area being protected. The resolution of the grid depends on the required accuracy of the tracking system, and restricted or prohibited locations must suit the terrain, buildings and geological features of the deployment space. For example, a lead JAGER vehicle could be adapted and tested to suit a particular airport, and then the bespoke algorithm and database uploaded to backup vehicles in that airport’s fleet.
APNT Performance
During the Joint Interagency Field Experimentation (JIFX) event at Camp Roberts, California, in November 2014, we tested the APNT system by deploying the vehicle with GPS, UAT and DME antennas simultaneously recording data. GPS receivers on the ground were used to collect reference measurements to estimate the time of transmission of the signals from the APNT sites. All signals were recorded at an altitude of 275 meters above ground level (600 meters above sea level), at four different points roughly 800 meters apart, and the data analyzed for comparison. As expected, the UAT broadcast was noisier than the GPS signal. However, it was possible to calculate a range from the UAT data that was accurate to within 16.6 meters of the GPS reference position, well within the 30 meters error bound specified in the project specification (see FIGURE 5).
Figure 5. UAT range deviates from GPS derived range-estimate by an average of only 16.6 meters throughout the duration of the test flight.
While UAV navigation using APNT was done offline in post-processing for these tests, with planned algorithm improvements and hardware acceleration the UAT signal can be used to get real-time position information nearly as accurate as that from GPS. Thus the JAGER UAV can be navigated with comparable reliability in both GPS and GPS-denied environments.
Terrestrial APNT signals will be received at a wide range of power levels. This effect is not observed with the GPS network, as the different satellite signals are broadcast from such a great distance that any differences in received signal strength are relatively small by the time they reach Earth. For terrestrial networks, signals from transmitters close to the receiver can be many times stronger than those further away, which can result in two issues: 1) interference where one signal overwhelms another, and 2) inability to process a signal if the receiver does not have adequate dynamic range to capture strong and weak signals clearly.
This problem was observed in our tests, as we were receiving two signals: one 13.7 kilometers (DME) and the other 43.5 kilometers (ADS-B UAT) from our test site. Calculating accurate ranging estimates from the two required determining a gain setting that had dynamic range adequate for receiving both signals clearly.
Vehicle Performance
During experimental testing, the vehicle itself also underwent rigorous assessment of its performance under different conditions. Due to the delicate and often expensive nature of the payloads and experiments made possible by the JAGER platform, it is essential that the vehicle perform as expected, and that there are multiple procedures in place to protect the payloads in case of vehicle failure.
Because the open-source autopilot had never been used with such a large vehicle, we first ground-tested the craft’s flight control and stability. The vehicle was tethered and constrained to move in only one axis, and ropes were used to control its roll. While altering autopilot variables controlling roll and pitch feedback loops, we measured the vehicle’s response to impulsive disturbances and the time taken for it to right itself when upset. In this way we could tune the control gains and verify that the vehicle would be exceptionally stable during flight in even the most challenging atmospheric conditions. While we preferred to fly in the early morning hours to exploit clear air and lower winds, we did perform tests with momentary gusts of up to 7 m/s during envelope expansion flights.
We tested the vehicle with two accelerometers on board to measure how the rotors’ vibrations affected the rest of the craft. One accelerometer was attached to the airframe itself, while the other was secured to the payload plate. A comparison of the acceleration data recorded by the two instruments revealed that the payload plate experienced significantly less vibration than the airframe during flight, and both measurements remained well within the tolerances advised by the airframe manufacturer.
Two crucial flight modes also were tested before payloads were flown on the vehicle. Both altitude-control mode and position-control mode were tested to ensure that they could precisely constrain respectively the vehicle’s altitude and absolute position in a range of atmospheric conditions. Results showed that in altitude control mode, the vehicle’s z-coordinate was held constant to within ± 0.5 meters. In position control mode, its x- and y-coordinates remained within ± 1.0 meters (or a single vehicle length).
The success of the JAGER tracking mission also depends on accurate position measurements from the UAV. Operators must be confident in the vehicle’s position, so that ground forces can easily apprehend the located jammer, and also so that there is confidence in the success of safety protocols including geo-fencing, no-fly zones and minimum flight altitudes.
In addition to the geo-fencing and flight precautions taken by the tracking algorithm, the JAGER UAV has several other safety procedures executed automatically by the autopilot. A non-catastrophic error in the flight systems or payload is transmitted to the ground station for human troubleshooting, and commands can be sent to the vehicle as to how to proceed.
Finally, should we continue operations and allow its batteries to get sufficiently low, the vehicle will automatically return to launch site for landing and battery replacement. A catastrophic failure such as the loss of a motor will result in an immediate controlled landing. The craft can also be commanded from the ground station to land or return to launch, and can be taken over by a human pilot at any time.
Other tests verified that the vehicle has the range and endurance to be successful when deployed in an airport setting. When fully loaded with APNT and tracking payloads, the UAV exhibited a top speed of 10 m/s, enough to cover the length of an A380-capable runway in less than 5 minutes. A 20-minute flight endurance means that even including hovering during jamming signal observations by the tracking antenna, the JAGER system can hunt easily and effectively throughout an airport-sized area. Furthermore, we continue to explore techniques to improve dash capability, including reducing the weight of the APNT payload, and we anticipate describing results of these efforts in future reports.
Electromagnetic Interference
Because of the payload tray’s small area (0.5 m2), electromagnetic interference (EMI) between APNT components was a significant issue during testing. The GPS and UAT receivers are extremely sensitive to interference from other sources emitting in the frequency ranges to which they are tuned. The APNT computer, by contrast, is composed of various processors, clocks, drives and power boards that emit powerful electromagnetic noise at a wide range of frequencies as a byproduct of their normal operation.
The size and mass of the APNT computer board meant that it had to be mounted in the center of the payload tray to avoid unbalancing the UAV. That left a maximum 7 centimeters of space around the computer on which to mount the two antennas (see FIGURE 6). With no shielding, the EMI from the computer proved powerful enough to completely overwhelm the GPS, UAT and DME network signals, making navigation and position estimation using any network impossible.
Figure 6. Diagram showing the APNT experimental payload, and the proximity of the EMI-radiating CPU to numerous antennas.
The EMI problem was solved in three ways. Masts were used to raise the receiving antennas to a height of 19 centimeters above the payload tray, the maximum height at which a mast collapse wouldn’t cause catastrophic rotor and vehicle failure.
The antennas also were moved around the edge of the payload tray so as to be furthest from the system components radiating at their particular frequency. Two devices that proved particularly problematic were the solid-state hard drive in the CPU and the telemetry radio antenna, which radiated EMI that interfered with the GPS and UAT frequencies respectively. This was solved by moving the telemetry antenna to the underside of the craft, and the GPS antenna to the far side of the payload plate from the hard drive. The flexible design of the payload plate described earlier ensured that the relocation and testing of components was a straightforward process.
Shielding, however, proved to be the most important factor in eliminating EMI. Custom-made copper shields were added to the two masts to shield the antennas from the computer below them while still allowing an unobstructed view of the sky (see PHOTO). We tested numerous shielding iterations, including wire meshes and aluminum and lead foils; however; all were ineffective due to the strength and wide range of EMI wavelengths emitted. Finally, the computer itself was covered in a 2-millimeter layer of copper and 1-millimeter steel sheet. This combination struck the best balance between effectiveness and weight: aluminum was light but proved ineffective at shielding, while lead was very effective at EMI shielding but was too heavy for the UAV to carry.
The APNT payload prior to installation of the DME antenna. The copper shielding on the CPU and antennas can be clearly seen.
Conclusions
The development of the JAGER system contributes to U.S. preparation for a GPS jamming attack on civil aviation. While the first iteration described here is a significant improvement on previous jammer-hunting systems, future iterations of the JAGER UAV will be able to successfully navigate in a GPS-denied environment using alternative navigation signals including UAT and DME, and broadcast an accurate estimate of their position down to the ground.
The use of an octocopter flight system gives speed, maneuverability and sensory perception that far exceed any ground-based tracking effort. A fully loaded top speed of 10 m/s and almost instantaneous direction changes allow for efficient hunting over an airport-sized area and the location of a GPS jammer to within 30 meters, within a 20-minute flight endurance.
As the JAGER system can be entirely assembled from commercially available or open-source components and operates entirely autonomously, the system provides a low-cost, readily obtainable solution to the problem of GPS jamming. This means that it can be deployed quickly and is operable without extensive prior training.
The integration of autopilot, APNT navigation and tracking systems also allows for comprehensive monitoring and control of the UAV from the ground. Telemetry and data links to the ground station provide real-time updates as to the craft’s position, the jammer’s believed location and the status of all systems and instruments running on the vehicle. Safety protocols implemented in the software ensure that there is no risk of collision with site buildings, vehicles or personnel.
JAGER’s modular design gives operators extensive flexibility in situations that are capable of being successfully resolved by the system. The switching of equipment and software to allow the UAV to use GPS navigation to hunt a UAT or DME jammer, for example, could be effected in a matter of seconds.
The JAGER system also provides a reliable test platform for any experiment that requires airborne operation. The exceptional stability of the airframe combined with extended flight time, high top speeds and pinpoint positioning lends the system to a wide variety of applications beyond jammer tracking, including network monitoring, atmospheric experiments and biological research.
Manufacturers
The JAGER UAV airframe is a S1000 octocopter by DJI Innovations, Shenzhen, China; the flight batteries are a 8000 mAh model by Hextronik, Dongguan, China; the autopilot hardware and GPS antenna is a Pixhawk by 3D Robotics, Inc., San Diego, California; the autopilot software is based on PX4 by Pixhawk.org. The JAGER navigation GPS is made by u-blox, and the receiver for the APNT clock is made by Trimble. The UAT hardware includes an ASR-2300 multichannel transceiver by Loctronix Corporation, Woodinville, Washington; the tracking hardware comprises a 2.4 GHz Yagi antenna from L-com, North Andover, Massachusetts; an RN-XV Wi-Fi module by Roving Networks, Chandler, Arizona; and an Odroid-U3 computer by Hardkernel Co., Gyeonggi, South Korea.
James Spicer is pursuing concurrent bachelor’s and master’s degrees in aeronautics and astronautics at Stanford University.
Adrien Perkins is a Ph.D. candidate in aeronautics and astronautics at the Stanford University GPS Laboratory. He received his undergraduate degree in mechanical aerospace engineering at Rutgers University.
Louis Dressel is a graduate student at Stanford University. He received his undergraduate degree in aerospace engineering from Georgia Tech, with a minor in computer science.
Mark James is a master’s student in aeronautics and astronautics at Stanford University.
Yu-Hsuan Chen is a research associate at the Stanford GPS Laboratory. He received his Ph.D. in electrical engineering from National Cheng Kung University, Taiwan.
Sherman Lo is a senior research engineer at the Stanford GPS Laboratory.
David S. De Lorenzo is a principal research engineer at Polaris Wireless and a consulting research associate to the Stanford GPS Laboratory.
Per Enge is a professor of aeronautics and astronautics at Stanford University, where he is the Vance D. and Arlene C. Coffman Professor in the School of Engineering. He directs the Stanford GPS Laboratory.
U.S. Transportation Secretary Anthony Foxx today announced a significant NextGen milestone with the completion of En Route Automation Modernization (ERAM), a highly advanced computer system used by air traffic controllers to safely manage high-altitude traffic.
ERAM was designed to be the operating platform for NextGen technologies, including the Automatic Dependent Surveillance-Broadcast (ADS-B) system. ADS-B transmits information about altitude, airspeed and location derived through GPS from an equipped aircraft to ground stations and to other equipped aircraft in the vicinity. Air traffic controllers use the information to “see” participating aircraft in real time with the goal of improving traffic management.
“Looking at the future of air travel, we know that there will be more planes in our skies and more people in our airports, and in order to meet this challenge we must integrate cutting-edge technology into our aviation system,” said Secretary Foxx. “ERAM is a major step forward in our relentless efforts to develop and implement NextGen. With this new technology, passengers will be able to get to their destinations, faster, safer, and have a smoother ride — all while burning less fuel to get there.”
ERAM is the backbone of operations at 20 of the Federal Aviation Administration’s (FAA’s) en route air traffic control centers. The system, a crucial foundation for NextGen, drives display screens used by air traffic controllers to safely manage and separate aircraft.
“ERAM gives us a big boost in technological horsepower over the system it replaces,” said FAA Administrator Michael Huerta. “This computer system enables each controller to handle more aircraft over a larger area, resulting in increased safety, capacity, and efficiency.”
The first ERAM system went online at Salt Lake City Center in March 2012. The final installation was completed last month at New York Center.
ERAM uses nearly two million lines of computer code to process critical data for controllers, including aircraft identity, altitude, speed, and flight path. The system almost doubles the number of flights that can be tracked and displayed to controllers.
Other NextGen technologies include:
Automatic Dependent Surveillance-Broadcast (ADS-B): The FAA is moving steadily toward replacing the old system of ground-based radars to track aircraft with one that relies on satellite-based technologies, including GPS. ERAM already receives information from aircraft equipped with ADS-B and displays that data on controllers’ screens. This technology has made it possible for controllers to provide radar-like separation to aircraft that previously operated in areas where no radar is available, such as the Gulf of Mexico and large parts of Alaska. ADS-B will replace radar as the primary means of tracking aircraft by 2020.
Performance Based Navigation (PBN): Controllers are already using ERAM to make use of Performance Based Navigation (PBN) procedures that enable controllers and flight crews to know exactly when to reduce the thrust on aircraft, allowing them to descend from cruising altitude to the runway with the engines set at idle power, saving on flying time and fuel consumption.
Data Comm: To reduce congestion on radio frequencies, the FAA and the aviation industry continue to develop Data Comm, which will allow controllers and pilots to communicate by direct digital link rather than voice, similar to text messaging. ERAM is already equipped to handle this technology.
Secretary Foxx and Administrator Huerta attributed the success of the development and installation of ERAM to the collaboration between FAA management and labor, including the National Air Traffic Controllers Association (NATCA) and the Professional Aviation Safety Specialists (PASS). This collaborative process is now a blueprint that will be applied to the rollout of future technologies.
The Association for Unmanned Vehicle Systems International (AUVSI) will host Unmanned Systems 2015, which will run from May 4-7 at the Georgia World Congress Center Atlanta. With 8,000 attendees from around the world, Unmanned Systems 2015 is the largest expo and trade show in the industry, according to AUVSI.
The event will include three days of interactive exhibits and exciting demonstrations of air and ground vehicles spread throughout 350,000 square feet of exhibit space. The 150-plus educational sessions, workshops, and panel presentations will focus on the future of commercial, humanitarian, environmental, governmental, and military applications for robotics and unmanned systems. For more information on the sessions, see AUVSI’s Program Planner.
This year’s conference will feature keynote addresses and panel discussions by:
Colin Guinn, chief robotics officer, 3D Robotics
Dave Vos, project lead, Project Wing @ GoogleX
David Vigilante, senior vice president, legal, CNN
Helen Greiner, CEO, CyPhy Works
Hugh Herr, MIT professor and head of biomechatroinics at the MIT Media Lab
Rep. Frank Lobiondo, New Jersey House of Representatives
Henrik I. Christensen, distinguished professor, KUKA chair of Robotics, and director of the Robotics & Intelligent Machines Center, Georgia Tech
For more information and a full agenda of events, visit www.auvsishow.org.
Cobham AvComm, formerly the Aeroflex AvComm business unit, has introduced the ATC-5000NG NextGen ATC/DME Test Set.
Designed for engineering development, design validation, manufacturing and return-to-service test applications, the ATC-5000NG is the replacement product for the legacy SDX-2000 and the ATC-1400A/S-1403DL. The software defined radio architecture supports more transponder RTCA DO-181E test capability than the legacy products did and has new capability needed to support the Federal Aviation Administration’s NextGen test requirements including ADS-B (RTCA DO-260B) and UAT (RTCA DO-282).
ADS-B is the Automatic Dependent Surveillance-Broadcast for next-generation (NextGen) aircraft navigation. The FAA has mandated that aircraft operating in airspace that now requires a Mode C transponder must be equipped with ADS-B Out by Jan. 1, 2020.
“We are excited to introduce the new ATC-5000NG which offers our customers the most comprehensive test set available in the market today. This will help our customers prepare for new requirements driven by the FAA’s NextGen and Europe’s SESAR projects,” said Ryan Panos, vice president and general manager of Cobham AvComm.
In September 2014, Cobham completed its acquisition of Aeroflex for $1.46 billion.
In the latest update of its Motion Tracker product portfolio, Xsens has added active heading stabilization (AHS) to its core sensor fusion algorithms on the MTi 10-series and MTi 100-series. Both series are MEMS-based inertial measurement units (IMU), attitude and heading reference systems (AHRS), and vertical reference units (VRUs).
The AHS algorithm delivers fundamentally improved heading tracking accuracy, Xsens said. The improved robustness in heading tracking is particularly evident in Xsens’ line of vertical reference units (MTi-20 and MTi-200). These products now provide actively stabilized heading tracking, delivering 20x less drift than pure gyroscope dead reckoning for most application scenarios. This means heading tracking drift as low as 1 degree after one hour for many applications, while remaining fully immune to magnetic distortions.
Xsens said this characteristic makes the MTi line of products a highly accurate, but cost-effective solution for robotic/indoor navigation, camera stabilization, satellite communication, directional drilling, borehole/pipeline inspection and pedestrian navigation applications, Xsens said.
“Customers are already choosing our MTis because of their accurate heading tracking capabilities, but this algorithm will bring the accuracy to a whole new level, enabling more applications and creating new markets. The 12 cm2 MTi comes with an easy-to-use library, so that integrating the solution is straight-forward,” said Marcel van Hak, Product Manager of Industrial Applications for Xsens.
AHS is available immediately as a free firmware upgrade to all MTi customers as part of the just-released MT Software Suite 4.3.
The following video shows a demonstration of the Active Heading Stabilization, with the Xsens MTi is mounted on a robotic vacuum cleaner.
