Q: How can the safety, security, and full utility of GNSS applications be ensured while evolving to the best and most efficient use of limited and extremely valuable electromagnetic spectrum?
Mitch Narins, principal consultant, Strategic Synergies, LLC
A: (1) Agree that “No electromagnetic spectrum use will be approved, now or in the future, that impacts GNSS PNT users.” – a common mission statement essential to establishing trust!
(2) Determine how best to migrate today’s GNSS PNT users to be more resilient to both interference and planned future adjacent band services.
(3) Provide detailed architectures, network layouts, and implementation plans for rollout of new adjacent band services compliant with (1) and supportive of (2).
A: We cannot ignore fielded legacy systems, but neither can we chain ourselves to old technology and hinder progress.
Spectrum usage cannot be solved by less regulation, but it can be with innovative regulatory ideas adhering to minimalist principles. For example, would a “cash for clunkers” program work to eliminate weak receivers from the field to enable more efficient spectrum use?
This is one of those situations where government involvement can spur an innovative solution.
Tersus GNSS Inc. has released a major upgrade to its Precis-BX306 RTK board with new and improved GPS/GLONASS functionality. Tersus GNSS is a manufacturer of high-precision GNSS real-time kinematic (RTK) boards, receivers and systems.
Precis-BX306 Board Easy Kit.
The new version of Precis-BX306 supports up to 20-Hz RTK solution and raw measurement output, which can be integrated with autopilots and inertial navigation units.
With improved algorithms, the new Precis-BX306 demonstrates its ability that the 30-km baseline can be fixed quickly, the company said.
Moreover, the dynamic fix rate shows an advantage comparing to the majority of competitive solutions. Stable fix rate is achieved when it is working under city valley, tree, and other challenging environment.
“The technology changes made in this version give a whole new user experience for our customers,” said Xiaohua Wen, founder and CEO. “With the updated capabilities, the Precis-BX306 is particularly useful for drones, surveyors and geographic information system data users interested in an affordable RTK solution.”
This latest version of Precis-BX306 is pin-to-pin compatible with major GNSS boards in the market, offering a flexible interface. Event mark and PPS are supported as always. All enhanced features demonstrate Tersus’ commitment to the needs of customers who value dynamic accuracy and stability.
The new Precis-BX306 is available for order and delivery immediately, and it will be shown at Intergeo in Berlin, Germany, Sept. 26-28.
DOT serves as the civil lead for the GPS and chairs the CGSIC in this capacity. NAVCEN is assigned duties as deputy chair and executive secretariat for the CGSIC.
Subcommittees of the CGSIC for Timing, State and Local Government, International Information, and Survey, Mapping and Geosciences will hold meetings Sept. 25, and a summary of these meetings will be presented to the CGSIC plenary session Sept. 26.
The keynote speaker for this year’s plenary session will be Keith Conner, Ph.D., Senior Engineer, Science and Technology First Responders Group, U.S. Department of Homeland Security.
Presentations include:
Operational status and modernization of the GPS constellation of satellites
U.S. Space-Based Position, Navigation and Timing policy
GPS augmentation systems
Briefings from the National Aeronautics and Space Administration (NASA) and the National Parks Service
Information related to U.S. engagement with other international Global Navigation Satellite Systems as well as a variety of applications of the use of GPS
The full agenda is available. CGSIC presentations will be posted online shortly after the meeting ends.
In Portland, Oregon, and in Berlin, Germany, the two largest and most important international conferences on GPS, GNSS, PNT, survey, mapping and geodesy take place this year on exactly the same dates — just 5,177 miles apart. Now that’s bad timing. Our strategy is to divide our forces and send key personnel to interact with industry leaders at each gathering — to bring you the news and developing stories you need to keep on the forefront of change.
If you’re at ION GNSS+ or Intergeo, look for these faces, come up and introduce yourselves. We want to talk with you! If you’re not fortunate enough to attend either conference, look to our website, newsletters and this magazine for product launches, videos and in-depth stories filed from the developing frontiers of PNT. We’ll be reporting !!Live!! and for weeks, even months, to come.
Attending Intergeo in Berlin:
Burch
Barwacz
Joyce
Gerard
Tim Burch is our survey editor; in his day job he’s a professional surveyor and board of directors secretary of that profession’s national society.
Allison Barwacz is digital media content producer for North Coast Media (NCM, that’s us) with a passion for videography and writing.
Mike Joyce and Ryan Gerard, senior account manager and account manager, respectively, work closely with our marketing partners, who make this magazine and multi-media communications channel possible.
Attending ION GNSS+ in Portland:
Stoltman
Whitford
Mitchell
Cozzens
Harms
Sabau
Limpert
Cameron
Langley
Kevin Stoltman is founder and president of NCM, with a distinguished career in business-to-business publishing.
Marty Whitford is editorial director and publisher; earlier, he actually worked at GPS World and attended ION-GNSS 2004.
Michelle Mitchell is account manager for GPS World and senior marketing and event manager for NCM. She knows the GPS industry landscape and players extremely well.
Tracy Cozzens is our managing editor, with her hands on all the controls.
Joelle Harms is an award-winning digital media manager, focused on content planning and creation.
Joe Sabau is an account manager with a keen eye for market trends.
Kelly Limpert is a digital media content producer developing a strong online and social media presence for all of our partners.
Richard Langley is GPS World’s innovation editor and a professor at the University of New Brunswick.
A GPS-lidar fusion technique implements a novel method for efficiently modeling lidar-based position error covariance based on features in the point cloud. The fusion uses a three-dimensional (3D) city model to detect and eliminate non-line-of-sight (NLOS) GPS satellites to improve global positioning.
The technique has potential application in UAV missions such as 3D modeling, filming, surveying, search and rescue, and package delivery.
By Akshay Shetty and Grace Xingxin Gao, University of Illinois
Unmanned aerial vehicles (UAVs) commonly rely on GPS for continuous and accurate outdoor position estimates. However, in certain urban scenarios, additional onboard sensors such as light detection and ranging (lidar) are desirable due to errors in GPS measurements. To fuse these measurements it is important, yet challenging, to accurately characterize their error covariance. We propose a GPS-lidar fusion technique with a novel method for efficiently modeling the position error covariance based on surface and edge features in point clouds. We use the lidar point clouds in two ways: to estimate incremental motion by matching consecutive point clouds; and, to estimate global pose (position and orientation) by matching with a 3D city model. For GPS measurements, we use the 3D city model to eliminate NLOS satellites and model the measurement covariance based on the received signal-to-noise-ratio (SNR) values. Finally, all the above measurements and error covariance matrices are input to an unscented Kalman Filter (UKF), which estimates the globally referenced pose of the UAV. To validate our algorithm, we conducted UAV experiments in GPS-challenged urban environments on the University of Illinois at Urbana-Champaign campus.These experiments demonstrate a clear improvement in the UAV’s global pose estimates using the proposed sensor fusion technique.
SITUATION
Emerging applications in UAVs such as 3D modeling, filming, surveying, search and rescue, and package delivery all involve flying in urban environments. In these scenarios, autonomously navigating a UAV has certain advantages such as optimizing flight paths and sensing and avoiding collisions. However, to enable such autonomous control, we need a continuous and reliable source for UAV positioning. In most cases, GPS is primarily relied on for outdoor positioning. However, in an urban environment, GPS signals from the satellites are often blocked or reflected by surrounding structures, causing large errors in the position output.
In cases when GPS is unreliable, additional onboard sensors such as lidar can provide the navigation solution. An onboard lidar provides a real-time point cloud of the surroundings of the UAV. In a dense urban environment, lidar can detect a large number of features from surrounding structures such as buildings.
Positioning based on lidar point clouds has been demonstrated primarily by applying different simultaneous localization and mapping (SLAM) algorithms. In many cases, algorithms implement variants of iterative closest point (ICP) to register new point clouds.
APPROACH
The main contribution of this article is a GPS-lidar fusion technique with a novel method for efficiently modeling the error covariance in position measurements derived from lidar point clouds. Figure 1 shows the different components involved in the sensor fusion.
Figure 1. Overview of sensor fusion architecture.
We use the lidar point clouds in two ways: to estimate incremental motion by matching consecutive point clouds; and, to estimate global pose by matching with a 3D city model. We use ICP for matching the point clouds in both cases.
For the lidar-based position estimates, we proceed to build the error covariance model depending on the surrounding point cloud. First, we extract surface and edge feature points from the point cloud. We then model the position error covariance based on these individual feature points. Finally, we combine all the individual covariance matrices to model the overall position error covariance ellipsoid.
For the GPS measurement model, we use the pseudorange measurements from a stationary reference receiver and an onboard GPS receiver to obtain a vector of double-difference measurements. Using the double-difference measurements eliminates clock bias and atmospheric error terms, hence reducing the number of unknown variables. We use the global position estimate from the lidar-3D city matching to construct LOS vectors to all the detected satellites. We then use the 3D city model to detect NLOS satellites, and consequently refine the double-difference measurement vector. We create a covariance matrix for the GPS double-difference measurement vector based on SNR of the individual pseudorange measurements.
We implement a UKF to integrate all lidar and GPS measurements. Additionally, we incorporate orientation, orientation rate and acceleration measurements from an onboard inertial measurement unit (IMU). Finally, we test the filter on an urban dataset to show an improvement in the navigation solution.
LIDAR-BASED ODOMETRY
ICP is commonly used for registering three-dimensional point clouds. It takes a reference point cloud q, an input point cloud p, and estimates the rotation matrix R and the translation vector T between the two point clouds. Different variants of the algorithm generally consist of three primary steps.
Matching. This involves matching each point pi in the input point cloud, to a point qi in the reference point cloud. The most common method is to find the nearest neighbors of each point in the input point cloud. For our application, a kDtree performs best since the two point clouds are relatively close to each other.
Defining Error Metric. This defines the error metric for the point pairs. We choose the point-to-point metric, which is generally more robust to difficult geometry than other metrics such as point-to-plane. The total error between the two point clouds is defined as follows:
(1)
where N is the number of points in the input point cloud p.
Minimization. The last step of the algorithm is the minimization of the error metric with regard to the rotation matrix R and the translation vector T between the two point clouds.
We use ICP to estimate the incremental motion of the lidar between consecutive point clouds. Figure 2 shows our implementation of ICP to estimate the lidar odometry.
Figure 2. The input to ICP is a reference point cloud q and an input point cloud p as shown in (a). The algorithm calculates the rotation matrix R and the translation vector T such that the error metric E is minimized. (b) shows the reference point cloud q and the transformed input point cloud R • p + T.
MATCHING LIDAR, 3D MODEL
We generate our 3D city model using data from two sources: Illinois Geospatial Data Clearinghouse and OSM. The Illinois Geospatial Data were collected by a fixed-wing aircraft flying at an altitude of 1700 meters, equipped with a lidar system including a differential GPS unit and an inertial measurement system to provide superior global accuracy. Since the data were collected from a relatively high altitude, it primarily contains adequate details for the ground surface and the building rooftops. In order to complete the 3D city model, we need additional information for the sides of buildings. We use OSM to obtain this information. OSM is a freely available, crowd-sourced map of the world, which allows users to obtain information such as building footprints and heights. Figure 3 shows a section of the 3D city model for Champaign County.
Figure 3. Section of the point cloud for Champaign County dataset. (Left) shows the 3D city model using only the Illinois Geospatial Data. (Right) fhows the model after incorporating building information from OpenStreetMap.
To estimate the global pose of the lidar, we match the onboard lidar point cloud with the 3D city model using ICP, in these steps:
Use the position output from onboard GPS receiver as an initial guess. If position output is unavailable, use the position estimate from the previous iteration as an initial guess. For orientation, use the estimate from the previous iteration. Thus, we obtain an initial pose guess .
Project the onboard lidar point cloud pL to the same space as the 3D city model qcity using .
Implement ICP, to obtain the rotation RL and translation TL between the two point clouds. Use this output to obtain an estimate for the global pose .
Figure 4 shows the results of implementation of the above method. While navigating in urban areas, the GPS receiver position output used for the initial pose guess might contain large errors in certain directions. This might cause ICP to converge to a local minimum, depending on features in the point cloud pL generated by the onboard lidar.
Figure 4. Global pose estimation with the aid of 3D city model. (Left) shows the intial position guess (red dot, with term in red outlined box) and the onboard lidar point cloud pL projected on the same space as the 3D city model qcity. (b) shows the updated global position (green dot, with term in green outlined box) after the ICP step. We see an improvement in the global position, as the point cloud matches with the 3D city model.
To evaluate how our lidar-3D city model matching algorithm performs in such challenging cases, we test it in two different urban areas as shown in Figure 5. We begin by selecting a grid of initial position guesses up to 20 meters away from the true position. With an adequate distribution of features, ICP is able to correctly match the two point clouds and provide an accurate position estimate after matching. In contrast, when there’s an urban scenario with a relatively poor distribution of features, ICP is unable to estimate the position accurately.
Figure 5. Lidar-3D city model matching in two different urban areas. We begin with a grid on initial position guesses (red) around the true position (black). In (a) and (b), there are adequate features. The position estimates after matching (blue) converge to the true position. In (c) and (d) the feature distribution is relatively poor. The position estimates after matching (blue) are parallel to the building surface.
MODELING ERROR COVARIANCE
We model the lidar position error covariance as a function of the surrounding features. In urban environments, we typically observe structured objects such as buildings, hence we focus primarily on surface and edge features in the point cloud. We extract these feature points based on the curvature at each point. Points with curvature values above a threshold are marked as edge points, whereas points with curvature values below a threshold are marked as surface points. (For detailed discussion of the algorithms involved, see GPS-LiDAR_AkshayShetty-algorithms.
For each surface feature point, we first compute the normal by using 9 of the neighboring points to fit a plane. We model the error covariance ellipsoid with the hypothesis that each surface feature point contributes in reducing position error in the direction of the corresponding surface normal. Additionally, we assume that surface points closer to the lidar are more reliable than those further away, because of the density of points.
For each edge feature point, we first find the direction of the edge using the closest edge points in the scans above and below. We model the error covariance ellipsoid with the hypothesis that each edge feature point helps in reducing position error in the directions perpendicular to the edge vector. A vertical edge, for example, would help in reducing horizontal position error. Additionally, we assume that edge points closer to the lidar are more reliable than those further away, again because of the density of points. Figure 6 shows sample error covariance ellipsoids for a surface point and an edge point.
Figure 6. Position error covariance ellipsoid for surface and edge feature points. The exact sizes of the ellipsoids are tuned during implementation.
To obtain the overall position error covariance, we combine the error covariance matrices for all the individual surface and edge feature points. Figure 7 shows the combined covariance ellipsoid for two different scenarios. We observe that while passing through a corridor, the covariance ellipsoid is larger in the direction parallel to the building sides due to a poor distribution of features.
Figure 7. Overall position error covariance ellipsoids (black) for two point clouds (green). We combine the error ellipsoids from individual surface (red) and edge (blue) feature points.
GPS MEASUREMENT MODEL
We use pseudorange measurements from the GPS receiver to create the measurement model. To eliminate certain error terms, we use double-difference pseudorange measurements, which are calculated by differencing the pseudorange measurements between two satellites and between two receivers. Before proceeding to use the pseudorange measurements, we check if any of the satellites detected by the receiver are NLOS signals. We use the 3D city model mentioned earlier to detect the NLOS satellites. We use the position output generated by the lidar-3D city model matching to locate the receiver on the 3D city model.
Next, we draw LOS vectors from the receiver to every satellite detected by the receiver and eliminate satellites whose corresponding LOS vectors intersect the 3D city model. Figure 8 shows the above implementation in an urban scenario.
Figure 8. Elimination of NLOS satellite signals. LOS vectors are drawn to all detected satellites: SV3, SV14, SV16, SV22, SV23, SV26, SV31. The LOS vectors to satellites SV23 and SV31 intersect (red) the 3D city model and are eliminated from further calculations.
After eliminating the NLOS satellites, we select satellites that are visible to both the user and the reference receivers to create the GPS double-difference measurement vector and its covariance. We assume that the individual pseudorange measurements are independent, and that the variance for each measurement is a function of the corresponding SNR. We propagate the covariance matrix for the individual pseudorange measurements, to obtain the covariance matrix for the double-difference measurements.
GPS-Lidar Integration
In addition to using a lidar and a GPS receiver, we use an IMU on board the UAV. Figure 9 shows the experimental setup: the UAV designed and built by our research group. For the double-difference GPS measurements, we use a reference receiver within a kilometer of our data collection sites. We implement a UKF to fuse measurements from the sensors and estimate the global pose of the UAV.
Figure 9. Experimental setup for data collection. Our custom-made iBQR UAV mounted with a lidar, a GPS receiver and antennas, an IMU, and an onboard computer.
Position and orientation estimates from lidar and GPS are incorporated via the correction step of the filter, whereas the IMU measurements are included in the prediction step. For position corrections from lidar, we use our point cloud feature based model for the error covariance. For GPS double-difference measurements, we use the covariance based on the individual pseudorange measurement SNR.
We implement our algorithm on an urban dataset collected on our campus of University of Illinois at Urbana-Champaign. As shown in Figure 10, the GPS measurements and the GPS position output contain large errors, due to the presence of nearby urban structures. Here we stack all the double-difference measurements and compute the unweighted least square estimate of the baseline between the UAV and the reference receiver.
Figure 10. Position estimates from GPS measurements. The position output from the GPS receiver (blue) and the unweighted least-squares position estimate (red) contain large errors.
For the lidar measurements, we check the output from our incremental ICP odometry method and the lidar-3D city model matching algorithm. Furthermore, we implement an ICP mapping algorithm to check the performance of existing ICP-based methods on the dataset. In Figure 11, the ICP odometry method and the ICP mapping algorithm accumulate drift over the course of the trajectory. The lidar-3D city model matching algorithm does not drift over time; however, the position still contains errors in situations where the lidar does not detect enough number of points or the matching algorithm converges to a local minimum.
Figure 11. Position estimates from lidar point clouds. The incremental ICP odometry (green) and the ICP mapping (blue) estimates accumulate drift over time. The lidar-3D city model matching (yellow) does not drift over time, but contains errors where the ICP algorithm might converge to a local minimum.
Figure 12 shows the output of the filter for the same trajectory. The filter output estimates the actual path much more accurately than the individual measurement sources by themselves.
Figure 12. Position estimates from UKF, integrating GPS and lidar measurements. The filter position output (blue) resembles the actual trajectory, more accurately than any individual source of GPS or lidar measurements.
CONCLUSION
In summary, we proposed a GPS-lidar integration approach for estimating the navigation solution of UAVs in urban environments. We used the onboard lidar point clouds in two ways: to estimate the odometry by matching consecutive point clouds, and to estimate the global pose by matching with an external 3D city model. We built a model for the error covariance of the lidar-based position estimates as a function of surface and edge feature points in the point cloud. For GPS measurements, we eliminated NLOS satellites using the 3D city model and used the remaining double-difference measurements between an onboard receiver and a reference receiver. To construct the covariance matrix for the double-difference measurements, we used the SNR values for individual pseudorange measurements.
Finally, we applied an UKF to integrate the measurements from lidar, GPS and an IMU. We experimentally demonstrated the improved positioning accuracy of our filter.
ACKNOWLEDGMENTS
The authors would like to sincerely thank Kalmanje Krishnakumar and his group at NASA Ames Research Center for supporting this work under the grant NNX17AC13G.
The material in this article was first presented at the ION GNSS+ 2017 conference in Portland, September 2017.
MANUFACTURERS
The lidar used aboard the UAV in these tests is a Velodyne VLP-16 Puck Lite. The GPS receiver is a u-blox LEA-6T with a Maxtena M1227HCT-A2-SMA antenna. The IMU is an Xsens Mti-30, and the onboard computer an AscTec Mastermind 3a.
The iBQR UAV was designed and assembled by the authors.
AKSHAY SHETTY received an M.S. degree in aerospace engineering from University of Illinois at Urbana-Champaign. He is also pursuing a Ph.D. at the same university.
GRACE XINGXIN GAO received a Ph.D. degree in electrical engineering from Stanford University. She is an assistant professor in the Aerospace Engineering Department at the University of Illinois at Urbana-Champaign.
Even a GNSS receiver that can supply raw pseudorange and carrier-phase measurements now costs only a few hundred dollars, and in this month’s column, a couple of researchers from Down Under pit a couple of these receivers up against a couple of survey-grade receivers. Did this cheap receiver turn out to be a good thing?
By Robert Odolinski and Peter J.G. Teunissen
ALL GOOD THINGS ARE CHEAP; ALL BAD ARE VERY DEAR. That’s what the famous American essayist (and surveyor) Henry David Thoreau wrote in his diary on March 3, 1841. He was likely referring, in part, to the cheapness of the things he came across in nature such as birdsong or the plants and trees on the shores of Walden Pond and the dearness of some luxuries and comforts of civilization, which he tended to eschew. But what has that got to do with GPS, you might ask?
When they were first introduced in the late 1970s and early 1980s, GPS receivers were very dear. Many of them sold for anywhere from $50,000 to $250,000, which would be equivalent to about twice those amounts in today’s dollars. The first civilian receivers were large bulky affairs. As I documented in this column in April 1990 (“Smaller and Smaller: The Evolution of the GPS Receiver”), the “first commercially available GPS receiver was the STI-5010 built by Stanford Telecommunications Inc. It was a dual-frequency, C/A- and P-code, slow-sequencing receiver. Cycling through four satellites took about five minutes, and the receiver unit alone required about 30 centimeters of rack space. External counters, also requiring rack space, made pseudorange measurements. An external computer controlled the receiver and computed positions.” While it could be transported in a small truck (and some were), it was not designed for portability and ease of use by surveyors or geodesists.
Then, in 1982, Texas Instruments introduced the first relatively compact civil GPS receiver, the TI 4100, also known as the Navstar Navigator. And as I also noted in that column more than 15 years ago, this “receiver could make both C/A- and P-code measurements along with carrier-phase measurements on both L1 and L2 frequencies. Its single hardware channel could track four satellites simultaneously through a multiplexing arrangement. The 37 × 45 × 21-centimeter receiver/processor had a handheld control and display unit and an optional dual-cassette data recorder for saving measurements for post-processing. The unit, although portable, weighed 25 kilograms and consumed 110 watts of power (the receiver doubled as a hand warmer). Field operation required a supply of automobile batteries.”
My, how things have changed. Beginning around 1990, receivers steadily got smaller and smaller and cheaper and cheaper. Survey-grade GNSS (not just GPS) receivers can now be purchased for well under $10,000 and consumer-grade units sell for as little as a hundred dollars or less. And, of course, the GNSS modules inside smartphones and other devices cost manufacturers only a couple of dollars or so.
But even a GNSS receiver that can supply raw pseudorange and carrier-phase measurements now costs only a few hundred dollars, and in this month’s column, a couple of researchers from Down Under pit a couple of these receivers up against a couple of survey-grade receivers. Did this cheap receiver turn out to be a good thing?
Read on to find out.
GPS has been the number-one positioning tool for a range of applications during the past few decades. The integration of the emerging global navigation satellite systems, such as the Chinese BeiDou Navigation Satellite System (BDS), can give improved precise (millimeter- to centimeter-level) real-time kinematic (RTK) positioning. When BDS is combined with GPS, about double the number of satellites are visible in the Asia-Pacific region, which can make single-frequency RTK and low-cost receiver RTK positioning possible.
In this article, we will analyze the performance of L1 GPS + B1 BDS in Dunedin, New Zealand, using low-cost receivers. We compare their performance to that of L1+L2 GPS survey-grade receivers.
First, we describe the GPS+BDS functional and stochastic models and the data used for our evaluations. Least-squares variance component estimation (LS-VCE) is used as a means to determine the code and phase (co)variances to formulate a realistic stochastic model. (An incorrect stochastic model will deteriorate the ambiguity resolution and consequently the achievable positioning precisions.)
Having correctly defined the stochastic model, we focus on the positioning performance. We investigated the ambiguity resolution and positioning performance, both formally and empirically, for customary and high-elevation cut-off angles. The high cut-off angles are used to mimic situations when low-elevation multipath is to be avoided. Lastly, we compared all our results between using low-cost and survey-grade antennas.
GPS+BDS POSITIONING MODEL
The model that we used for positioning is given as follows. Assume that sG + 1 GPS satellites are tracked on fG frequencies and sB + 1 BDS satellites on fB frequencies. As we apply system-specific double-differencing (DD), one pivot satellite is used per system. The total number of DD phase and code observations per epoch then equals 2 fGsG + 2 fBsB. We assume for now that cross-correlation between frequencies as well as code and phase is absent. The combined multi-frequency short-baseline GPS+BDS model is then defined as follows.
The system-specific DD phase and code observation vectors are denoted as φ* and p*, respectively, with * = {G, B} where G = GPS and B = BDS. The single-epoch GNSS model of the combined system is given as
(1)
and
(2)
in which
is the combined phase vector,
is the combined code vector,
is the combined integer ambiguity vector,
is the real-valued baseline vector,
is the combined phase random observation noise vector,
is the combined code random observation noise vector, and
D[.] denotes the dispersion operator.
The entries of the baseline design and wavelength matrices are given as
where is the x 1 vector of 1s, is the differencing matrix, is the unit matrix, the geometry-matrices GG and GB contain the undifferenced receiver-satellite unit direction vectors for GPS and BDS, respectively, is the wavelength of frequency , denotes the Kronecker product, and “diag” and “blkdiag” indicate diagonal and block diagonal matrices, respectively. The entries of the positive definite variance matrices are given as
(3)
where , denote the phase and code standard deviation, respectively, and the satellite elevation-angle-dependent weight.
The model in Equation 1 applies to short baselines, and thus the ionospheric and tropospheric delays are assumed absent. The broadcast ephemerides are used to obtain the satellite coordinates. Further, the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) technique is used to estimate the integer ambiguities a. The observation noise vectors εand e, respectively, are zero-mean vectors, provided that no multipath is present in Equation 1.
EXPERIMENT SETUP
The GNSS receivers we used are depicted in FIGURE 1. Firstly, two low-cost single-frequency receivers were set up to collect L1+B1 GPS+BDS data for two days. These receivers cost a few hundred U.S. dollars. Since the patch antennas we used have been shown to have less effective signal reception and multipath suppression in comparison to survey-grade antennas, the receivers that collected data for two days were additionally connected to such antennas. These antennas have a cost of slightly more than US$1,000 per antenna. To compare the low-cost solution to a survey-grade receiver-solution, two such receivers (which cost several thousand U.S. dollars) were connected to the same survey-grade antennas through splitters and collected L1+L2 GPS data. A detection, identification and adaption procedure was used to eliminate any outliers.
FIGURE 1. Low-cost single-frequency receivers collecting GPS+BDS data for single-baseline RTK, with patch antennas (left) and survey-grade antennas (right) on Jan. 4–6 and Jan. 6–8, 2016, respectively. Survey-grade dual- frequency GPS receivers were connected to the same survey-grade antennas simultaneously to truly track the same GPS constellation.
FIGURE 2 depicts the corresponding redundancy of the two receiver models (that is, the number of observations minus the number of estimated unknowns) together with the number of satellites over 48 hours (30-second epoch interval). The number of BDS satellites (magenta lines) is overall smaller than when compared to GPS (blue lines) in Dunedin. However, Figure 2 also shows that the model strength of L1+B1 GPS+BDS, as measured by its redundancy, is almost similar to that of L1+L2 GPS except for some hours at the middle of the two days. This implies that the two receiver models can potentially give competitive RTK ambiguity resolution and positioning performance. This is however only true if the receiver code and phase observation noise would be of similar magnitude between the receivers used, hence the need for an analysis of the receiver observation precision.
FIGURE 2. Redundancy (left) and number of satellites (right) of L1+B1 GPS+BDS and L1+L2 GPS during Jan. 6–8, 2016, (48 hours) for an elevation cut-off angle of 10°.
In our receiver evaluations, we determined a set of reference ambiguities by using a known baseline and treating them as time-constant parameters over the two days in a dynamic model.
LOW-COST RTK POSITIONING
The code and phase variances were estimated by LS-VCE using data independent from the data used for the following positioning analysis. The variances are needed to formulate a realistic stochastic model, whereas an incorrect stochastic model will deteriorate the ambiguity resolution and consequently the achievable positioning precisions. TABLE 1 depicts the corresponding estimated standard deviations (STDs) used for our positioning models.
TAB LE 1. Zenith-referenced undifferenced code and phase standard deviations estimated by least-squares variance component estimation.
Table 1 shows that the code precision of L1 GPS and B1 BDS improves significantly when the survey-grade antennas are used instead of patch antennas (49 centimeters STD for L1/B1 that decreases to about 30 centimeters), due to their better signal reception and multipath suppression abilities. For testing our stochastic model, we used data that is independent from the data used to estimate the code/phase precision.
Positioning Performance. The single-epoch (instantaneous) RTK positioning results for 24 hours data are shown in FIGURE 3, with ambiguity-float solutions shown at the top and ambiguity-fixed solutions at the bottom. Only the correctly fixed solutions are depicted as determined by comparing the instantaneously estimated ambiguities to the set of reference ambiguities. The 95% empirical and formal confidence ellipses and intervals are shown in green and red, respectively. They were computed from the empirical and formal position variance matrices. The empirical variance matrix was estimated from the positioning errors as obtained from comparing the estimated positions to precise benchmark coordinates. The formal variance matrix used was determined from the mean of all single-epoch formal variance matrices.
FIGURE 3. Horizontal (north (N), east (E)) position scatter and corresponding vertical (U) time series of the float (top) and correctly fixed (bottom) L1+B1 GPS+BDS single-epoch RTK solutions for an elevation cut-off angle of 10°. The 95% empirical and formal confidence ellipses and intervals are shown in green and red, respectively. The 24 hour (30 second) period is 22:00-22:00 UTC Jan. 5-6, 2016, for patch antennas in (a) and 21:48-21:48 UTC Jan. 8-9, 2016, for survey-grade antennas in (b), which are periods independent of the periods used to determine the stochastic model through the code/phase STDs in Table 1.
Figure 3 shows a good fit between the formal and empirical confidence ellipses/intervals, which thus illustrates realistic LS-VCE STDs in Table 1 that were used in the stochastic model. Note also the two-order of magnitude improvement when going from float to fixed solutions, and that the low-cost receiver plus survey-grade antenna has the most precise ambiguity-float positioning solutions.
Ambiguity Resolution and Positioning Performance for Higher Cut-Off Angles. We subsequently investigated the low-cost L1+B1 GPS+BDS performance for high elevation cut-off angles, so as to mimic situations in urban canyon environments or when low-elevation-angle multipath is present and is to be avoided. We have made comparisons to the survey-grade L1+L2 GPS results. It has been shown that a good ambiguity resolution performance does not necessarily imply a good positioning performance, so we investigated what effect this has on our positioning models.
The following integer least-squares (ILS) success rates (SRs) are thus computed based on epochs with the condition of positional dilution of precision (PDOP) ≤ 10 and averaged over all epochs over two days of data. By including and excluding epochs with large PDOPs, we can show how the positioning performance of the different models is affected by poor receiver-satellite geometries. To better understand how this exclusion of epochs with large PDOPs also influenced the empirical ambiguity-correctly-fixed positioning performance, we constructed TABLE 2, which shows the corresponding positioning STDs for two days of data. These STDs were computed by comparing the estimated positions to precise benchmark coordinates. In addition to the positioning performance, we depict in Table 2 the corresponding empirical ILS SR for full ambiguity-resolution, which is given by the ratio of the number of correctly fixed epochs to the total number of epochs.
TABLE 2. Single-epoch empirical STDs (N, E, U) of correctly fixed positions for the three positioning models together with their ILS SR for four elevation cut-off angles and 48 hours of data (Jan. 4–6 and Jan. 6–8, 2016). The empirical STDs and ILS SRs are also shown when conditioned on PDOP ≤ 10.
Table 2 shows that the L1+B1 low-cost receiver plus patch antenna combination has (as expected) smaller SRs in comparison to those when the survey-grade antenna is used. This latter combination has comparable SRs to the (PDOP-conditioned) SRs of the survey-grade L1+L2 GPS receiver for cut-off angles up to 25°.
In support of better understanding Table 2, FIGURE 4 shows typical positioning results for the different receiver and antenna combinations with elevation cut-off angles of 10° (top two rows) and 25° (bottom two rows). The first and third rows show the local horizontal (N, E) positioning scatterplots and the second and fourth rows the vertical (U) time series over two days of data. The float solutions are depicted in gray, and incorrectly and correctly fixed solutions in red and green, respectively. The zoom-in is given to better show the spread of the correctly fixed solutions with millimeter-centimeter level precisions. The formal ambiguity-float STDs are also shown under the up time series to reflect consistency between the empirical and formal positioning results.
FIGURE 4. Horizontal (N, E) scatterplots and vertical (U) time series for L1+B1 low-cost receiver with patch antenna (first column) with 99.5% (89.8%) ILS SR, L1+B1 low-cost receiver with survey-grade antenna (second column) with 100% (97.8%) ILS SR, and survey-grade L1+L2 GPS (third column) with 100% (94.1%) ILS SR, using 10° (top two rows) and 25° (bottom two rows) cut-off angles respectively (Jan. 4–6, 2016, for low-cost receiver with patch antenna and Jan. 7–8, 2016, for the low-cost and survey-grade receivers with survey-grade antennas). The SRs are conditioned on PDOP ≤ 10 and computed based on all epochs. Below the vertical time series, the ADOP is depicted in blue color, the 0.12-cycles level as red, and ambiguity-float vertical formal STDs are shown in gray.
We also depict in Figure 4 the ambiguity dilution of precision (ADOP) as an easy-to-compute scalar diagnostic to measure the intrinsic model strength for successful ambiguity resolution. The ADOP is defined as
(cycles) (4)
with n being the dimension of the ambiguity vector, the ambiguity variance matrix, and |.| denoting the determinant. ADOP gives a good approximation to the average precision of the ambiguities, and it also provides for a good approximation to the ILS SR. The rule-of-thumb is that an ADOP smaller than about 0.12 cycles corresponds to an ambiguity SR larger than 99.9%.
Figure 4 shows that more solutions are incorrectly fixed (red dots) when the ADOPs (blue lines) are larger than the 0.12 cycle level (red dashed lines). The figure also reveals that the L1+B1 low-cost receiver plus patch antenna combination achieves an ILS SR (99.5%) similar to that of the survey-grade L1+L2 GPS receiver (SR of 100%) for the cut-off angle of 10°. This ILS SR corresponds to the availability of correctly fixed solutions (green dots) with millimeter-centimeter level positioning precision over the two days. The L1+L2 GPS receiver has, moreover, large ambiguity-fixed positioning excursions at the same time as the formal STDs are large for the cut-off angle of 25° due the poor GPS-only receiver-satellite geometry for this high cut-off angle. This is also reflected by the corresponding relatively large ambiguity-fixed STDs depicted in Table 2 that are improved from decimeter- to millimeter-level when the PDOP ≤ 10 condition is applied. Figure 4 also shows that the L1+B1 low-cost receiver with the survey-grade antenna has a larger SR of 97.8% when compared to the PDOP-conditioned SR for L1+L2 GPS of 94.1% for the cut-off angle of 25° (see also Table 2), owing to the use of BDS that significantly improves the receiver-satellite geometry.
Finally, we also tested the low-cost receiver-solution (with survey-grade antennas) for a baseline length of 7 kilometers, where (small) residual slant ionospheric delays are present. It was shown that this combination still has the potential to achieve ambiguity resolution and positioning performance competitive with the survey-grade receiver-solution.
CONCLUSIONS
In this article, we evaluated a low-cost L1+B1 GPS+BDS RTK setup and compared its ambiguity resolution and positioning performance to a survey-grade L1+L2 GPS solution in Dunedin, New Zealand. The LS-VCE procedure was used to determine the variances of the low-cost receivers. The estimated variances are needed so as to formulate a realistic stochastic model, otherwise the ambiguity resolution and hence the achievable positioning precisions would deteriorate.
Since we analyzed a short baseline, the LS-VCE variances were shown to likely be affected by multipath. To mitigate multipath we connected the low-cost receivers to survey-grade antennas with better signal reception and multipath suppression abilities. It was shown that the survey-grade antennas can significantly improve the performance for the low-cost receivers so that the code/phase noise estimates more resemble that of survey-grade receivers. The LS-VCE STDs were furthermore shown to be realistically estimated for an independent time period.
We also demonstrated that the low-cost receivers can give competitive instantaneous ambiguity resolution and positioning performance to that of the survey-grade receivers. This is particularly true when the low-cost receivers are connected to survey-grade antennas.
ACKNOWLEDGMENTS
This article is based on the paper “On the Performance of a Low-cost Single-frequency GPS+BDS RTK Positioning Model” presented at the 2017 International Technical Meeting of The Institute of Navigation held Jan. 30-Feb. 1, 2017, in Monterey, California.
Ryan Cambridge at the School of Surveying, University of Otago, collected the low-cost receiver data. Author Peter J.G. Teunissen was supported by an Australian Research Council Federation Fellowship. All of this support is gratefully acknowledged.
MANUFACTURERS
The low-cost receivers used in the research were u-blox EVK-M8T receivers. The survey-grade receivers were Trimble NetRS receivers. The patch antennas were u-blox ANN-MS antennas, while the survey-grade antennas were Trimble Zephyr 2 GNSS antennas.
ROBERT ODOLINSKI conducted his Ph.D. studies at Curtin University, Perth, Australia, from 2011 to 2014. His research focus is next-generation multi-GNSS integer ambiguity resolution enabled precise positioning. In 2015, Odolinski started his position as a lecturer/research fellow in geodesy/GNSS at the School of Surveying, University of Otago, New Zealand.
PETER J.G. TEUNISSEN is a professor of geodesy and navigation and the head of the Curtin GNSS Research Centre, Curtin University. He is also with the Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands. His research interests include multiple GNSS and the modeling of next-generation GNSS for high-precision positioning, navigation and timing applications.
FURTHER READING
• Authors’ Conference Paper
“On the Performance of a Low-cost Single-frequency GPS+BDS RTK Positioning Model” by R. Odolinski and P.J.G. Teunissen in Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, Jan. 30 – 1 Feb., 2017, pp. 745–753.
• Authors’ Related Work
“Single-Frequency, Dual-GNSS Versus Dual-frequency, Single-GNSS: A Low-cost and High-grade Receivers GPS-BDS RTK Analysis” by R. Odolinski and P.J.G. Teunissen in Journal of Geodesy, Vol. 90, No. 11, 2016, pp. 1255–1278, doi:10.1007/s00190-016-0921-x.
“Combined BDS, Galileo, QZSS and GPS Single-frequency RTK” by R. Odolinski, P.J.G. Teunissen and D. Odijk in GPS Solutions, Vol. 19, No. 1, 2015, pp. 151–163, doi:10.1007/s10291-014-0376-6.
“Instantaneous BeiDou+GPS RTK Positioning With High Cut-off Elevation Angles” by P.J.G. Teunissen, R. Odolinski and D. Odijk in Journal of Geodesy, Vol. 88, No. 4, 2014, pp. 335–350, doi: 10.1007/s00190-013-0686-4.
“The Future of Single-Frequency Integer Ambiguity Resolution” by S. Verhagen, P.J.G. Teunissen and D. Odijk in Proceedings of the VII Hotine-Marussi Symposium on Mathematical Geodesy, Rome, June 6–10, 2009, edited by N. Sneeuw, P. Novák, M. Crespi and F. Sanso, International Association of Geodesy Symposia, Vol. 137, 2012, pp. 33–38, doi:10.1007/978-3-642-22078-4 5.
“Centimeter-Level Positioning for UAVs and Other Mass-Market Applications” by C. Mongredien, J.-P. Doyen, M. Strom and D. Ammann in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 1441–1454.
“Initial Assessment of the COMPASS/BeiDou-2 Regional Navigation Satellite System” by O. Montenbruck, A. Hauschild, P. Steigenberger, U. Hugentobler, P.J.G. Teunissen and S. Nakamura in GPS Solutions, Vol. 17, No. 2, 2013, pp. 211–222, doi:10.1007/s10291-012-0272-x.
• LAMBDA
“On the Reliability of Integer Ambiguity Resolution” by S. Verhagen in Navigation, Vol. 52, No. 2, Summer 2005, pp. 99–110, doi: 10.1002/j.2161-4296.2005.tb01736.x.
“ADOP in Closed Form for a Hierarchy of Multi-frequency Single-baseline GNSS Models” by D. Odijk and P.J.G. Teunissen in Journal of Geodesy, Vol. 82, 2008, pp. 473–492, doi: 10.1007/s00190-007-0197-2.
In recent years there has been a proliferation of software-defined radio (SDR) data-collection systems and processing platforms designed for GNSS receiver applications or those that support GNSS bands. For post-processing, correctly interpreting the GNSS SDR sampled datasets produced or consumed by these systems has historically been a cumbersome and error-prone process.
This is because these systems necessarily produce datasets of various formats, the subtleties of which are often lost in translation when communicating between the producer and consumer of these datasets.
This specification standardizes the metadata associated with GNSS SDR sampled data files and the layout of the binary sample files.
The GNSS SDR Metadata Standard defines parameters and schema to express the contents of SDR sample data files. The standard is designed to promote the interoperability of GNSS SDR data collection systems and processors.
The metadata files are human readable and in XML format. A compliant open source C++ API for reading metadata and binary samples is also officially supported to promote ease of integration into existing SDR systems.
Review the formal standards document and click on Submit a Comment to provide feedback. Comments will be accepted through December 31, 2017.
I had my special ISO-certified glasses ready. Living in Oregon, I wasn’t about to miss the once-in-a-lifetime chance to see a total eclipse of the sun.
On Aug. 21, my family drove a few miles north to get into the path of totality, which for us lasted about a minute. It was definitely worth the field trip.
Besides regular folk like me, experts in numerous fields turned their eyes — and their instruments — to the eclipse.
The National Center for Atmospheric Research took to the air with a Gulfstream V fitted out with sensors and equipment for atmospheric research. The flight gathered data about the sun that can’t be collected from the ground.
With better instruments than ever before, for the first time scientists had the chance to observe the corona in the infrared spectrum, which may provide insight into the sun’s magnetic fields.
Back on terra firma, atmospheric scientists closely monitored changes in temperature and other weather effects. The temperature dropped as much as 7 degrees in Crossville, Tennessee, reports the National Weather Service.
Scientists at zoos and aquariums across the country closely watched animal behavior during totality. Species exhibiting unusual behavior included elephants, hippos, crocodiles and penguins.
The scientists found a “decrease in the number of free electrons in the part of the Earth’s ionosphere along the eclipse path where sunlight was temporarily blocked by the moon…
“TEC [total electron content] time series from two continuously operating GPS monitoring stations near the path of totality…show a small dip of about 2 TECU [TEC units] or so around 18:00 UTC on Aug. 21, coincident with the timing of the eclipse.”
The eclipse also affected WAAS real-time correction data from geostationary satellites.
While study of the data continues, it’s clear that GPS easily withstood the eclipse. Learn more here.
By Christopher Ball, 412th Test Wing Public Affairs
What happens when GPS isn’t available?
A collection of U.S. Department of Defense units and universities found out when they gathered at Edwards Air Force Base, California, to evaluate various aerial platforms in a degraded GPS environment this summer.
The week-long test event called DT NAVFEST — short for Developmental Test Navigation Festival — was the first large-scale program of its kind, according to James Cook, KC-46A project manager with the 418th Flight Test Squadron.
“DT NAVFEST was established to provide a locally more realistic GPS jamming environment in which aircraft platforms and unmanned aerial vehicles could evaluate their performance under a degraded GPS signal,” Cook said. “Other locations around the U.S. provide such environments, but having it locally allowed for direct program input and cost savings to customers by not having to deal with the logistics costs of deploying to those locations.”
Cole Johnson, technical lead for NAVFEST, explained how they create a degraded GPS environment.
“GPS signals are super faint,” he said. “Imagine a 30-watt lightbulb 12,000 miles in space. So it doesn’t take much interference for your smartphone’s GPS to lose lock on such a low power signal. Interference could occur from walking in a dense forest, through a canyon, inside a building, driving among skyscrapers, or from GPS jammers. The end effects of GPS jammers aren’t much different than the other causes of interference, they all make it harder for your GPS receiver to pick out faint GPS signals from the air, except jammers do it by adding noise to the environment.”
Teams from the University of Illinois Champagne Urbana and Stanford University were invited to the first-ever DT NAVFEST at Edwards Air Force Base to test their projects in a GPS degraded environment. (Photo: U.S. Air Force/Wei Lee)
The GPS jammers and support came from the 746th Test Squadron at Holloman Air Force Base, New Mexico.
According to Wei Lee, test safety engineer with the 412th Test Wing, the universities were invited to participate in DT NAVFEST on a trial basis with the hope of expanding to other institutions in the future.
“Live GPS jamming data is extremely difficult for academic labs to obtain due to the complexity of working with the Federal Aviation Administration and regional first responders,” Lee said. “It is crucial that the Department of Defense support basic research and development that is ongoing in our nation’s top academic institutions. Many of the low technology readiness level projects will eventually migrate from academic labs to defense industry and military applications. Allowing the labs to participate on a non-interference basis is a win-win situation.”
To minimize the effect on the local community and air traffic, planning of the GPS jamming was initiated months in advance. According to Johnson, the GPS jammers had a vertical reach of upwards of 30,000 feet, so the first step was contacting the FAA, which provided a list of “green” times when commercial air traffic was at its lowest. This led to the testing being performed between 1 and 6 a.m. on test days.
Johnson said the team performed extensive modeling and simulation to identify how far the GPS interference would reach. “Not just at 30,000 feet, but ground level as well.”
The models suggested a small part of the Antelope Valley — a couple of small towns around Edwards — could be affected. “We wanted to err on the side of caution, so we constructed a huge list of emergency services from the Antelope Valley to contact.”
The team also set up phone lines the FAA and any emergency service could call up during testing and request the jammers to be turned off.
The 746th Test Squadron from Holloman Air Force Base, New Mexico, provided an array of GPS jamming equipment and support for DT NAVFEST at Edwards Air Force Base. The jammers provided a degraded GPS environment for testing multiple aerial platforms throughout the week. Testing was done from 1 to 6 a.m. each day to minimize impact on the community and civilian air traffic. (Photo: U.S. Air Force/Cole Johnson)
Cook said the event was extremely successful, judging by the feedback from the customers.
“For a first-of-its-kind event, it executed fairly smoothly, thanks to the test team and customers’ direct involvement,” he said. “The technical knowledge and support from the 746th TS was awesome. And the support given to this program from 412th Test Wing all the way down to the Airman on the ground providing direct support.”
Arianespace will launch four new satellites for the Galileo constellation, using two Ariane 62 versions of the next-generation Ariane 6 rocket from the Guiana Space Center in French Guiana.
The Ariane 62 rocket. (Image: Arianespace)
The contract will be conducted by the European Space Agency (ESA) on behalf of the European Commission (DG Growth) and the European Union.
This is the first ESA first contract to use the company’s new rocket.
Stéphane Israël, Arianespace chief executive officer, and Paul Verhoef, director of Navigation at the European Space Agency (ESA), signed the launch contract for four new satellites to join the European satellite navigation system Galileo. The contract will be conducted by ESA on behalf of the European Commission (DG Growth).
These launches are planned between the end of 2020 and mid-2021, using two Ariane 62 launchers — the configuration of Europe’s new-generation launch vehicle that is best suited for the targeted orbit. The contract also provides for the possibility of using the Soyuz launch vehicle from the Guiana Space Center, if needed.
Both missions will carry a pair of Galileo spacecraft to continue the constellation deployment for Europe’s satellite-based navigation system. The satellites, each weighing approximately 750 kg, will be placed in medium earth orbit (MEO) at an altitude of 23,222 kilometers and be part of the Galileo satellite navigation constellation.
An ESA video about Ariane 6 is below:
Galileo is the first joint infrastructure financed by the European Union, which also will be the owner. The Galileo system incorporates innovative technologies developed in Europe for the greater benefit of citizens worldwide.
A total of 18 Galileo satellites already are in orbit. Fourteen of these satellites were launched two at a time by Soyuz launchers, with the last four orbited on a single Ariane 5 ES mission in November 2016. Two more Ariane 5 ES missions are planned on December 12, 2017 and in the summer of 2018.
Following the signing of this latest contract, Stéphane Israël, CEO of Arianespace, issued this statement:
“Arianespace is especially proud to have won this first launch contract for the Ariane 6 from its loyal customers and partners, the European Commission (DG Growth) and ESA. We are very pleased to have earned this expression of trust from the European Commission; by choosing to continue the deployment of the Galileo constellation with two Ariane 62 launches, they become the first confirmed customer for our next-generation heavy launcher, which is slated to make its initial flight in the summer of 2020. Through this decision, which adds two additional launches to follow the already-scheduled Ariane 5 ES flights, the European Commission and ESA are clearly indicating a key commitment to Arianespace’s next generation of launchers, which reaffirms more than ever its mission to ensure Europe’s autonomous access to space.”
The second satellite of Japan’s Quasi-Zenith Satellite System (QZSS) has started transmitting navigation signals. QZS-2, or Michibiki-2, was launched on June 1, 2017, and joins its predecessor QZS-1 (Michibiki-1), which has been in orbit since September 2010.
Both satellites have been placed into inclined geosynchronous, elliptical orbits, which enable extended satellite visibility periods over Japan and are characteristic features for this regional navigation system.
The third satellite QZS-3 was launched on Aug. 19, 2017, into a geostationary orbit. If all goes according to plan, a fourth satellite in an eccentric orbit will follow by the end of this year and complete the constellation.
Three of six new Lockheed Martin-developed receivers are now deployed at U.S. Air Force monitoring stations to maintain the accuracy of GPS satellite signals.
In June, the first Monitor Station Technology Improvement Capability (MSTIC) receiver became operational at Cape Canaveral Air Force Station, Florida. Upgrades continued at USAF monitoring stations at Kwajalein Atoll and Hawaii. These upgrades from early 1990s technology are part of an overall effort to modernize the current GPS ground control system, known as the Architecture Evolution Plan Operational Control Segment.
MSTIC software-defined radio technology replaces legacy receivers’ hardware-based application-specific integrated circuit platform. MSTIC leverages commercial off-the-shelf hardware without the need for custom firmware. Standard interfaces and architecture configurability simplify sustainment and enable MSTIC software to migrate to new hardware platforms as commercial vendors increase processing power, improve reliability and enhance cybersecurity. MSTIC enables remote application of mission-specific software updates to improve performance and enable reception of modernized GPS signals, according to the company.
The three remaining GPS Monitoring Stations will be upgraded with MSTIC receivers by the end of 2017.
The navigation payload before integration into the second GPS III SV, which now is in environmental testing. (Photo: Harris)
GPS III Payloads Delivered
Harris Corporation has delivered the third of 10 advanced navigation payloads to Lockheed Martin. The payloads will increase accuracy, signal power and jamming resistance for GPS III satellites. They feature a Mission Data Unit (MDU) with a 70-percent digital design that links atomic clocks, radiation-hardened computers and powerful transmitters, enabling signals three times more accurate than those on current GPS satellites. The new payloads also boost satellite signal power, increase jamming resistance by eight times and help extend the satellite’s lifespan.
The payload was integrated into GPS III SV03 over the summer. The first navigation payload is integrated aboard GPS III SV01, which is in storage awaiting expected 2018 launch.
Harris announced it is in full production and on target to deliver the fourth GPS III navigation payload to Lockheed Martin in fall. Harris is also developing a fully digital MDU for the U.S. Air Force’s GPS III Space Vehicles 11+ acquisition. The new MDU will be demonstrated in fall 2017 and provides even greater flexibility, affordability and accuracy versus existing GPS satellites.
Next GLONASS-M Readied
The Russian navigation satellite GLONASS-M 52 moved from ISS-Reshetnev Company’s assembly plant to the Plesetsk Cosmodrome launch site about 800 km north of Moscow in August. One of the system’s ground spares, it was built more than two years ago and stored awaiting launch. The satellite is due to launch in September.
There are six GLONASS-M satellites in ground reserve.
The U.S. Air Force has awarded Lockheed Martin a $45.5 million contract to provide military code (M-code) early use (MCEU) capability to the Global Positioning System (GPS).
Part of the Air Force’s overall modernization plan for the GPS, M-code is an advanced, new signal designed to improve anti-jamming and protection from spoofing — as well as increased secure access — to military GPS signals for U.S. and allied armed forces.
MCEU will provide command and control of M-code capability to eight GPS IIR-M and 12 GPS IIF satellites on orbit, as well as future GPS III satellites, which the Air Force expects will begin launching in 2018.
MCEU is envisioned as a way to accelerate M-code’s deployment to support testing and fielding of modernized user equipment in support of the warfighter.
The Military Code (M-Code) Early Use (MCEU) contract will accelerate deployment of command and control of M-code capability to GPS IIR-M and GPS IIF satellites currently on orbit, as well as future GPS III satellites (like GPS III SV02 above). (Photo: Lockheed Martin)
The U.S. Air Force’s MCEU contract directs Lockheed Martin to upgrade the existing Architecture Evolution Plan (AEP) Operational Control System (OCS), allowing it to task, upload and monitor M-code within the GPS constellation. The contract includes new software and hardware development that will be deployed in 2019 to worldwide ground facilities that support the Air Force’s GPS.
“When people think of GPS, they often think of the satellites that provide the signals, but do not remember the important ground system behind it,” said Mark Stewart, Lockheed Martin’s vice president for Navigation Systems. “We recognize the ‘ground’ is critical for any major space mission constellation and we are proud that we can help the Air Force with this part of their GPS modernization plan.”
The AEP OCS — maintained by Lockheed Martin under the GPS Control Segment (GCS) Sustainment Contract — controls the 12 GPS IIR, 8 IIR-M and 12 IIF satellites in orbit today. The company has successfully implemented several recent projects to modernize and sustain the system for the Air Force.
In June, Lockheed Martin deployed the first of its state-of-the-art GPS Monitor Station Technology Improvement Capability (MSTIC) receivers at Cape Canaveral Air Force Station. The software-defined MSTIC system replaces 30-year-old hardware, positioning the Air Force to take advantage of commercial off-the-shelf technology enhancements in processing power, reliability and cybersecurity in the future. Six Air Force AEP OCS monitoring stations around the world will receive the MSTIC upgrade by the end of 2017.
In February 2016, the Air Force awarded Lockheed Martin the GPS III Contingency Operations (COps) contract to upgrade the AEP OCS with new capabilities so it could support the more powerful, next-generation GPS Block III satellites. The COps program passed a successful Critical Design Review milestone with the Air Force in December 2016.
Also in 2016, under the GCS contract, Lockheed Martin completed the commercial off-the-shelf upgrade No. 2 (CUP2) project — part of a multi-year plan to modernize the AEP OCS’ technology and enhance the system’s ability to protect data and infrastructure from internal and external cyber threats, as well as improve its overall sustainability and operability. CUP2 is now fully operational and managing the current GPS constellation.
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The Institute of Navigation’s 
