Rohde & Schwarz designed its GNSS simulator for the R&S SMBV100A with a focus on production testing of GNSS receivers.
Rohde & Schwarz now offers a new, speed-optimized production tester — the R&S SMBV100A vector signal generator equipped with the R&S SMBV-P101 package.
During production testing of modules and receivers for satellite-based communications, the basic GNSS signal reception and the connection between the antenna and GNSS chipset need to be checked. The GNSS production tester simulates separate satellites for the GPS, GLONASS, BeiDou and Galileo navigation standards in the L1/E1 band specifically for these production tests.
The four satellite constellations can be activated individually, each with a high dynamic range of 34 dB. Level changes can be made on the fly without interrupting the signal, enabling users to simultaneously perform independent sensitivity tests for each navigation system. The 1 pps or 10 pps GNSS marker allows exact time synchronization between the tester and the DUT. Pure, level-stable CW signals can also be generated to calibrate the test setup or to simulate interferers.
The R&S SMBV-P101 option additionally offers test functions for efficient characterization of GNSS chipsets, Rohde & Schwarz said. As a result, a receiver’s ability to handle high-movement dynamics can be verified quickly and cost-effectively. To do this, users can access both predefined and user-defined Doppler profiles, from which the R&S SMBV100A automatically generates the appropriate satellite signal.
The R&S SMBV-P101 GNSS production tester package for the R&S SMBV100A is now available from Rohde & Schwarz.
SVN49 in space (artist’s rendering). The signal anomaly from SVN 49 alerted researchers to new possibilities in analysis and monitoring.
Chip Transition-Edge Based Signal Tracking for Ultra-Precise GNSS Monitoring Applications
By Sanjeev Gunawardena, John Raquet and Frank van Graas
Tracking GNSS signals using their underlying spreading sequence chip transition edges reveals positive versus negative chip asymmetries that are characteristic to each satellite. This asymmetry is due to various types of natural signal deformation that is known to occur within the satellite’s signal generation and transmission hardware. This novel concept of monitoring chip asymmetry can extend the state of the art in the areas of GNSS signal-quality monitoring and authentication. A technique to directly monitor chip asymmetry within a specially designed ChipShape GNSS receiver architecture employs separate code discriminators that align themselves to the chip rising-edge and falling-edge zero crossings.
The detailed study of naturally-present deformations in GNSS signals is a relatively new activity that was sparked by the GPS SVN49 anomaly and the associated research activities that followed. This research area has numerous applications that include:
Informing the design of sudden signal deformation detection and alerting algorithms for safety-of-life differential GNSS applications (such as aviation).
GNSS signal “fingerprinting” and authentication.
The detailed study of long-term degradation effects of GNSS satellite signal generation and transmission hardware.
Analysis of the impact to the first item in this list of swapping a satellite’s signal generation modules by its control segment.
Multipath detection, characterization, and mitigation are also closely tied to all research relating to GNSS signal deformation monitoring (SDM).
High-fidelity SDM can be performed using two methods:
observation of actual GNSS signals above the thermal noise floor using a high-gain dish antenna;
the combination of long coherent integration and multi-correlator processing.
Our previous research has revealed that these two methods are highly complementary for gaining full insight into the effects and causes of observed natural signal deformations.
Among the handful of multi-correlator processing techniques that can be applied for SDM, ChipShape processing allows the correlation function resolution to be finely adjustable while providing good numerical processing efficiency. This processing technique also allows chip-transition eye diagrams to be constructed in order to provide additional insight such as positive and negative chip width asymmetries.
One goal of our SDM research involves developing capabilities to observe GNSS signals with the highest levels of fidelity practically achievable in order to further the application areas described above. Key to this is developing techniques to track GNSS signals using a reference point that is both consistent and invariant (to the greatest extent possible) to nominal signal deformations and environmental effects such as multipath. Traditional multipath mitigating techniques such as narrow correlator and double-delta correlator are sub-optimal in this regard. This is because a significant portion of the signal around the chip transition point (that is, 10 percent and 20 percent for 0.1 chip correlator spacing, respectively) must be integrated to realize these discriminators and maintain robust tracking in moderate dynamics conditions. This integration tends to low-pass filter the desired observables.
Chip Transition Edge-Based Code Tracking
Figure 1 illustrates normalized C/A code chip rising edges for the GPS constellation of June 2014. These chip shapes were processed using a front-end with 24 MHz bandwidth. For visual comparison purposes, this and other related plots were obtained using 600 seconds of coherent integration.
Figure 1. Normalized ChipShape rising edges for the GPS SPS constellation of June 2014; each color represents a different GPS satellite.
The code tracking loop used to obtain this result employed an empirical normalized coherent rising-edge discriminator given by:
(1)
Where τ is relative code phase in chips, d is Early-Late correlator spacing,R’XYZ(i) is the differential correlation output for integer bin i obtained using ChipShape processing with masking sequence XYZ. bin(x) is a function that selects the closest ChipShape vector index that corresponds to relative code phase x. Each ChipShape processing bank is configured to span one chip early and one chip late with a resolution of N bins per chip, thus producing a ChipShape vector of 3N bins. α is a scale factor obtained through trial and error to yield robust tracking performance as observed by the code-minus-phase measurement. For the result shown in Figure 1, N=240 and d ≈ 0.017 chips.
The figure clearly shows that the rising-edge zero crossings vary by SV. This variation is due to nominal signal deformation present in each GPS-SPS signal.
Figure 2 illustrates the rising-edge zero crossings aligned to zero relative code phase. This alignment was performed by interpolating each R’NPN vector, precisely estimating code phase at the zero-crossing point, and shifting the curve appropriately.
Figure 2. Normalized ChipShape rising edges for the GPS SPS constellation of June 2014: Zero crossing compensated.
Figure 3 shows zero crossings for the falling edges after all rising edges were aligned to zero. The figure clearly illustrates subtle asymmetries between positive and negative chips which span a range of approximately ±1.5 meters. These asymmetries are not directly observable using typical GNSS receiver processing. However, they can lead to pseudorange biases through the resulting distortion that occurs to the traditional correlation function.
Figure 3. Normalized ChipShape falling edges for the GPS SPS Constellation of June 2014 when rising edges are aligned to zero.
In general, a family of code discriminators that precisely track chip rising-edge zero crossings can be defined by:
(2)
Where R’NPX is a linear combination of orthogonal ChipShape components that preserve the rising-edge transition, e.g.: R’NPX =R’NPN +R’NPP. R’FFX is a linear combination of orthogonal ChipShape components that preserve the non-transitioning (that is, flat) sections of chips, for example: R’FFX =R’PPP + R’PPN −R’NNP − R’NNN. a and b define an integration interval within the range −1 to +2 chips with respect to the chip transition edge. β is a bias compensation term. represents the real or imaginary component function for the coherent discriminator (depending on the modulation phase of the signal being tracked), or the magnitude function for a non-coherent discriminator implementation.
Similarly, a family of code discriminators that precisely track chip falling-edge zero crossings that occur one chip after the rising edges tracked by the discriminator of Equation 2 can be defined by:
(3)
Then, a two-step technique to precisely monitor chip asymmetry can be described as follows:
Setup two identical ChipShape processing channels to track a given PRN. Progressively tighten the code tracking loops to track the rising-edge zero crossings of the underlying signal using the discriminator of Equation 2.
After steady-state zero-crossing rising-edge tracking is achieved, switch the second channel’s code discriminator to that of Equation 3. This will cause the second channel to track the zero crossings of the falling edges that occur one chip later in the underlying signal’s spreading sequence. The discriminator’s linear range must be wide enough to pull-in the chip asymmetry shown in Figure 3.
When the second channel re-converges as a result of Step 2, the relative pseudorange displacement that occurs is equal to the chip asymmetry in meters. Hence, chip asymmetry can be monitored for the entire visible pass of a satellite. It is expected that positive and negative chip transitions are equally affected by channel distortions (that is, code and carrier multipath, ionosphere, troposphere, and the receiver antenna and front-end transfer function). Hence, the rising-edge-code-minus-falling-edge-code measure of chip asymmetry is expected to be invariant to most if not all channel distortions.
Estimating Compensation Parameters
As shown in Equations 2 and 3, due to natural signal deformation of many types, the rising and falling-edge zero-crossing discriminators are expected to be SV number, PRN code and elevation angle dependent. Hence, α and β must be estimated for a given correlator spacing d separately for all SV signals of the constellation. These values will also be specific to a given antenna and receiver front-end.
Figure 4 illustrates the procedure used to estimate the scale factor and bias terms starting with the empirical rising-edge tracking process described above.
Figure 4. Procedure for estimating scale factors and biases for rising-edge tracking early-late and double-delta code discriminators.
The following figures illustrate the edge tracking discriminator calibration process using R’NPN for a single SV.
Figure 5 illustrates the early-plus-late functions computed for various correlator spacings. As described previously, these functions typically do not cross through zero codephase due to natural signal deformation.
Figure 5. Uncorrected rising-edge early-late discriminator functions for various correlator spacings.
Figure 6 illustrates the rising-edge discriminator functions after bias compensation.
Figure 6. Rising-edge early-late discriminator functions for various correlator spacings after bias compensation.
Figure 7. Calibrated rising-edge early-late discriminator functions for various correlator spacings.
Figure 8 illustrates the multipath error envelopes for the rising edge-based coherent code discriminators. The performance of these discriminators is similar to the traditional Early-Late discriminators for the same correlator spacings. This result is consistent with the theoretical bounds for code multipath.
Figure 8. Multipath error envelopes for various rising edge-based coherent early-late code discriminator functions.
As shown in Figure 4, the edge-tracking discriminators described in Equations 2 and 3 that are based on Early-Late bin spacings can be combined to obtain edge-tracking double-delta discriminators. Double-delta discriminators provide significantly improved multipath performance.
In general, the edge-tracking double-delta discriminator for inner correlator spacing d is formed by the linear combination of two early-late edge-tracking discriminators, as follows:
(4)
Scale factor γ is estimated such that overall multipath error is minimized according to a given design criteria.
Figure 9 illustrates the double-delta rising-edge discriminator with inner spacing of 0.017 chips. This discriminator has a pull-in range of approximately ±0.01 C/A chips.
Figure 11 illustrates the multipath error envelope for the coherent rising-edge double-delta discriminator. Performance is consistent with a traditional second-derivative discriminator.
Figure 11. Multipath error envelope for coherent rising-edge double-delta code discriminator with inner spacing of ~0.017 C/A chips.
Figure 12 illustrates the performance of the various rising-edge tracking discriminators for a live-sky GPS-SPS signal (de-trended code-minus-carrier measurement). This figure clearly demonstrates robust code tracking and the multipath and noise mitigating benefit of ultra-narrow rising-edge discriminators.
Figure 12. Code tracking performance for live sky data of various rising edge-based coherent early-late code discriminator functions.
Conclusions
An empirical chip rising edge-based tracking technique was used to observe the underlying chip shapes of live sky GPS-SPS signals at high fidelity. These results reveal positive versus negative chip asymmetries that are characteristic to each satellite. The novel concept and technique of directly monitoring chip asymmetry has potential to extend the state of the art in the areas of GNSS signal quality monitoring and authentication.
Disclaimers. The views expressed in this paper 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 United States Government.
Acknowledgments. This research was supported by the Air Force Research Laboratory Sensors Directorate.
The authors thank Ohio University Avionics Engineering Center for making available a cluster of high-performance computers to process the 20 TB dataset for this research, and Kadi Merbouh of Ohio University for maintaining and overseeing operation of this equipment.
The ChipShape processing is an extension of the signal compression technique first published by Larry Weill and licensed by NovAtel for use in its Vision Correlator technology.
This article is based on a paper presented at ION Pacific PNT 2015 in Honolulu.
SANJEEV GUNAWARDENA is a research assistant professor with the Autonomy & Navigation Technology (ANT) Center at the Air Force Institute of Technology (AFIT). He earned a Ph.D. in electrical engineering from Ohio University.
JOHN RAQUET is a professor of electrical engineering and the Director of the ANT Center at AFIT. He has been involved in navigation-related research for more than 25 years.
FRANK VAN GRAAS is the Fritz J. and Dolores H. Russ professor of electrical engineering and principal investigator with the Avionics Engineering Center at Ohio University. He received the ION Johannes Kepler, Thurlow and Burka awards, and is a Fellow and past president of the ION.
NASA’s Ikhana is being used to test a system that will allow uncrewed aircraft to fly routine operations within the National Airspace System. (Credit: NASA)
NASA plans to install a Locata network (LocataNet) as the core positioning technology for safety-critical unmanned aerial systems (UAS) research at its Langley Research Center in Hampton, Va., according to an announcement by Locata.
NASA Langley is tasked with performing rigorous and repeatable scientific evaluation of new UAS safety and technology concepts under development. The LocataNet will provide high-precision non-GPS-based positioning, navigation and timing (PNT) that is essential for this work. Known for its long history of aeronautics research, NASA Langley is a key center for UAS research and development. In June, one of Langley’s unmanned hexacopters (a drone with six rotors) delivered medical supplies to a clinic, the first such delivery by an unmanned drone.
Locata’s centimeter-accurate positioning will now assist NASA to develop and improve flight-critical technology systems that support air transportation safety, efficiency and performance. Langley’s extensive state-of-the-art facilities will be further enhanced with the installation of the LocataNet.
The NASA LocataNet is scheduled to be installed and commissioned before the end of 2015. Locata will supply the LocataLite Transmitters and Locata receivers required by NASA for the installation. Aviation-quality Locata antennas, developed by Cooper Antennas (UK) and previously used by the U.S. Air Force in its own military LocataNets, will also be installed. Locata engineers will support the physical installation, ongoing training and the future technical support required by NASA Langley for this world-first UAS deployment.
Locata Corporation has invented new terrestrial positioning networks which function as local, ground-based replicas of GPS. These networks can be thought of as “GPS hotspots,” according to the company. Locata has amassed 146 granted patents to date protecting these innovations, with many more patents in the works.
Locata is currently shipping commercial systems to demanding and professional end users such as the USAF, NASA, Leica Geosystems, and many others. Locata enables their integration partners to extend GPS-like positioning coverage to modern industrial, commercial, consumer and government applications in areas where GPS is erratic, jammed or unavailable.
“Locata is proud and delighted to have received an order for NASA’s first LocataNet. Globally significant installations like this prove Locata’s new technology is delivering unprecedented levels of performance to many important new applications,” said Nunzio Gambale, Locata CEO. “As our technology roll-out begins to gain pace, the exceptional value Locata brings to next-gen mobile apps has attracted interest from players all over the world. In fact, our list of relationships is now looking like a roster of the world’s crème-de-la-crème. I honestly can’t think of a better or more prestigious name than NASA to add to our growing partner list.”
“Our team is savoring the opportunity to work alongside NASA engineers and we’re excited that Locata will help advance the safety-critical performance of Unmanned Aerial Systems,” he continued. “Almost all future mobile devices or machines, be they on the road, in the air, on a mine site, in a port, in a warehouse, in your mobile phone, or part of the inevitable Internet of Things — all of them are critically dependent on pervasive, reliable, high-accuracy positioning. Locata is being leveraged into these next-gen systems because it’s clear that satellite-based solutions alone can no longer deliver what’s required. Soon, as we bring miniaturized Locata transmitters and receivers to market, our innovations will enable even greater advances in cutting-edge consumer, commercial, and government applications.”
NASA Testing Program. As part of its UAS research, NASA is testing a system that would make it possible for unmanned aircraft to fly routine operations in United States airspace. Through the agency’s Unmanned Aircraft Systems Integration in the National Airspace System (UAS-NAS) project, NASA, General Atomics Aeronautical Systems, Inc. (GA-ASI) and Honeywell International, Inc., are flying a series of tests which began on June 17 and will run through July at NASA’s Armstrong Flight Research Center in California.
“We are excited to continue our partnership with GA-ASI and Honeywell to collect flight test data that will aid in the development of standards necessary to safely integrate these aircraft into the National Airspace System,” said Laurie Grindle, UAS-NAS project manager at Armstrong.
This is the third series of tests that builds upon the success of similar experiments conducted late last year that demonstrated a proof-of-concept sense-and-avoid system. The tests engage the core air traffic infrastructure and supporting software components through a live and virtual environment to demonstrate how a remotely piloted aircraft interacts with air traffic controllers and other air traffic.
“This is the first time that we are flight testing all of the technology developments from the project at the same time,” Grindle said.
This series of tests is made up of two phases. The first is focused on validation of sensor, trajectory and other simulation models using live data. Some of the tests will be flown with an Ikhana aircraft, based at Armstrong, that has been equipped with an updated sense-and-avoid system, as well as other advanced software from Honeywell.
Other tests will involve an S-3B plane from NASA’s Glenn Research Center in Cleveland, serving as a high-speed piloted surrogate aircraft. Both tests will use other aircraft following scripted flight paths to intrude on the flight path the remotely-piloted craft is flying, prompting it to either issue an alert or maneuver out of the other aircraft’s path. These flights will also conduct the first full test of the traffic alert and collision avoidance system (TCAS II) on a remotely piloted aircraft.
During the June 17 test, which lasted a little more than five hours, the team accomplished 14 encounters using the Ikhana aircraft and a Honeywell-owned Beech C90 King Air acting as the intruder. A second test was flown the following day, with a total of 23 encounters. The project team plans to fly more than 200 encounters throughout the first phase of the test series.
“Our researchers and project engineers will be gathering a substantial amount of data to validate their pilot maneuver guidance and alerting logic that has previously been evaluated in simulations,” said Heather Maliska, Armstrong’s UAS-NAS deputy project manager.
The second phase of the third test series will begin in August and will include a T-34 plane equipped with a proof-of-concept control and non-payload communications system. It will evaluate how well the systems work together so that the aircraft pilots itself, interacts with air traffic controllers and remains well clear of other aircraft while executing its operational mission. The aircraft, which will have an onboard safety pilot, will fly an operationally representative mission in a virtual airspace sector complete with air traffic control and live and virtual traffic.
FIGURE 2. TEC map over São Paulo state as forecast by the CALIBRA model on Sept. 26, 2012, at 2:00 UT. The range of the TEC in the image is from 0 to 90 TEC units (blue to red). The red line is the geomagnetic equator.
Countering Ionospheric Disturbances Affecting GNSS in Brazil
By Marcio Aquino
After 27 months of intense research, the CALIBRA project ended successfully in February 2015, with the project team devising solutions to tackle the effects of perturbations typical of the Brazilian ionosphere on high-accuracy GNSS positioning. CALIBRA was funded by the European Union and the European GNSS Agency.
Kicked off in 2012, CALIBRA first confirmed the vulnerability of GNSS high-accuracy techniques to ionospheric disturbances through a thorough user performance review, where degradation in GNSS Precise Point Positioning (PPP) and real-time kinematic (RTK) positioning was seen to correlate with the occurrence of ionospheric scintillation and high Total Electron Content (TEC) variability. This is especially so in Brazil, because of its geographical location extending across the magnetic equator in one of the most troublesome ionospheric regions of the Earth, qualifying the country as a test-bed for worst-case scenarios.
The team established a suitable metric to characterize these disturbances, which was used in developing the new models and algorithms to counter their effects. The short-term empirical CALIBRA Forecasting Model (CFM) for TEC and scintillation was developed and tested.
To counter scintillation, a number of approaches were proposed and their benefits demonstrated. Building on the project’s success, CALIBRA partner INGV (Istituto Nazionale di Geofisica e Vulcanologia) filed a patent for the CFM and a new spin-off company — SpacEarth Technology — was set up. SpacEarth aims to secure the software’s commercialization for potential applications and services, while also improving and adapting it to evolving market needs.
Another outcome of commercial interest is that project partner Septentrio developed several rover-level mitigation approaches, notably a new model for ionospheric delay estimation.
Monitoring Network. To support the research and operational activities of the project, a dedicated network of ionospheric scintillation monitor receivers (ISMRs) was deployed, forming the CIGALA-CALIBRA network of 12 monitoring stations equipped with PolaRxS receivers. A web interface for data analysis — the ISMR Query Tool — was developed by project partner UNESP (São Paulo State University) and is available for public use, collecting and treating more than 10 million observations of GPS, GLONASS, Galileo, BeiDou and other augmentation systems on a daily basis. Data visualization and data mining techniques support users in data analysis and knowledge extraction.
Finally, two important field trials aiming to validate the new algorithms were carried out in Brazil, involving actual precision agriculture and offshore operations. For the precision agriculture trial, the Brazilian company Agro Pastoril Campanelli provided expert operational environment and support.
The tractor used in the precision agriculture trial at Agro Pastoril Campanelli’s premises.
For the offshore trial, the project counted on the collaboration of the DOF Brasil Group representing Norskan Offshore, a provider of high-end offshore services to the Brazilian oil and gas industry. Detailed results of both trials are in the project’s final report, which can be accessed through the GSA.
The Geograph vessel is operated by DOF Brasil.Setting up the receiver antenna for the offshore trial on board the Geograph vessel.
To provide a glimpse of the performance of the CALIBRA algorithms during the offshore trial, in FIGURE 1 we selected a period when strong scintillation conditions were encountered. In the top plot, two height component time series for kinematic PPP processing are shown, respectively, for the case where no mitigation is applied (black time series) and the case where the CALIBRA algorithm is applied (red time series).
FIGURE 1. Performance of CALIBRA algorithms in the offshore trial.
The bottom plot shows the level of amplitude scintillation (S4 index) affecting the GPS satellites over a 10-degree elevation angle.
The improvement obtained with the CALIBRA solution can be seen in particular during the PPP convergence period (18:00 to 18:30 UT) and during the period of strong scintillation (22:30 to 23:30 UT). As there was no accurate ground truth available, the RMS values with respect to the mean height, taken from the quiet period (between 19:00 and 22:00 UTC), along with the percentage of improvement when applying the CALIBRA mitigation approach are summarized in TABLE 1.
TABLE 1. RMS values with respect to mean height, 19:00–22:00 UTC.
Despite all the successful work carried out by CALIBRA, the team notes that research must be continued to accomplish further improvement in models and algorithms to finally develop processes for real-time operation. The challenge would be to counter these ionospheric threats in the scope of an operational service aimed to provide robust high-accuracy positioning to support user applications.
Furthermore, there were strong indications that the addition of Galileo will assist in mitigating the problems addressed in the project when more signals are available in space.
Marcio Aquino is a Principal Research Fellow at the Nottingham Geospatial Institute of Nottingham University and leader of CALIBRA.
The first and best step to combat the growing worldwide problem of GPS jamming is to pursue technologies that can detect and locate the jammers. Signal Sentry 1000 uses arrayed sensors to do just that: look out for jamming and track down its source once sensed.
An array of sensors can be deployed for sensitive and high value entities such as infrastructure installations, including airports, railroads, chemical plants, electric power plants and grids, cargo ports, wireless communication systems and financial transfer centers. The sensors will connect to servers that assimilate the sensor data and provide operator interfaces.
Signal Sentry 1000 is based on a server/client model. The user accesses Signal Sentry using a URL and secure log-in specific to the user’s system. The user’s particular home screen displays a map with each installed sensor displayed with an icon reflecting status. Interferers are displayed as red stars or as error ellipses.
The Signal Sentry web page lists all the interferers stored in the database with their start and end times. The user can manipulate the list by changing the minimum duration of the event to be displayed as well as if the interferer had been geolocated or not, or both. If an interference event was less than a minute long, it may not have a geolocation entry.
Geolocation Methodology. Geolocation of jammers is accomplished through proprietary algorithms running at the network server that utilize digitized, timestamped I and Q samples of received interference waveforms, GPS observables, and other parameters captured by each sensor. This data is processed in a Kalman-filter based location algorithm to determine an initial jammer position and track the position of the jammer throughout the jamming event. This improves performance with moving jammers (that is, vehicle-based) and enables continued jammer location with a limited sensor set (potentially due to signal blockage, erroneous data due to multipath, or out-of-range conditions). Upon detection of an interference event by any sensor, the server polls the entire sensor network for data and determines if the information is sufficient to perform geolocation.
The user receives near-real-time status of event detections and geo-location of the interferer (if possible). Sensor data polling, geolocation processing and GUI updates continue until the interference stops or the emitter goes out of sensor range. Sensor data from each event is stored for later replay and processing using Signal Sentry event analysis tools.
An interference event frequency chart (Figure 1) provides a tool for forensically evaluating the occurrence of interferers. It displays interference events as circles; the size of the circle represents the number of events that occurred at that day of the week and time. When dots are selected on the chart, a map below the chart shows the location of the interference events. More than one dot can be selected at a time. This allows a user to find correlations in time and space, to determine if events occur at specific locations at certain times of the day and/or days of the week.
FIGURE 1. Interference event frequency chart.
Selecting the interferer on the map and then the details button on the popup brings up the interferer details page (Figure 2). Users can sign up for interferer alerts to be sent to their email or phone by text.
FIGURE 2. Interferer details.
Testing
Signal Sentry 1000 was deployed and tested in GPS jamming trials at Sennybridge, United Kingdom, in August 2014. Testing included stationary jammers and mobile jammers moving at up to 50 mph, in open fields and built-up areas.
Sentry Arrayed. The sensors used in these trials were custom units designed and built to Harris specifications by Chronos Technology Ltd. Each consisted of an embedded GPS receiver, an interference signal receiver and a local processor with a network communications interface.
An array of eight sensors was geographically distributed around the test facility. Each sensor and a centralized Signal Sentry processing server were equipped with a mesh data networking capable radio for wireless data communications of commands, status and event data. In other Signal Sentry deployments, the server software is typically hosted on a cloud server, and sensors communicate with the server either via hard-wired internet connections or wirelessly through cellphone network-compatible data adapters.
Jammer Profile. Two jammers performed during the trials, a 150mW and a .5W jammer, used to disrupt the GPS L1 C/A code at 1575.42 MHz.
Open Field. Atest in an area with no obstructions included static jammers and dynamic jammers. Five waypoints along the road, in an area measuring 320 by 444 meters, were surveyed prior to the test using a handheld GPS receiver, to evaluate location accuracy.
Table 1 shows static test results. The accuracy error is the average delta between the Signal Sentry-reported jammer positions versus the actual surveyed jammer positions. The number of points column contains the number of measurements reported by Signal Sentry during the test scenario for each waypoint. The overall average accuracy error for the static jammer test was 17.25 meters.
TABLE 1. Open field static accuracy.
Open Field, Mobile Jammer. In these tests, the jammer was driven in a car on the road through the sensor field. The car was driven at 25 mph north to south, then 50 mph south to north (Figure 3). Cars in the north parking lot caused multipath errors when the jammer came in contact with that area.The overall average accuracy error for the dynamic tracking was 10 meters.
FIGURE 3. Jammer locations detected by Signal Sentry, when jammer was driven at 50 miles per hour, north to south. Green triangles denote sensor locations.
Obstructed Area Test. This test evaluated performance in an urban environment called a FIBUA (Fighting in Built-up Areas), using stationary and dynamic jammers. Seven waypoints in an area 176m x 253m were surveyed for the purpose of evaluating location accuracy. Table 2 shows the results with the 150mW jammer held stationary at the waypoints. Figure 4 provides a graphical view of the jammer position in relation to the waypoints. The overall average accuracy error is 21.40 meters.
TABLE 2. Urban static accuracy.
Obstructed Area, Mobile Jammer. In these tests, the jammer was driven in a car at approximately 20 mph on the road through the sensor field, using a .5W jammer. The overall average accuracy error for this dynamic tracking was 50 meters.
FIGURE 4. Urban area test; jammer locations in yellow, locations delivered by Signal Sentry in red, sensor locations in green.
All figures provided by Jeffrey Coffed and Joseph Rolli.
CSR is becoming Qualcomm Technologies International.
Qualcomm started the acquisition process for CSR in October 2014. With the expected close of the acquisition in two weeks on Aug. 13, the name of the company — Cambridge Silicon Radio Limited, or CSR — will be changed to Qualcomm Technologies International Ltd. (QTIL).
Here is the renamed company’s contact information:
Qualcomm Technologies International, Ltd.
Churchill House, Cambridge Business Park, Cowley Road
Cambridge, CB4 0WZ, UK
Emails will retain an @csr.com address until a QTIL address is created.
CSR is known to the GPS/GNSS industry as the maker of the SiRFstar series of chips, which are used in many consumer devices. Qualcomm is a leading maker of chips used in smartphones.
CSR issued a letter to its customers explaining the change, sent by Chris Dale, senior manager, CSR Global Procurement. Dale asked that customers make the appropriate change in their purchase order systems before Aug. 13. “As part of an ongoing program of review and improvement to trading arrangements and business processes, we will be updating our standard terms of purchase. You will be able to review the revised terms at qualcomm.com/procurement.”
The revised terms of purchase will apply to purchase orders issued by QTIL on and after Aug. 13.” Finally, please note that for the near term there will be no changes to the Accounts Payable or purchasing process or contacts,” Dale wrote.
QinetiQ today announced a major breakthrough in developing a robust navigation receiver that will use the Galileo, Europe’s satellite navigation system — in particular, the secured Public Regulated Service (PRS).
QinetiQ’s new high-performance, next-generation GNSS receiver is multi‑constellation and multi‑frequency, and is designed to process encrypted signals from the Galileo PRS service as well as open services such as GPS. Qinetiq introduced the receiver today at the UK Space Conference, being held July 13-15 in Liverpool.
The receiver — now a in prototype form — is a significant step towards developing an end-user product for navigation, tracking and timing, QinetiQ said. It will offer highly secure, accurate and reliable position, velocity and timing intended for users with a mission-critical need such as governments, the military and emergency services across Europe.
“We are delighted that, after years of QinetiQ R&D and collaboration with the EU, European Space Agency (ESA) and UK government, we have achieved this major step towards our goal of offering robust navigation products using Galileo,” said Nigel Davies, head of QinetiQ’s Secured Navigation Group. “It is a significant breakthrough for us to have built a fully operational receiver on a platform, which proves our product architecture, functionality and algorithms.”
“Our next step will be working to refine the product family and preparing it to be brought to market, which includes developing additional features and reducing its size to that of a postage stamp, in a form factor similar to our existing, highly successful Q20 receiver,” Davies said. “We have full confidence in this product and are proud to be at the forefront of this exciting new phase in European navigation.”
The prototype receiver is a multi-constellation, multi-frequency, all‑in‑view receiver that can receive and process the Galileo PRS as well as Galileo Open Service and GPS Standard Positioning Service. It is also designed to utilize other GNSS signals including the Russian GLONASS and Chinese Beidou systems as well as space-based augmentation services (SBAS) such as WAAS and EGNOS.
The receiver, which is based on the military standard SEM-E form factor, is also designed for integration into multi-sensor navigation systems and is designed to provide high levels of protection against jamming and spoofing. It has a fast acquisition capability and is designed for government security accreditation.
It is expected that a suite of robust products will be ready by 2020 to coincide with the completion of the Galileo project, which will be the world’s third GNSS to be completed after the United States and Russian systems.
The new receiver is part of a long pedigree in robust GNSS receivers. Q20 was QinetiQ’s first GPS receiver, designed for specific challenging applications: high dynamics, or high sensitivity like tracking from inside a shipping container. QinetiQ’s family of receivers will include two new products based on the new receiver. Q40 will be QinetiQ’s next-generation robust open service receiver, which will be a multi‑constellation, multi‑frequency open-service receiver which can use signals from all of the GNSS open services. Q50 will incorporate all of the functionality of the Q40 receiver, but also offer Galileo PRS for authorized users who need the additional capabilities and robustness.
“The device we have built is a major stepping stone to Q40 and Q50 as the technology has all been built for the receiver products and is designed to be shrunk on to a single ASIC microchip,” Davies said. “Our focus of attention will now be to turn what we have built into an ASIC product which is ready for market.”
Figure 1. Example of the GNSS signal propagation using ray-tracing and a 3D building map.
A particle-filter-based positioning method using a 3D map to rectify the errors created by multipath and non-line-of-sight signals on the positioning result delivered by a low-cost single-frequency GPS receiver takes a multi-GNSS approach, using the combined signals of GPS, GLONASS and QZSS. The method outperforms conventional positioning in availability and positioning accuracy. It will likely be fused with other sensors in a future pedestrian navigation application.
By Li-Ta Hsu, Shunsuke Miura and Shunsuke Kamijo
GPS provides an accurate and reliable positioning/timing service for pedestrian application in open field environments. Unfortunately, its positioning performance in urban areas still has a lot of room for improvement, due to signal blockages and reflections caused by tall buildings. The signal reflections can be divided into multipath and non-line-of-sight (NLOS) effects. Recently, use of 3D building models as aiding information to mitigate or exclude multipath and NLOS effects has become a promising area of study.
At first, researchers used the 3D map model to simulate multipath effects to assess the single-reflection environment of a city. Subsequently, the metric of NLOS signal exclusion using an elevation-enhanced map, extracted from a 3D map, was developed and tested using real vehicular data. An extended idea of identifying NLOS signals using an infrared camera onboard a vehicle has been suggested. The potential of using a dynamic 3D map to design a multipath-exclusion filter for a vehicle-based tightly coupled GPS/INS integration system has also been studied. A forecast satellite visibility based on a 3D urban model to exclude NLOS signals in urban areas was developed.
The research approaches outlined above seek to exclude the NLOS signal; however, the exclusion is very likely to cause a horizontal dilution of precision distortion scenario, due to the blockage of buildings along the two sides of streets. In other words, the lateral (cross direction) positioning error would be much larger than that of the along-track direction.
Therefore, approaches applying multipath and NLOS signals as measurements become essential. One of the most common methods, the shadow-matching method, uses 3D building models to predict satellite visibility and compare it with measured satellite visibility to improve the cross street positioning accuracy. A multipath and NLOS delay estimation based on software-defined radio and a 3D surface model based on a particle filter was proposed and tested in a static experiment in the Shinjuku area of Tokyo.The research team of The University of Tokyo developed a particle-filter-based positioning method using a 3D map to rectify the positioning result of commercial GPS single-frequency receiver for pedestrian applications.
An evaluation of the QZSS L1-submeter-class augmentation with integrity function (L1-SAIF) correction to the proposed pedestrian positioning method was also discussed in an earlier paper by the authors of this article. However, satellite visibility in the urban canyon using only GPS and QZSS would not be enough for this proposed method. The use of emerging multi-GNSS, encompassing GLONASS, Galileo and BeiDou, could furnish a potential solution to the lack of visible satellites for this method. This article assess the performance of the proposed pedestrian positioning method using GPS, GLONASS and QZSS.
Building Models Construction
Our work established a 3D building model by a 2D map that contained building location and height information of buildings from 3D point-clouds data. The Fundamental Geospatial Data (FGD) of Japan, which provided by Japan geospatial information authority, is open to the Japanese public. This FGD data is employed as 2D geographic information system (GIS) data. Thus, the layouts and positions of every building on the map could be obtained from the 2D GIS data. In this article, the 3D digital surface model (DSM) data is provided by Aero Asahi Corporation. Figure 2 shows the process of constructing the 3D building model used here. This process first extracts the coordinates of every building corner from FGD as shown in the left of Figure 2. Then, the 2D map is integrated with the height data from DSM. The right of Figure 2 illustrates an example of a 3D building model established in this way. The 3D building map contains a very small amount of data for each building in comparison to that of the 3D graphic application. For our purposes, the file only contains the frame data of each building instead of the detail polygons data. This basic 3D building map is utilized in the simulation of ray-tracing.
Figure 2. The construction of the 3D building map from a 2D map and DSM.
Our version of the ray-tracing method does not consider diffractions or multiple reflections because these signals occurred under unfavorable conditions. Here, we utilize only the direct path and a single reflected path. The developed ray-tracing simulation can be used to distinguish reflected rays and to estimate the reflection delay distance. Our research work assumes that the surfaces of buildings are reflective smooth planes, that is, mirrors. Therefore, the rays in the simulation obey the laws of reflection. In the real world, the roughness and the absorption of the reflective surface might create a mismatch between the ray-tracing simulation and the real propagation. Here we ignore this effect, as the roughness of the building surface is much smaller than the propagation distance.
The opening figure (Figure 1) shows an example of the GNSS signal propagation using ray-tracing and a 3D building map. Red, green and white lines denote the LOS path, reflected paths and the NLOS paths, respectively. In this environment, a conventional positioning method such as weighted least squares (WLS) usually estimates the position on the wrong side of street as shown in the red balloon. With the aid of 3D building model and ray-tracing, the map-based positioning method is able to provide a result close to the ground truth.
Map-Based Pedestrian Positioning
The flowchart of the 3D city building model-based particle filter is shown in Figure 3. This method first implements a particle filter to distribute position candidates (particles) around the ground-truth position. In Step 2, when a candidate position is given, the method can evaluate whether each satellite is in LOS, multipath or NLOS by applying the ray-tracing procedure with a 3D building model. According to the signal strength, namely carrier-to-noise ratio (C/N0), the satellite could be roughly classified into LOS, NLOS and multipath scenarios. If the type of signal is consistent between C/N0 and ray-tracing classification, the simulated pseudorange of the satellite for the candidate will be calculated. In the LOS case, simulated pseudoranges can be estimated as the distance of the direct path between the satellite and the assumed position. In the multipath and NLOS cases, simulated pseudoranges can be estimated as the distance of the reflected path between the satellite and the candidate position via the building surface.
Figure 3. Flowchart of the particle filter using 3D city building models.
Ideally, if the position of a candidate is located at the true position, the difference between the simulated and measured pseudoranges should be zero. In other words, the simulated and measured pseudoranges should be identical. Therefore, the likelihood of each valid candidate is evaluated based on the pseudorange difference between the pseudorange measurement and simulated pseudorange of the candidate, which is simulated by 3D building models and ray-tracing.
Finally, the expectation of all the candidates is the rectified positioning of the proposed map method. This method can therefore find the optimum position through a dedicated optimization algorithm of these assumptions and evaluations. The positioning principle of the proposed method is very different from the conventional GPS positioning method, that is, WLS. As a result, the calculation of the positioning accuracy of the 3D map method should be also different.
We define two positioning performance measures for the 3D map method: user range accuracy of the 3D map method (URA3Dmap) and positioning accuracy.
The value of URA3Dmap is to indicate its level of positioning service, which is similar to the user range accuracy (URA) of conventional GPS. The URA3Dmap is defined based on the percentage of the valid candidates from all candidates outside the building. The higher percentage of the valid candidate implies a higher confidence of the estimated position. Ideally, if the center of the candidate distribution is not far from the ground truth, the simulated pseudorange of the candidates located at the center of distribution would be very similar to the measurement pseudorange. We define the URA3Dmap as shown in Table 1.
Table 1. The definition of URA and URA3Dmap used in this article.
Experiments and Discussion
We selected the Hitotsubashi and Shinjuku areas in Tokyo to construct a 3D building model because of the density of the tall buildings. In this area, multipath and NLOS effect are frequently observed. We tested pedestrian navigation in a typical path that included walking both sides of street and passing through/waiting at a road intersection. The cut-off angle is 20 degrees. The data were collected in November and December 2014.
We compare here two single point positioning methods: single-point positioning solutions provided by open source RTKLIB software (RTKLIB SPP), and the proposed 3D map method. RAIM FDE of the RTKLIB SPP is used here as a conventional NLOS detection algorithm. The test used a geodetic-grade GNSS receiver and a commercial grade receiver. The geodetic receiver was only used to collect the QZSS L1-SAIF correction signal. The antenna of the commercial receiver was attached in the strap of the backpack as shown in Figure 4. The receiver is connected to a tablet to record the GNSS measurements and is set to output pseudorange measurements and positioning results every second.
Figure 4. Equipment set-up.
We generated a quasi-ground truth using a topographical method.Video cameras were set in the ninth and18th floors of a building near the Hitotsubashi and Shinjuku areas, respectively, to record the traveled path. The video data output by the cameras are used in combination with one purchased high-resolution aerial photo to get the ground truth data. The aerial photo is 25 cm/pixel and therefore the error distance for each estimate can be calculated. The synchronization between video camera and commercial GNSS receiver is difficult to get as accurate as in the topographical method. As a result, we used point to “points” positioning error to evaluate the performance of the dynamic experiment. The synchronization error is limited to 1 second. Hence, for each estimated position x(t), the ground truth points used to calculate the positioning error is xGT (t-1), xGT (t) and xGT (t+1). The point to “points” positioning error is calculated as:
Three performance metrics are used here: mean, standard deviation of the point to points error, and the availability of positioning solution. The availability defined here means the percentage of given solutions in a fixed period. For example, if a method outputs 80 epochs in 100 seconds, the availability of the method is 80 percent.
This research demonstrates two dynamic data. The skyplot of the data are shown in Figure 5. The satellites are tracked by the commercial receiver. The grey areas indicate the obstruction of the surrounding buildings. The two dynamic data are typical signal receptions at Hitotsubashi (middle urban canyon) and Shinjuku (deep urban canyon) areas.
Hitotsubashi Mid-Canyon. To study the benefit of using different GNSS constellations in the 3D map method, Figure 6 shows the trajectory estimated by the proposed method under different satellite constellations. The different colors indicate different values of URA3Dmap of each point. This walking trajectory is divided into five sections (identified as A, B, C, D and E in the right-most of the three plots). In the GPS-only case (left), results in A and B sections have much better performance than sections D and E, because more than half of the GPS satellites are blocked at D and E, as shown in the left of Figure 5.
Figure 5. The left and right are the skyplot of the dynamic experiment at the Hitotsubashi and Shinjuku areas, respectively, in Tokyo.
The middle plot in Figure 6 shows the trajectory using GLONASS. It is obvious that the positioning results located at the right side of street are greatly increased, derived from the greater number of satellites in view. However, the quality of the GLONASS signal is not as good as GPS because multipath has a double effect on GLONASS.
Figure 6. Positioning results of the proposed 3D map method using different combinations of satellite constellations in a middle urban canyon.
In summary, the positioning error of applying GLONASS maintains a similar level, and availability increases about 12 percent compared to using GPS only. The right plot of Figure 6 shows the result after adding QZSS L1 C/A and L1-SAIF. This increases the results of C, D and E sections, because QZSS provides a high-elevation-angle satellite to the 3D map method. As a result, the number of valid candidate points in C, D and E sections increases dramatically. The reliability in C, D and E sections is also much higher than that of GPS+GLONASS. In addition, the trajectory became smoother than before.
Table 2 compares the positioning results of both RTKLIB SPP and the 3D map method, showing the 3D map method using GPS, GLONASS and QZSS to have the best performance among three scenarios. The positioning error mean and availability are 3.89 meters and 96.72 percent, respectively. The positioning error mean could be further improved to 3.23 meters if selecting the position point with URA3Dmap ≤ 3 (yellow, orange and red points in Figure 6). This selection will lose about 17 percent of availability.
Table 2. Positioning results of the 3D map method using different combinations of satellite constellations in a middle urban canyon.
Shinjuku Deep Canyon. We conducted a similar experiment in the Shinjuku area of Tokyo, the most urbanized area in Japan (Figure 7). The positioning results and skyplot are shown in Figure 8 and the right of Figure 5, respectively. Table 3 compares the results of the two methods using the three constellation configurations.
Figure 7. Deep urban canyon environment, Shinjuku, Tokyo. (Courtesy Google Earth)Figure 8. Positioning results of the proposed 3D map method using different combinations of satellite constellations in a deep urban canyon.Table 3. Performance comparison of RTKLIB SPP and the proposed 3D map method using different combinations of satellite constellations in a deep urban canyon.
As shown in the left of Figure 8, only half of the GPS-only solutions are on the correct side of the street. A few points are incorrect due to the insufficient number of satellites. Adding GLONASS measurements greatly increases the availability, and most of the GPS-only outliers are corrected. The positioning error mean improves from 12.7 to 10.3 meters, and the availability improves from 53.2 to 75.9 percent. GLONASS measurements provide such a significant improvement because the distribution of GPS and GLONASS satellites are complementary.
After adding the QZSS measurements, availability further increases to 88.6 percent, and positioning error mean is reduced to 5.7 meters. The positioning error mean could be further improved to 4.2 meters if selecting the position points with URA3Dmap ≤ 3: the red, orange and yellow points in Figure 8. Although this selection will lose about 12 percent of availability, it could be easily compensated by a simple filtering technique.
Comparing Table 2 and Table 3, we find the positioning error of the proposed method in the middle urban canyon is about 1 meter worse than that in the deep urban canyon. This is because of the increase of multiple reflected signals.
The target application of this 3D map method is consumer-based pedestrian navigation. Most of these applications benefit from an integrated system of multiple sensors. The 3D map method could serve as one sensor for such an integrated system. The calculation of positioning accuracy is required to indicate the quality of the point solution estimated by this method. Figure 9 shows the relationship between the calculated accuracy and positioning error. We can find that the calculated accuracy is able to describe the performance of the proposed method.
Figure 9. Positioning error of the 3D map method using GPS+GLONASS+QZSS. The purple line denotes the calculated 68 percent accuracy of the proposed method.
The performance of the conventional method is very inaccurate in this deep urban canyon. Its positioning error is larger than 40 meters. Figure 10 shows the number of satellites in this data. Note the number of LOS satellites is determined by the ray-tracing simulation according to the ground truth trajectory.
Figure 10. Number of LOS satellites, the number of satellites used in the 3D map method, and the total number of satellites tracked by the commercial-grade receiver.
The number of LOS satellites means the light-of-sight path of satellite is not blocked by buildings. Note that the LOS signal also contains the multipath effect. In this deep urban canyon, the number of LOS signals is much less than that of all received satellites. This implies a lot of NLOS is received, which deteriorates the performance of the conventional method. The map-based method is able to correct most of the NLOS signals.
The number of satellites used in the map-based method is close to the number of all the satellites received. Therefore the map-based method can achieve better performance than the conventional method. Figure 11 demonstrates the comparison between the map-based method and the commercial GNSS receiver. The map-based method is simply smoothed by a moving average filter with 3 seconds data. It is difficult to understand the pedestrian trajectory by the commercial-grade receiver result. In some cases, the commercial receiver will estimate the pedestrian to be on the wrong side of the streets. The proposed method, instead, is capable of estimating the result at the correct side of the street.
Figure 11. Positioning results of the proposed 3D map method and commercial-grade receiver using GPS+GLONASS+QZSS in the deep urban canyon.
Li-Ta Hsu is a post-doctoral researcher at the Institute of Industrial Science of the University of Tokyo. He received his Ph.D. degree in aeronautics and astronautics from National Cheng Kung University, Taiwan.
Shunsuke Miura received an M.S. degree in information science from the University of Tokyo in 2013.
Shunsuke Kamijo received a Ph.D. in information engineering from the University of Tokyo, where he is now an associate professor.
Satel presents the tiny UHF radio data transceiver module Satelline TR4.
The new Satelline TR4 from Satel, a Finnish manufacturer of radio data transmission systems, is a compact UHF transceiver with transmitting power of 1,000 mW. The transceiver is compatible with the protocols of Pacific Crest, Trimble and Satel.
The type certifications in important regions of the world make the TR4 ideal for integration in end devices intended for international use. With a weight of only 18 g, transmitting power of 1,000 mW and an “over the air” data transmission rate of 38,400 bps, it fulfills all present-day standards, Satel said.
The tiny UHF transceiver is designed for easy integration, with dimensions of 56 x 36 x 6 millimeters, Satel said. Robust UHF frequencies (400-470 MHz) are a reliable basis for the communication of self-sufficient stations even in the event of unknown topology. In addition, the new TR4 features the advantages of the Satelline EASy and 3AS modems, including channel scanning, error correction and compatibility with protocols of Pacific Crest, Trimble and, of course, Satel.
Since license-free frequencies are becoming more popular, such as 869 MHz in Europe and 915 MHz for the American market, transceiver modules of identical design will follow. The identical footprint and the standardized communication commands will minimize integration costs. Later it will only be necessary to insert the corresponding radio module for the destination and the end device will be ready to use.
In Germany, radio data transmission solutions from Satel are distributed exclusively by the full-range and systems provider Welotec. At INTERGEO in Stuttgart, September 15 – 17, the partners will present their products at adjacent stands: Satel and Welotec will be in Hall 4 – Booth G4.020.
PlanetiQ has selected Blue Canyon Technologies to build its weather satellite constellation, set to launch in 2016 and 2017. PlanetiQ chose BCT as a partner in developing the world’s first commercial constellation dedicated to weather, climate and space weather based on BCT’s development track record and its cutting-edge, low-cost design approach that has delivered hundreds of components and systems for numerous space missions, PlanetiQ said.
“Weather is the next commercial space frontier, as demand grows not only for better forecasts of day-to-day weather, severe storms and hurricanes, but also for weather and climate data solutions that enhance weather readiness, support risk management and increase business intelligence,” said Anne Hale Miglarese, president and CEO of PlanetiQ. “Together, PlanetiQ and BCT bring the innovation, technical expertise and experience to cost-effectively produce the high-quality data needed to transform the weather satellite industry and deliver unprecedented economic value.”
PlanetiQ has co-located its aerospace engineering team at BCT’s Boulder facilities, where both the satellites and sensors will be manufactured and integrated, and is already working side-by-side with BCT on the initial set of 12 microsatellites. Working together with the PlanetiQ team, BCT has dramatically reduced the satellite size and weight without sacrificing any instrument capabilities.
“We are certainly pleased to be chosen by PlanetiQ. Weather is emerging as a major growth sector for aerospace, and our partnership with PlanetiQ positions BCT and the state of Colorado to play a leading role,” said George Stafford, president and CEO of BCT. “Our systems and components match well with PlanetiQ’s instrument requirements, and we are glad to be working on this spacecraft and mission.”
In early June, PlanetiQ announced the successful testing of its first “Pyxis” weather sensor and is setting up for production with BCT. Pyxis collects dense, precise measurements of global temperature, pressure and water vapor — similar to data collected by weather balloons but on a global scale — using a technique called GPS Radio Occultation (GPS-RO). Among the satellite data sources currently ingested into computer weather models, GPS-RO has shown the most cost-effective, highest impact per observation on forecast accuracy. But only a sparse amount of GPS-RO data exists today.
Pyxis is the only GPS-RO sensor in such a small package that is powerful enough to provide more than 10 times the amount of data available from GPS-RO sensors currently on orbit, and to routinely probe down into the lowest layers of the atmosphere where severe weather occurs.
“The small size and weight of the Pyxis sensor — combined with BCT’s high-performance mission experience — will allow us to quickly field a constellation to provide the highest quality, most cost-effective weather data ever available,” said PlanetiQ FounderChris McCormick, who leads PlanetiQ’s instrument team and developed the sensors for the only GPS-RO constellation that has provided operational weather forecast data. “With 12 satellites providing 8 million data points per day, GPS-RO will easily become the most important contributor to weather forecast accuracy at a fraction of the cost of traditional weather satellites.”
SatGen v3 software is now available for the LabSat GNSS simulator by Racelogic. Version 3 includes BeiDou (BDS) in addition to GPS and GLONASS.
SatGen is billed as a powerful and intuitive software package that gives users the ability to create scenarios for replay through any LabSat simulator. The software creates either user-generated or imported trajectory files for use with a LabSat simulator.
The addition of the BeiDou B1 signals means that users can now test a device’s effectiveness as if it were being used within the operating area of the Chinese constellation, which at present only provides full coverage in Asia.
The BeiDou constellation is set to become globally operational by 2020. With the new SatGen v3, users can create scenarios that include signals from satellites yet to be launched, so new products can be developed in readiness for the full constellation.
SatGen v3 can produce scenarios with one, two or three sets of signals being simultaneously output: GPS, GLONASS and now BeiDou. The software now matches the record and replay abilities of the LabSat 3 simulator.
A trial of SatGen is available here. To purchase a full copy contact a LabSat distributor.
Averna has launched an RF tool offering high-performance record-and-playback and real-time simulation in one platform.
The Averna RP-6100 series is a self-contained, record-and-playback solution for RF application validation. It can capture all GNSS bands, as well as HD Radio, Wi-Fi, LTE, radar, and cognitive radio — plus impairments — to significantly advance RF projects and harden product designs. The RP-6100 series features up to four channels, 160 MHz of recording bandwidth, tight channel synchronization, an extended frequency range of 10 MHz to 6 GHz, and 14-bit resolution.
The RP-6100 can also be equipped with Skydel Solutions’ software-defined, real-time GNSS simulator, which delivers easy setups, integrated maps, dynamic scenario creation, high precision and tight parameter controls to enable highly repeatable simulations of current and future GNSS conditions, as well as corner cases.
Features include:
Frequency range of 10–6000 MHz, covering all GNSS bands, plus HD Radio, WiFi, LTE, and more
Multi-channel (1-4): Up to 160 MHz of bandwidth at 14-bit resolution (< 1 Hz)
3.8 TB SSD storage or 16 TB HDD storage (for up to 22 hours of recordings)
Preloaded with RF Studio software for quick setups and in-depth analysis
Four models: RP-6120 (2 ch.), RP-6120P (2 ch. portable), RP-6120D (2 ch. desktop) and RP-6140 (4 ch.)
Optional real-time Skydel GNSS Simulator for complete GNSS corner-case/testing scenarios
“We are very excited to partner with Skydel Solutions as a way to continue to provide our customers with the latest technologies and products,” said Benoit Richard, VP of Innovation & Strategy at Averna. “Their technology maps perfectly with our portfolio of RF instrumentation solutions, which empower device manufacturers to efficiently generate, record, simulate, analyze, and play back all common radio, video, and navigation signals, ensuring complete test coverage and the highest quality for their RF products.”
“Today, Skydel is proud to introduce its software-defined GNSS Simulator, running in real-time Ettus and NI USRP hardware,” said Stéphane Hamel, co-founder and CEO of Skydel Solutions. “We are also very pleased to announce that our GNSS Simulator can be combined with Averna’s RP-6100 Series. These technologies complement each other perfectly, making the combined solution the ideal platform for high-performance design validation of RF and GNSS devices.”
