GNSS receivers in cell phones will soon support four or more satellite constellations and derive additional location measurements from other sources: cellular location, MEMS sensors, Wi-Fi, and others. The authors propose test standards covering these sources, meeting industry requirements for repeatable testing while considering the user experience.

By Peter Anderson, Esther Anyaegbu, and Richard Catmur

Cellular location test standards include well-defined and widely used standards for GPS-based systems in both the 3rd Generation Partnership Program cellular technologies of GSM/WCDMA/LTE, typically referenced as the 3GPP standards, and for CDMA technologies in the 3GPP2 standards. These standards provide a reference benchmark for location performance in the laboratory, when the unit under test is directly connected to the test system via a coax connection. In addition, standards are being rolled out, such as the CTIA ­— The Wireless Association total isotropic sensitivity (TIS) requirement, for over-the-air (OTA) testing and developed further with LTE A-GPS OTA using SUPL 2.0. These tests are typically performed in an anechoic chamber and allow the performance of the antenna to be included.

Recently developed standards such as the 3GPP Technical Specification (TS) 37.571-1 cover multi-constellation systems, typically GPS and GLONASS for a two-constellation system, or GPS, GLONASS and Galileo for a three-constellation system, with options for additionally supporting QZSS and space-based augmentation system (SBAS) satellites. During 2014, the standards will encompass additional constellations such as the BeiDou satellite system.

Figure 1A. GNSS systems available in the 2015-2020 timescale.

Figure 1B. GNSS systems available in the 2015-2020 timescale.

Significant change is also happening with the additional technologies such as cellular location, Wi-Fi, and micro-electromechanical systems (MEMS) sensors providing location information. Hybrid solutions using all/any available location information from these multiple technologies present significant challenges to both the test environment and the related test standards.

The acceptance levels required for the platform integrators and their customers are becoming much more stringent, as the use cases of the location become more diverse. These present further challenges to the performance requirements for test standards for cellular location.

Measuring Performance

The rapid growth in the GNSS applications market has driven users to demand improvements in the performance and reliability of GNSS receivers. The test standards currently employed by cellular phone and network manufacturers to evaluate the performance of GNSS receivers are even more stringent than the regulatory mandates for positioning of emergency callers and other location-based services. Emergency-call positioning is an example of a service that must provide a position fix in both outdoor and indoor environments.

A user’s experience with a GNSS receiver begins when he switches on the device. The quality of his experience defines the basic performance criteria used to assess the performance of a GNSS receiver.

• How long did it take to get a position fix?
• How accurate is the position fix?
• When the fix is lost, how long did it take the device to reacquire satellites and re-compute the fix?

These expectations  define the performance of the GNSS receiver. Manufacturers use these performance metrics to compare the performance of different GNSS receivers.

The receiver’s time-to-first-fix (TTFF) depends on the initial conditions; that is, the type of acquisition aiding data (almanac data, ephemerides, knowledge of time and frequency, and so on) available to the receiver when it is switched on.

Users now expect location-based applications to work regardless of where they are and whether they are in a fixed location or on the move. They expect the same level of performance when they are indoors at home or at work, as outdoors in a rural or urban environment. This has led to an increased demand for accurate and reliable outdoor and indoor positioning.

Reacquisition time — how quickly a receiver recovers when the user goes through a pedestrian underpass or under a tunnel or a bridge, for instance — is not tested in any of the existing test standards discussed here.

The useable sensitivity of any GNSS receiver is key to its performance. It defines the availability of a GNSS positioning fix. The acquisition sensitivity defines the minimum received power level at which the receiver can acquire satellites and compute a position fix, while the tracking sensitivity of a receiver defines the minimum received power level at which a GNSS receiver is still able to track and maintain a position fix.

Different applications use different criteria to characterize the performance of a GNSS receiver. In an E911 scenario, for instance, position accuracy and response time are critical, whereas for navigation while driving, accuracy and tracking sensitivity are important. The test criteria employed by different manufacturers are intended to verify the suitability of a particular device for the required application.

The initial test conditions are defined by the manufacturers to ensure that the different devices are tested in the same way. These conditions describe how the test sessions are started, and what acquisition aiding data are available at the start of the test session.

The main divisions among performance tests are:

• Laboratory-based tests, either conducted versus OTA RF testing, or simulated versus record-and-playback signal testing.
• Real-world testing (field testing). This can be difficult because the test conditions are never the same. Fortunately, it is possible to record these scenarios using an RF data recorder. This allows the same real-world scenario (with the same test conditions) to be tested repeatedly in the lab.
• Static scenario testing versus moving scenario testing.
• Comparison tests — relative testing (comparing one receiver against another): for reported signal-to-noise ratio (SNR), reported accuracy, and repeatability tests.

Current GNSS Test Standards

Varying performance requirements test the TTFF, accuracy, multipath tolerance, acquisition, and tracking sensitivity of the GNSS receiver. The first three following are industry-defined test standards:

3GPP2 CDMA Performance Standards. The 3GPP2 CDMA test standards (C.S0036-A) are similar to the 3GPP test standards. The 3GPP2 is for CDMA cellular systems, which are synchronized to GPS time.

3GPP GNSS Performance Standards. The latest 3GPP TS 37.571-1 test standard describes the tests for the minimum performance requirements for GNSS receivers that support multi-constellations. It is slightly more stringent than the original 3GPP TS 34.171 test standard. In the 3GPP TS 37.571-1 coarse-time sensitivity test case, signals for only six satellites are generated, whereas in the TS 34.171 coarse-time sensitivity scenario, signals for eight satellites are generated.

Table 1 shows the power levels and satellite allocation for a multi-constellation 3GPP TS 37.571-1 coarse-time sensitivity test case. In this scenario, the pilot signal will always be GPS, if GPS is supported. The signal level of the pilot signal for GPS and GLONASS have been set as –142 dBm, while the non-pilot signal level for GPS and GLONASS have been set as –147 dBm.

Table 1. 3GPP TS 37.571-1 Satellite allocation.

For the 3GPP TS 37.571-1 fine-time assistance test case, six satellites are generated. For the dual-constellation fine-time test, the split is 3+3, and for a triple-constellation test case, the split is 2+2+2, as shown in Table 2.

Table 2. 3GPP TS 37.571-1 fine-time satellite allocation.

OTA Requirements. Testing standards have been rolled out for OTA testing, where the testing is typically performed in an anechoic chamber, allowing antenna performance to be included, with tests for the receive sensitivity referenced to an isotropic antenna and over partial summations such as the upper hemisphere. They measure the TIS of the final receiver, and operator requirements typically require  OTA acquisition sensitivity of –140 dBm and tracking sensitivity of –145 dBm or lower.

Other modified test standards used by manufacturers to assess the performance of the GNSS receiver include:

Nominal Accuracy Margin Test. This test is based on the 3GPP nominal accuracy test case. All signals are reduced in steps of 1 dB till the test fails to achieve a fix in 20 seconds.

Dynamic Range Margin Test. This test is based on the 3GPP dynamic range test case. All signals are reduced in steps of 1 dB till the test fails to achieve a fix in 20 seconds.

Sensitivity Coarse-Time Margin Test. This test is based on the 3GPP sensitivity coarse-time test case. Both the pilot and non-pilot signals are reduced in steps of 1dB till the test fails to achieve a fix in 20 seconds.

Pilot Sensitivity Coarse-Time Margin Test. This test is based on the 3GPP coarse-time sensitivity test case. The non-pilot signals are always kept at –152 dBm while the signal level of the pilot signal is reduced in steps of 1 dB till the test fails to achieve a fix in 20 seconds.

Non-Pilot Sensitivity Coarse-Time Margin Test. This test is based on the 3GPP coarse-time sensitivity test case. In this test, the pilot signal is always kept at –142 dBm while the signal levels of the other seven non-pilot signals are reduced in steps of 1 dB till the test fails to achieve a fix in 20 seconds.

These modified performance tests are used because they map directly to the end-user’s experience in the real world, measuring the position accuracy, response time, and sensitivity of the GNSS receiver.

Current Equipment. The equipment required for the current test standards are all GNSS multi-satellite simulator-based, either using a single constellation (for GPS), or a multi-constellation GNSS simulator as a component of a larger cellular test system.

Limitation of Current Standards

So far, tests for GNSS in cellular devices have been very much customer/manufacturer specific, starting with 3GPP-type tests, but adding to them. Each will have its own preferred type of tests, with different configurations and types of tests. They have included primarily GNSS simulator tests, either directly connected to the device under test or using radiated signals, together with some corner cases. With chips such as the ST-Ericsson CG1960 GNSS IC, this means that different tests need to be performed for each customer.

Typically the tests are focused on cold or hot TTFF type tests, or sensitivity type tests. Live signal tests have typically been used for drive tests, with a receiver being driven around an appropriate test route, normally in an urban environment. More recently RF replays have become much more widely used, but do require truth data to give validity. RF replay tests are typically used for specific difficult routes for urban drive tests or pedestrian tests.

The 3GPP types of test standards were developed to provide a simple set of repeatable tests. However, they are idealistic, and they do not relate closely to any real-world scenario, and the test connection is defined to be at the antenna port of the system. In reality, different manufacturers and network operator standards take these tests as a given, and define margins on the tests to allow for typical losses due to antennas and implementation on a platform. These margins might be as much as 8 or 10 dB. In addition, manufacturers and network operators define their own variants of the 3GPP tests to match typical real-world usage cases, such as deep indoor.

Challenges

Current location test specifications assume that the key input to the location calculation is always the GPS constellation. With the rise of additional constellations and alternative location sources, and the challenges of the urban environment, GPS will be one of many different inputs to the location position. The key for the future will be for standards focused on testing location performance, irrespective of which constellations are visible, and also being able to fully test the system performance. Tests will be suggested that allow the basic functionality of a system to be checked, but can be enhanced to stress-test the performance of a receiver. As future location systems will use all available inputs to produce a location, there will be challenges to the supporting test standards and test equipment to handle all of these in parallel.

The initial challenge for location test standards has been the use of GNSS constellations in addition to GPS. Current leading GNSS receivers in cellular devices make use of GPS, GLONASS, SBAS, and QZSS, and network-aiding information for A-GLONASS is being rolled out in the cellular networks. The 3GPP TS 37.571-1 specification has been derived from the original GPS-only specification TS 34.171, with the addition of GLONASS and Galileo constellation options. These allow single-, dual-, or triple-constellation tests to be performed. If there is GPS in the system, then GPS is viewed as the primary constellation, and tests like the sensitivity coarse-time assistance test would have a satellite from the GPS constellation with the highest signal level. The test standards also accommodate the use of some satellites from SBAS such as WAAS and QZSS. These tests require that the performance shall be met without the use of any data coming from sensors that can aid the positioning.

This is only the first stage in the rollout of new GNSS constellations, and in the near future, GNSS receivers in cellular phones will support four or more constellations, and possibly also on frequencies additional to the L1 band, covering some or all of: GPS, GLONASS, Galileo, BeiDou Phase 2, BeiDou Phase 3, QZSS, SBAS, and IRNSS.

Table 3. Suggested four-constellation mix (Pilot signal to rotate round constellations).

The challenge for the minimum-performance specifications is to accommodate these different constellations as they become fully available. For the new constellations, this will initially be purely simulator-based, but could be extended to use of live data for certain test cases as the constellations are built up. A further challenge for the test specifications is that some of the systems are regionally based, so a performance specification based on a global approach is not applicable.

Further, tests must be severe enough to stress the receiver. With multiple constellations, it can be simple to pass a test without using all available satellites or constellations.

Other Location Sources (Hybrid Solution). Within the cellular platform, location can be provided by a number of different technologies, either separately or compositely, to provide a location to the accuracy required by the user. Technologies currently available include:

• Cellular network: cell ID and cell network triangulation
• LTE Positioning Protocol
• Fine time assistance (for aiding)
• Wi-Fi network name (service set identifier, or SSID)
• Wi-Fi ranging
• MEMS sensors
• Near-field communication
• Bluetooth
• Pseudolites, other beacons, coded LED lights, and so on.

Real-World Environments. Measuring performance in a real environment is becoming much more important, as the user experience becomes much more key. The product must not only pass particular specifications, but must also meet customer expectations. In the age of the blog, negative customer feedback can damage a product’s reputation. But with the various GNSS constellations and other sources of location information, performance testing is growing significantly in complexity, and test standards needed to cover this complexity will also become more complex. The simple user criteria could be stated as “I want the system to provide a rapid, accurate position wherever I am.” But how accurate?

The end-user of a location system does not use a GNSS simulator with clean signals, but a location device with live signals, often in difficult environments. This has been recognized by platform integrators, and live test routes for both urban drive and urban pedestrian routes are now required. The performance required of the receiver in these locations has also changed, from “just need to get a fix of limited accuracy” to getting accurate location information, both from a fix (even from a cold start in a built-up area), to continuous navigation (better than 30-meter accuracy 99 percent of the time) throughout a test run.

Typical environments for these test cases include locales in many major cities, such as the environment in the OPENING PHOTO  of Seattle and one shown here of Seoul, Korea.

Seoul, Korea, a typical test-case environment.

Coexistence and Interference. Recent controversies have raised the profile of GNSS interference from other wireless technologies. However, within the cellular platform, significant coexistence and potential interference issues are already present. These can occur due to adjacent channel interference, or from harmonics of cellular frequencies on the platform, for example, the second harmonic of the uplink channel for LTE Band 13 overlays the BeiDou-2 frequency of 1561MHz, and the second harmonics of both Bands 13 and 14 create out-of-band emissions in the GPS band (Figures 2 and 3).

Figure 2. BeiDou and LTE bands 13/14.

Figure 3. GPS and LTE bands 13/14.

Test Proliferation. The increase in the number of GNSS constellations together with the use of other location sources to provide a hybrid solution could increase the number of tests to be performed exponentially. When this is then combined with the need to test over a range of simulated and real-world locations, together with customer specific requirements, a set of tests could easily take weeks to run. It is therefore important to ensure that the cellular location test standards are carefully constructed to not significantly proliferate the number and time for tests to be performed.

Future Test Equipment

A new generation of test equipment is emerging to meet the new challenges and requirements of multi-constellation GNSS and hybrid location systems. These include:

GNSS Simulators. Simulators currently provide up to three GNSS constellations, together with augmentation systems. With the roll-out of BeiDou-2, four-constellation simulators will now be required. Currently all GNSS devices integrated in cellular platforms use the L1 band. This will also potentially change to multi-frequency use. The appropriate GNSS simulator will need to be included in the cellular test system.

New Hybrid Test Systems. As the need for testing hybrid positioning systems in cellular devices emerges, hybrid location test systems (HLTS) are becoming available that can simulate and test hybrids of A-GNSS, Wi-Fi, MEMS sensors, and cellular positioning technologies, all in one system.

Today, these test systems use separate simulators for the different individual technologies (like GNSS, Wi-Fi, and so on), but these are now being merged into multi-system simulators that combine a number of different technologies into one device (see Figure 4).
RF Replay. The use of RF replay units for replicating live trials is already widespread. This will extend with further constellations and further frequency bands.

Figure 4. Hybrid Location Test System (HLTS).

The advantages of using RF recorded data include:

• Gives real-world data, which if the location is chosen carefully will stress the device under test;
• Allows use of recorded test data from several/many urban locations;
• Good for drive and pedestrian test applications;
• Will be integrated in the HLTS type of test system.

The disadvantages of using RF recorded data include:

• Results not deterministic;
• Taken at one point in time, do not allow for future development of satellite constellations;
• Proprietary recording devices, difficult to define a standard;
• Need to include an inertial measurement unit (IMU) to get accurate truth data.

The difficulties of using RF replays include:

• Successfully integrating all the signal environment (cellular, Wi-Fi, MEMS, and so on);
• Multiple runs required to give reliable data (for example, 13 runs at different times of day to give a range of satellite geometry and user speed, between rush hour and middle of night);
• Multiple locations required to stress the system;
• Test time can be up to a day of real-time testing to re-run tests on one location.

Proposal for Hybrid Positioning

Tests should include a mixture of simulator-based tests, RF-replay-based tests, and live tests. This would comprise the following suite:
GNSS Performance Tests. The 3GPP type of tests (TS 37.571-1) are a good starting point for a minimum performance test, but they rely on the person running the test to define the number of constellations. To automate this, there could be a single test at the start of each test sequence to identify which constellations are supported (one to four), and then the formal test run for that mix of constellations. The constellations supported should be reported as part of the test report.

An option should be provided to allow margin tests for specific tests to be run, and these should again be reported in a standard method in the test report, specifying how far the device under test exceeds the 3GPP test. The typical margins expected for a GPS-only test would be between 8 and 10 dB in the 2014 timeframe. For a multi-constellation test, it will depend on the specific constellations used, but could be between 5 and 8 dB margin.

Ideally, a multipath scenario should be created that more closely matches the environment seen in a real urban environment.
Hybrid Location Tests. The main purpose of the hybrid location test is to prove that the different components of a cellular platform providing location are all operating correctly. A basic test would provide a sequence where the different combinations providing location are tested for correct operation separately, and then together. This would not be envisaged as a complete stress test, but each technology should be running in a mode where a location solution is not simple.

A simple example sequence of tests would be:

• GNSS performance test;
• Cell ID static test;
• Wi-Fi SSID static test
• Cell ID and Wi-Fi SSID static test
• Cell ID and GNSS static test (GNSS –142 dBm)
•  Wi-Fi SSID and GNSS static test (GNSS –142 dBm)
• Cell ID, Wi-Fi SSID, and GNSS static test (GNSS –142 dBm)
• Cell ID, Wi-Fi SSID, GNSS, and sensors moving test.

See how easily tests can proliferate!

A more stringent test could then be performed to stress-test the performance if required, and if required a playback test could be performed (see RF Replay test below).

The additional location sources can also aid in providing initial states and information for the position-determination system, in addition to the common assisted-GNSS information provided by the network. This will be particularly important in indoor and other environments where GNSS performance is compromised.

Further developments such as the LTE Positioning Protocol Extensions (LPPe) from the Open Mobile Alliance will also allow the sending of additional information to the device to improve the accuracy of the position. This additional information could include accurate time, altitude information, and other parameters. Future assistance standards should enhance the use of this information, and test standards should verify the correct use of this information.

RF Replay (or Playback) Tests. GNSS performance is statistical, and it is important to ensure that any tests have sufficient breadth and repetition to ensure statistical reliability. This applies to the more normal standard simulator tests, as well as to the uses of tests in the urban environment. For example, performance in the urban environment can vary significantly between two closely spaced runs, and can also be very dependent on the time of the day. A test done in the daytime may hit rush-hour traffic, whereas tests done at night will have relatively free flow, and hence faster average speeds. Additionally, the space-vehicle constellation geometry is constantly changing, which can enhance or degrade the GNSS performance. These factors need to be considered in generating any test routes.

For RF replay tests, a number of specific locations for urban driving and pedestrian routes should be specified. These locations should be based on network-operator test requirements, and include a mixture of suburban and deep urban environments (such as Tehran Street, Seoul). For each location, ten different data sets should be used, captured at different times, including peak rush hour at a specified hour. The data set should also include separate high-performance IMU data to provide truth data. To provide test consistency, a golden-standard data set should be used. But with different suppliers this would be difficult.

For pedestrian tests, a similar number of different routes should be defined, and data captured similarly. Ideally, all data useable for a hybrid solution should be captured, and available for replay. The test criteria analyzed for this could include: yield; horizontal position error, along-track error, across-track error, heading error, and speed error.

Interference Tests with Different Cellular Bands. It is important to have a standard test to demonstrate that the device under test does not have performance degradation due to interference from particular cellular subsystems interfering with the GNSS. For this test, the device should be tested in an OTA environment to ensure that all interference coupling mechanisms are present. Two tests should be performed: first, a tracking test. In this the A-GPS performance is tested by measuring the GNSS carrier-to-noise ratio for each GNSS band, while all the wireless channels on the platform are exercised sequentially. The test result would indicate the maximum number of dBs degradation that occurs.

Second, a cold-start test at –140 dBm should be performed separately while each wireless channel on the platform is exercised. Any extension in cold-start TTFF should be noted.

Conclusions

The challenges for cellular location test standards have increased significantly with the availability of new GNSS constellations, and the use of all available technologies within the cellular platform to provide the best appropriate location for the required use case. For test standards to be relevant, and also able to be run in an appropriate time, they must consider both the requirements to prove that the appropriate technology is operating correctly, and also bear a relationship to the final system performance required. This means, for example, that a multi-constellation GNSS receiver is really using all the constellations appropriately, and also that the end-user performance requirement is considered.

Existing cellular test standards are minimum performance requirements, but future standards should encapsulate the minimum performance requirements while also allowing standard extension to provide a consistent performance description.
Further to this, platform performance must be proved in all standing operating modes, which means, for example, that the cellular system be checked when operating in all supported bands.

Test equipment to support future cellular test standards is in development, but the significant challenges will be in providing equipment to fully support urban drive and pedestrian performance requirements.

In conclusion, the ability to appropriately test a hybrid location system, comprising multi-constellation GNSS and additional location technologies, presents almost as many challenges as generating the hybrid solution in the first place.

Acknowledgments

Many thanks to the GNSS team at ST-Ericsson, and at Spirent, and also to our customers for the challenges that they have presented as the required location performances have changed and increased.

Manufacturers

Figure 4 is taken from a Spirent Hybrid Location Test System (HLTS).

Peter Anderson received master’s degrees in electrical sciences from Cambridge University and in microelectronics from Durham University. Until recently, he was a GPS systems manager and the GNSS Fellow at ST-Ericsson; he is now a consultant with PZA Systems Ltd.

Esther Anyaegbu is a senior systems architect at ST-Ericsson. She earned her Ph.D. in data communications systems from the University of Leeds, where she focused on the processing of GNSS signals in the frequency domain.

Richard Catmur is head of standards development at Spirent Communications. He holds an M.A. in engineering science from Oxford University. He has served as rapporteur, editor, or major contributor to all 3GPP and OMA standards on the testing of positioning in wireless devices.

Within a day of their initial activation over central Europe on March 12, Galileo satellites were visible over North America. The PLAN Group of the University of Calgary was successful in capturing and processing the signals from these satellites as they emerged. Galileo PRN 11, 12, and 19 were found and tracked on E1B/C. The PLAN software GSNRx was also able to track simultaneously GPS L1 and GLONASS L1 and produce combined position solutions.

Examining the Galileo navigation message transmitted on the E1B signal, it was found that the satellite health status is flagged as E1B_HS=3 meaning Signal Component currently in Test, and the data validity status is flagged as E1B_DVS=1 meaning Working without Guarantee. Current Galileo-ready commercial receivers may automatically discard measurements from a satellites broadcasting such messages. Parsing the received words in the I/NAV message, it was noted that more 50 percent of them were of type 0, although all words (types 0 to 10) were decoded at some point during the test.

Data was collected using a roof-mounted NovAtel 702GG antenna and an in-house two-channel digitizing front-end clocked by a high quality OCXO and also a three-channel National Instruments front-end for post-processing. The two-channel intermediate frequency data was streamed live to a laptop computer for real-time processing with GSNRx. Two RF channels were processed, the first centered at 1574.0 MHz with an IF bandwidth of 10.0 MHz, for the GPS L1 C/A and Galileo E1B/C signals and the second centered at 1602.0 MHz again with a bandwidth of  10.0 MHz, for the GLONASS L1 OF signals. The GPS and GLONASS signals were tracked using a Kalman-filter-based tracking strategy while the Galileo signals were tracked using a specialized data-pilot algorithm.

Figure 1. Scatter plot of the north and east position

Pseudorange and Doppler observations were extracted from the tracking strategies at a rate of 2 Hz. A 2D horizontal plot of the combined GPS & GLONASS and the combined Galileo, GLONASS & GPS single-frequency single-point solutions is presented in Figure 1.

Figure 2: Skyplot of the Galileo satellites.

The pseudorange residuals are plotted against time for each PRN tracked from each of the three systems in Figure 3. It is apparent that the addition of the three Galileo observations contributes to a reduction in bias and standard deviation in the horizontal directions, showing an excellent functioning of the Galileo satellites and PLAN Group equipment and software.

Figure 3. Pseudorange residuals are plotted against time for each PRN tracked from each of the three systems.

Figure 4. A screenshot of the receiver processing the data.

Contact: Dr. James T. Curran

Email: James.T.Curran at ucalgary.ca

By Frederic Leveau, Solene Boucher, Erwan Goron, and Herve Lattard

A controlled radiated pattern antenna can be an effective way to protect GPS receivers against jamming. A new CRPA, composed of seven elements, works on the E5a, E5b, E6, L2, and L1 bandwidths. This article reports on radiation pattern measurements of the array in a test facility.

Controlled radiation pattern antenna (CRPA) technique is considered to be the best GPS pre-correlation protection technique against interference. It consists of an antenna array and a processing unit that performs a phase-destructive sum of the incoming interference signals, this process being equivalent to making nulls towards interferers in the array radiation pattern.

Considering the growing Galileo system and the possible interest of the French Ministry of Defense in the Public Regulated Service (PRS) , a prospective study was undertaken to develop an array compatible with GPS M-code, Galileo PRS, and aeronautical radionavigation signals in the E5 bandwidth. The French Expertise & Procurement Defence Agency (DGA) awarded the French company SATIMO a feasibility contract to design, conceive, realize, and evaluate a circular array composed of seven elementary patch antennas (see Figure 1).

Figure 1. CRPA unit receiving satellite and jammer signals.

Product Features

SATIMO, a company specializing in R&D for antennas and in innovative antenna test ranges, has since developed this GPS-Galileo CRPA antenna, shown below.

New CRPA developed by SATIMO.

The CRPA consists  of seven elementary patches covering E5a, E5b, L2, E6, L2, and L1 frequency bandwidths, using microstrip multilayer technology. Each element is housed in a 9-centimeter (diameter) by 2-centimeter (height) radome, connector excluded. In that volume, a space provision has been reserved to include a low-noise amplifier (LNA) and two filters for a sharp out-of-band rejection. As a consequence, it is possible to configure three types of arrays: passive without filters, passive with two passband filters, and finally active (including a LNA, with a gain > 26dB, NF<0.9dB) with two passband filters. The maximum gain levels in these configurations are from 3.6 dBi to 29.8 dBi. For radiation patterns, see Figure 2.

Figure 2A. CRPA radiation patterns.

Figure 2B. CRPA radiation patterns.

The design of the single element has been optimized to control the deviations of each patch antenna when included in a seven-element array.

To limit mutual coupling with respect to the array dimensions, the distance between the elements’ phase centers has been chosen close to 0.7 λ at L1 frequency. This value results in a 36.5-centimeter (diameter) array. The standalone antenna and the CRPA antenna have been validated through an environmental testing campaign.

Product Development

The usual iterative tuning and the optimization process for prototyping have been performed on SATIMO’s arch test range. This test facility indeed significantly reduces the time required to characterize the antenna-under-test (AUT) radiation pattern, in comparison with classical anechoic chamber test facilities.

More precisely, the arch test range instantaneously scans the field in one whole site angle cross-section plane, whereas the legacy systems mechanically scan the same cross-section plane by rotating the AUT for each incremental angle value. The spatial sampling of the near-field radiated by the AUT, thanks to a large number of probes along the arch surrounding it, enables a significant savings in time. The near-field results in the current plane can be displayed in real-time on a computer screen. Then, the rotation of AUT around its axis is automatically controlled by the measurement system, and a new acquisition is performed for each new cross-section plane. A Fourier transform computation is eventually applied to the 3D near-field to get the far-field radiation pattern.

The radiating characterization of the CRPA has been performed with a SATIMO SG24 system. With such a system, we have measured the complete 3D radiation patterns of each single element in less than 40 minutes per antenna.

Evaluation

The evaluation of the CRPA array was performed with this test bed in SATIMO’s facility (see photos below). The process  begain with measuring an element alone on a ground plane, in order to extract the gain, the axial ratio, the aperture angle, the matching values, and every feature that defines a fixed-radiation pattern antenna. The evaluation secondly consisted of characterizing the array, that is, extracting the gain and the phase of each element in the array, with respect to a reference element. To implement such a reference anytime during the near-field acquisition process, the arch test range (Figure 3) is very powerful, because all the probes constantly point at the center of the array, despite AUT’s motions. On the contrary, the need for such a reference makes measurements difficult in anechoic chambers, which often require canceling out misalignments, thanks to specific motions that must be taken into account in the computations.

CRPA in measurements.

CRPA in measurements.

Figure 3. Arch test range working principle.

Uses

Functional tests are another important part of the CRPA unit evaluation. Usually, two kind of tests can be conducted: outdoors or in anechoic chamber.

Classical Tests. DGA plans to perform outdoor test campaigns by utilizing an array placed on the roof of an all-terrain vehicle (see photo). The array will be connected to a CRPA GPS processing unit and to a receiver in the vehicle. Some interferers will be located along the trajectory of the vehicle, according to various scenarios defining their waveforms and their power levels. The CRPA capability to reject those interferers can then be assessed. These kinds of outdoor tests naturally suit CRPA’s processing unit and array characterization, as they involve radiated GPS and interfering signals. However, these kinds of tests are not reproducible and are quite complicated to set up.

Outdoor jamming test campaign.

Some tests in anechoic chambers could be an alternative in order to obtain reproducible test results, but in that case, transmitting GPS constellation signals indoor becomes a challenge. An option could be the use of a GPS signal simulator, but this means a unique direction of arrival of GPS signals. Moreover, no dynamic trajectory could be done.

New Test Bed. DGA recently acquired a test bed, developed by INEO Defense, that enables evaluating CRPA units in conducted mode, for example. There is no longer a need to radiate either GPS signals or interfering signals. The purpose of this test bed, called BAnc de Caractérisation des Antennes Réseaux Antibrouillage (BACARA), or test bed to characterize anti-jamming antenna arrays (Figure 4 and Figure 5), is to replace the array and simulate its GPS and jamming environment. This means that it is able to create elementary antenna phase delays and gains resulting from the array geometry, by using finite impulse response (FIR) filters (Figure 6). This is the reason why this test bed must be fed with the array phase and gain measurement results obtained with the arch test range.

Figure 4. BACARA test bed.

Figure 5. BACARA working principle.

Figure 6. BACARA working principle.

Alternatively, these results can be obtained with traditional anechoic chamber measurements. 10 channels of a multi-channel GPS simulator, each one matched with a satellite, are used by the test bed. Thus, BACARA coherently sums GPS constellation simulator output channels and interfering signals, so as to accurately simulate the array’s behavior in the laboratory. As a result, for any CRPA processing unit, it is possible to compare the array’s impact on a processing unit with an ideal array being composed of perfect elementary antennas.

Unfortunately, BACARA currently operates on L1 or L2, but not on the E6 and E5 bandwidths. On the other hand, this test bed is able to simulate dynamic trajectories, with the mobile positions and attitudes. Up to 10 internal jammers with various waveforms can be set up, and their power levels over time are computed by software like Warfare or Matlab. A numerical calibration allows some transparency of the test bed for CRPA units under test.

Figure 7. BACARA graphical user interface.

Figure 8. Examples of available simulated array geometry.

Conclusion

SATIMO, a company specializing in electromagnetic field measurements in the microwave frequency range and part of the Microwave Vision Group, has developed an array for the reception of M-code, PRS, and aeronautical radionavigation signals. This antenna array has been fully evaluated and qualified through electrical and environmental tests. The measurement methods have enabled the company to demonstrate the feasibility of the performances expected. Functional evaluations restricted to GPS are still under way. To do so, DGA will utilize its complementary outdoor and indoor test means, especially its laboratory test bed BACARA, as a tool to precisely evaluate GPS CRPA units.

Frederic Leveau works at the French MoD (DGA Information Superiority) as a radionavigation expert. His main interests are Galileo PRS prospective studies and developments and the integration of CRPA systems within French platforms.

Solene Boucher works at the French MoD (DGA Information Superiority) as a radionavigation expert. Her main interests are Galileo PRS prospective studies and developments. She is also responsible for the test bed BACARA.

Erwan Goron is an engineer at SATIMO Industries (Microwave Vision Group). His main activity is antenna conception.

Herve Lattard is an engineer at SATIMO Industries (Microwave Vision Group). His main activity is antenna conception.

A method of decoding an unknown pseudorandom noise code uses a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation satellites.

Decoding an unknown GNSS pseudorandom noise (PRN) code can be rather easily done using a high-gain steerable dish antenna as was used, for example, in determine the BeiDou-M1 broadcast codes before they were publicly announced. The signal-to-noise ratio within one chip of the code is sufficient to determine its sign. This article describes a method of getting this information using a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation (IOV) satellites. In spite of the fact that only pilot signal decoding seems to be possible at first glance, it is shown that in practice data signals can also be decoded.

Concept

The idea is to do coherent accumulation of each chip of an unknown signal during a rather long time interval. The interval may be as long as a full satellite pass; for medium Earth orbits, this could be up to six hours. One of the receiver’s channels is configured in the same way as for signal tracking. The I and Q signal components are accumulated during one chip length in the digital signal processor, and these values are added to an array cell, referenced by chip number, by the processor. Only a limited amount of information need be known about the signal: its RF frequency; the expected chip rate; the expected total code length; and the modulation method.

The decoding of binary-phase-shift-keying (BPSK) signals (as most often used) is the subject of this article. It appears that the decoding of more complicated signals is possible too, but this should be proved. A limitation of this method (in common with that of the dish method) is the maximum total code length that can be handled: for lengths greater than one second and bitrates higher than 10,000 kilobits per second, the receiver’s resources may not be sufficient to deal with the signal.

Reconstructing the Signal’s Phase

This method requires coherency. During the full accumulation period, the phase difference between the real signal phase and the phase of the signal generated by the receiver’s channel should be much less than one cycle of the carrier frequency. Depending on the GNSS’s available signals, different approaches may be used. The simplest case is reconstruction of a third signal while two other signals on different frequencies are of known structure and can be tracked.

The main (and possibly the only significant) disturbing factor is the ionosphere. The ionospheric delay (or, more correctly, the variation of ionospheric delay) is calculated using the two known tracked signals, then the phase of the third signal, as affected by the ionosphere, is predicted.

The final formula (the calculations are trivial and are widely available in the literature) is:

where:
φ1 , f1 are the phase and frequency of the first signal in cycles and Hz, respectively
φ2 , f2   are the phase and frequency of the second signal in cycles and Hz, respectively
φ3 , f3   are the phase and frequency of the third signal in cycles and Hz, respectively.

It was confirmed that for all pass periods (elevation angles less than 10 degrees were not tested), the difference between the calculated phase and real phase was always less than one-tenth of a cycle. GPS Block IIF satellites PRN 1 and PRN 25 were used to prove this: the L1 C/A-code and L5 signals were used as the first and second signals, with the L2C signal as the third unknown.

If two known signals are not available, and the ionospheric delay cannot be precisely calculated, it is theoretically possible to obtain an estimate of the delay from one or more neighboring satellites with two signals available. Calculations and estimations should be carried out to investigate the expected precision.

The Experiment

The Galileo E6-B/C signal as currently transmitted by the IOV satellites was selected for the experiment, as its structure has not been published. The E6 signal has three components: E6-A, E6-B and E6-C. The E6-A component is part of the Galileo Public Regulated Service, while the two other components will serve the Galileo Commercial Service. The E6-B component carries a data signal, while the E6-C component is a pilot signal.

From open sources, it is known that the carrier frequency of the E6 signal is 1278.75 MHz and that the E6-B and E6-C components use BPSK modulation at 5,115 chips per millisecond with a primary code length of one millisecond. E6-B’s data rate is 1,000 bits per second and the total length of the pilot code is 100 milliseconds (a secondary code of 100 bits over 100 milliseconds is also present in the E6-C signal, which aids in signal acquisition).

A slightly modified commercial high-precision multi-GNSS receiver, with the E6 band and without the GLONASS L2 band, was used for this experiment. The receiver was connected to a conventional GNSS antenna, placed on a roof and was configured as described above. The E1 signal was used as the first signal and E5a as the second signal. The E6 code tracking (using 5,115 chip values of zero) was 100 percent guided from the E1 code tracking (the changing of the code delay in the ionosphere was ignored). The E6 phase was guided from E1 and E5a using the above equation. Two arrays for 511,500 I and Q samples were organized in firmware. The integration period was set to one chip (200 nanoseconds).

Galileo IOV satellite PRN 11 (also variously known as E11, ProtoFlight Model and GSAT0101) was used initially, and the experiment started when the satellite’s elevation angle was about 60 degrees and lasted for only about 30 minutes. Then the I and Q vectors were downloaded to a PC and analyzed.

Decoding of Pilot Signal (E6-C)

Decoding of the pilot signal is made under the assumption that any possible influence of the data signal is small because the number of ones and zeros of E6-B in each of 511,500 chips of the 100-millisecond integration interval is about the same. First, the secondary code was obtained. Figure 1 shows the correlation of the first 5,115 chips with 5,115 chips shifted by 0 to 511,500 chips. Because the initial phase of the E6 signal is unknown, two hypotheses for computing the amplitude or signal level were checked: [A] = [I] + [Q] and [A] = [I] – [Q], and the combination with the higher correlation value was selected for all further analysis.

Figure 1. Un-normalized autocorrelation of E6-C signal chips.

In Figure 1, the secondary code is highly visible: we see a sequence of 100 positive and negative correlation peaks (11100000001111 …; interpreting the negative peaks as zeros).This code is the exact complement (all bits reversed) of the published E5a pilot secondary code for this satellite. More will be said about the derived codes and their complements later. It appears that, for all of the IOV satellites, the E6-C secondary codes are the same as the E5a secondary codes.

After obtaining the secondary code, it is possible to coherently add all 100 milliseconds of the integration interval with the secondary code sign to increase the energy in each chip by 100 times. Proceeding, we now have 5,115 chips of the pilot signal ­— the E6-C primary code.

To understand the correctness of the procedure and to check its results, we need to confirm that there is enough signal energy in each chip. To this end, a histogram of the pilot signal chip amplitudes can be plotted (see Figure 2). We see that there is nothing in the middle of the plot. This means that all 5,115 chips are correct, and there is no chance that even one bit is wrong.

Figure 2. Histogram of pilot signal chip amplitude in arbitrary units.

But there is one effect that seems strange at first glance: instead of two peaks we have four (two near each other). We will shortly see that this phenomenon results from the influence of the E6-B data signal and it may be decoded also.

Decoding the Data Signal

The presence of four peaks in the histogram of Figure 2 was not understood initially, so a plot of all 511,500 signal code chips was made (see Figure 3).
Interestingly, each millisecond of the signal has its own distribution, and milliseconds can be found where the distribution is close to that when two signals with the same chip rate are present. In this case, there should be three peaks in the energy (signal strength) spectrum: –2E, 0, and +2E, where E is the energy of one signal (assuming the B and C signals have the same strength).

Figure 3. Plot of 511,500 signal code chip amplitudes in arbitrary units.

One such time interval (starting at millisecond 92 and ending at millisecond 97) is shown in Figure 4. The middle of the plot (milliseconds 93 to 96) shows the described behavior. Figure 5 is a histogram of signal code chip amplitude for the signal from milliseconds 93 to 96.

Figure 4. Plot of signal code chip amplitude in arbitrary units from milliseconds 93 to 96.

Then we collect all such samples (milliseconds) with the same data sign together to increase the signal level. Finally, 5,115 values are obtained. Their distribution is shown in Figure 6.

The central peak is divided into two peaks (because of the presence of the pilot signal), but a gap between the central and side peaks (unlike the case of Figure 5) is achieved. This allows us to get the correct sign of all data signal chips. Subtracting the already known pilot signal chips, we get the 5,115 chips of the data signal — the E6-B primary code. This method works when there are at least some samples (milliseconds) where the number of chips with the same data bit in the data signal is significantly more than half.

Figure 5. Histogram of signal code chip amplitude.

Figure 6. Histogram of the signed sum of milliseconds chip amplitude with a noticeable presence of the data signal.

Proving the Codes

The experimentally determined E6-B and E6-C primary codes and the E6-C secondary codes for all four IOVsatellites (PRNs 11, 12, 19, and 20) were put in the receiver firmware. The receiver was then able to autonomously track the E6-B and E6-C signals of the satellites.

Initial decoding of E6-B navigation data has been performed. It appears that the data has the same preamble (the 16-bit synchronization word) as that given for the E6-B signal in the GIOVE Interface Control Document (ICD). Convolutional encoding for forward error correction is applied as described in the Galileo Open Service ICD, and 24-bit cyclic redundancy check error detection (CRC-24) is used. At the time of the analysis, all four IOV satellites transmitted the same constant navigation data message.

Plots of PRN 11 E6 signal tracking are shown in Figure 7 and in Figure 8. The determined codes may be found at www.gpsworld.com/galileo-E6-codes. Some of these codes may be the exact complement of the official codes since the code-determination technique has a one-half cycle carrier-phase ambiguity resulting in an initial chip value ambiguity. But from the point of view of receiver tracking, this is immaterial.

Figure 7. Signal-to-noise-density ratio of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.

Figure 8. Pseudorange minus carrier phase (in units of meters) of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.

Acknowledgments

Special thanks to JAVAD GNSS’s DSP system developers. The system is flexible so it allows us to do tricks like setting the integration period to one chip, and powerful enough to be able to do required jobs within a 200-nanosecond cycle. This article was prepared for publication by Richard Langley.

Manufacturers

A JAVAD GNSS TRE-G3T-E OEM receiver, a modification of the TRE-G3T receiver, was used in the experiment, connected to a conventional JAVAD GNSS antenna. Plots of E6 code tracking of all four IOV satellites may be found on the company’s website.

Sergei Yudanov is a senior firmware developer at JAVAD GNSS, Moscow.

In August 2010, NGS issued a Kinematic GPS Challenge to seek community input on the best practices for processing this large positioning data volume. Ten international groups answered the call, submitting 16 different position solutions calculated with a variety of software and techniques. However, the majority of solutions were corrupted by a characteristic “sawtooth” pattern which was tracked back to the aircraft receiver used in the initial challenge; for this challenge reissue, a second onboard GNSS receiver is used.  Also in this new call for participation, inertial measurement unit (IMU) data are made available for joint GPS+IMU processing.

“To further facilitate our software and method development, we invite interested researchers and practitioners to compute and submit solutions from samples of actual GRAV-D data,” said Gerry Mader and Theresa Diehl, NGS, in an invitation email. “In this new call, NGS requests that all participants submit a GPS-only solution utilizing the new aircraft GPS data. For those able to process with IMU data, we request additional submission of a second IMU+GPS solution. NGS would like to receive all solutions by April 1, 2013.

“This is a strictly voluntary exercise for those interested in such a comparison and we will share our results with the participants. We are also interested in possibly co-authoring a publication with the participants on the topic if results are significant.”

Detailed information on the challenge is available here:

• Test description for second call (PDF)
• Challenge website

Those interested in participating should read through the PDF (link above), then email Gerry Mader (gerald.l.mader at noaa.gov) and Theresa Diehl (theresa.diehl at noaa.gov) with any questions.

According to the Belgian GNSS receiver manufacturer, its engineers “are currently processing further data sets to finalize the implementation of full BeiDou support. Although the BeiDou constellation is still being deployed, the data analysis already shows promising results.”

Figure 1. Top: Computed height above reference point (m) for GPS-only, GPS+GLONASS and GPS+GLONASS+BeiDou vs. time of day (hour).

The bottom panel of Figure 1 shows that, even with the current BeiDou constellation (15 satellites total, of which five are geostationary over China, five in full mid-Earth orbit similar to GPS and GLONASS, and five in inclined geosynchronous orbit over Asia), the total number of satellites used over the European region reached 26 for a short moment.

Figure 2. L1 pseudorange residuals (m) for GPS (L1 C/A, top) and COMPASS (B1-I, bottom) vs. time of day (hour).

Figure 2 shows the L1 pseudorange residuals for all constellations individually. This comparison highlights the advantage of the GPS constellation, which builds on two decades of real-time orbit prediction. The BeiDou orbits are “quite accurate for a relatively young constellation, but show typical meter-level jumps when ephemerides are updated,” according to Septentrio.

Septentrio says that the new feature will soon become available on selected company platforms. Users of its multi-constellation receivers will then benefit from improvements in urban availability and signal integrity, thanks to the augmented signal coverage.

“The following graphs shows SNR and ‘code-minus-phase’ combination of svn #11 (sat #81 on graph), svn #12 (sat #82) , svn #19 (sat #89) and svn #20 (sat #90). C/A stands for E1, P2 for E5B, CL2 for E6, L5 for E5A.”

The announcement includes a link to a short article describing how these codes were found. The Galileo E6 codes have not been published by the European Space Agency.