Russian scientists propose a new code-division multiple-access signal format to be broadcast on a new GLONASS L3 signal. Once implemented across the modernizing GLONASS constellation, this will facilitate interoperability with — and eventually interchangeability among — other GNSS signals. The flexible message format permits relatively easy upgrades in the navigation message, if required.
By Alexander Povalyaev
Navigation messages (NM) developed and broadcast so far, by both GPS and GLONASS, are fixed, regular structures including pages (frames), subframes (rows), and words. Despite their simplicity, such structures are very conservative. The only possibility to update such navigation messages is restricted to the use of previously allocated backup frames. Increasing numbers of such frames make for ineffective use of navigation message transmission capacity. Conversely, the relatively small number of backup frames restricts the potential for future navigation-message upgrades.
This concept is illustrated by the next two figures. Figure 1 shows the structure of GPS NM superframe.nBackup subframes are showed in bold dots. We can see that from 125 subframes of a GPS NM with a duration of 12.5 minutes, 14 subframes (or roughly 11 percent) are backup ones.
Figure 1. Backup of GPS NM superframe.
Figure 2 shows the structure of GLONASS NM. Backup frames with indication of bit numbers are shown by unhatched fields. In the GLONASS superframe with a duration of 2.5 minutes, these bits occupy only about 3 percent.
If we assume a data equivalence transmitted in the GLONASS and GPS navigation solutions, we can see that data transmission rate in GLONASS is five times as much as in GPS. This is explained by the higher redundancy of the GPS NM. Besides the roughly 11 percent of subframes kept in backup, the GPS superframe reserves field for transmission of 32 satellite almanacs, although the number of satellites in GPS constellation is always less than 32. As a result, the NM transmission channel in GPS used ineffeciently.
For GLONASS, the situation is different. The NM includes only about 3 percent of backup bits, and the superframe reserves field for transmission of only 24 satellite almanacs. This significantly increases the NM transmission channel efficiency relative to GPS, but causes big problems during any process of system update.
In these cases, upgrades or updates should only occur when they furnish backward compatibility, which means that previously manufactured user equipment can still maintain its compatibility with the updated system. When generating a NM in the form of fixed, strictly regular structures including pages (frames), subframes (rows), and words meeting the backward compatibility principle, this means that update sonly can be done using backup frames, because modification of basic, non-redundant frames will produce problems with earlier user equipment health. From this point of view, a large number of backup frames in very preferable.
Difficulties. As an example, let us consider the problems that arise in the process of a GLONASS upgrade, the purpose of which is to increase the number of GLONASS satellites in the constellation up to 30. Such an upgrade can be done in order to exclude areas of dilution of precision (DOP) degradation that arise due to GLONASS’s symmetrical constellation geometry. To provide that the rule of backward compatibility is met, it is necessary that almanacs of six extra satellites be placed in backup bits of the superframe. But the number of such bits in the GLONASS superframe (as shown in Figure 2) allows placement of only one satellite almanac. Thus in the case of such an upgrade, the almanac of the first basic 24 salellites will be transmitted within the time of 1 superframe, that is, 2.5 minutes, and the almanac of the xis extra satellites will be transmitted consequently in backup rows within the time of six superframes, that is, 2.5 × 6 = 15 minutes.
Figure 2. Backup of GLONASS navigation message superframe.
A New Way. Avoiding such difficulties associated with NMs with fixed, strictly regular structures including pages (frames), subframes (rows), and words is possible through the use of a NM with flexible row structure. Such a structure was formed for the first time for the GPS L5 signal. In this structure, the NM is formed as a variable-row flow of different types. Each row type has a unique structure and contains specified information type, for example: ephemeris, almanacs of specified satellites, parameters of Earth pole movement models, parameters of ionosphere delay models, and so on.
User equipment allots a successive row from the flow, defines its type, and in accordance with the type allots data contained in this row. When using such NM structure, strict regularity of different data types received by user equipment is disturbed, but GNSS control system guarantees that data transmission delays for each data type in NM will not exceed maximum values previously defined in the interface control document (ICD). For example, rows with ephemeris data in the GPS L5 signal are transmitted a minimum of once every 24 seconds, the so-called restricted almanac of the system is transmitted minimum once every 10 minutes, and so on. (See the “Navstar GPS Space Segment/User Segment L5 Interfaces, IS-GPS-705,” www.navcen.uscg.gov/pdf/Number.pdf.)
Deploying a Growing GNSS. A flexible row structure of the NM provides more effective use of NM transmission channel capacity, especially during the stage of system deployment which, as experience has shown, may last several years. During this stage, the GNSS orbital constellation is not complete and thus the NM may be generated as a row flow containing almanacs of only those satellites that are actually included in the orbital constellation. Reducing the number of rows with satellite almanacs allows reducing the time interval per which ephemeris are transmitted. Obviously a NM with fixed regular structures does not permit this capability.
The main advantage of a NM with flexible row structure is the possibility of its evolutional upgrade meeting the rule of backward compatibility. For this purpose, the ICD of respective signals for developers of user equipment states that if the user equipment encounters unknown row types, it should ignore them. This allows adding new row types in the process off GNSS upgrade. Including rows of new types in the NM certainly lowers the transmission rate, relative to rows of old types.
Previously manufactured user equipment ignores rows with new types and therefore does not use innovations introduced in the process of GNSS upgrade, but at the same time its health is not affected. More recent user equipment gets the opportunity to use data both from old and new row types and therefore to use introduced innovations.
In this case, user equipment upgrade replaces old software versions with new ones. This replacement is not due to any invalidity of old software version, but the equipment owner’s desire to benefit from the innovations introduced by GNSS.
Very old row types may on the other hand be removed from NM. At that point, very old and not-upgraded user equipment would become non-operational. This situation is quite normal because it may be considered as excluding excessively obsolete user equipment from operation.
When using flexible row structure, a GLONASS NM upgrade as in the previous example on exceeding the number of satellites up to 30 would mean simply exceeding the number of rows with the type defining the structure of almanac data. In this case, transmission rate of ephemeris and almanac would certainly degrade a little, but it would require no conversion of user-equipment software.
Status. Currently GLONASS uses signals with frequency separation in L1 (1592.9 – 1610 MHz) and L2 (1237.8 – 1256.8 MHz). The system upgrade now underway will in the long-range outlook turn to signals with code-division multiple-access (CDMA) in L1, L2, and L3 (1190.35 – 1212.23 MHz). One satellite has been launched transmitting signals with code separation in L3.
The NM of all new GLONASS signals with code separation, or CDMA, will have flexible row structure. Documents are now being developed concerning NM row structure of this type. For example, Figure 3 shows the structure of 20throw type for open signal L3OC with code separation in L3 containing almanac. L3OC signal rows contain 300 bits and have time interval of 3 seconds.
Figure 3. The structure of 20th row type for GLONASS open signal L3OC with code separation.
Parameters shown in Figure 3 have the following meaning:
TM time mark signal Type row type (in this case = 20) String count time mark numeralization; j number of satellite transmitting present NM Гj health operative feature («0») or unhealth operative feature («1») of satellite j navigation radiosignal lj reliability feature («0») or unreliability feature («1») of NM data in the current row with number j; П1 service bits for calling ground control system (НКУ) П2 satellite orientation mode feature: П2 = 0, satellite is in orientation mode to the Sun; П2 = 1, satellite is in the mode of anticipatory turn or in the mode change status (Sun orientation and anticipatory turn) КР feature of planned correction of onboard time scale (OTS) by ± 1 sec at the end of Greenwich current quarter А anomaly feature of the following row which, when onboard time scale has been corrected by ± 1 sec, will have 2 or 4 sec CRC control bits of cyclic redundancy code.
The above parameters of 20th row type are service parameters. Their content remains unchanged for all NS rows of L3OC. The following parameters of 20th row type are information parameters. Ns the number of satellites in the current constellation EA satellite almanac age NA calendar day number within 4-year interval to which almanac belongs РСA status register of navigation radiosignals L1, L2, L3 MA satellite upgrade with the number j τA correction for transition from OTS of the satellite with number j to GLONASS time scale (GTS) λA geodetic longitude of the first ascending node of the satellite orbit with number j within the day with number NA tλA the time (according to the Moscow decree time) when the satellite with the number j transits the first ascending node within the day with number NА ΔiA correction to the orbit inclination average value (63º) for the satellite with the number j εA satellite orbit eccentricity with the number j ωA satellite orbit perigee argument for the satellite with the number j ΔTA correction to average value (43,200 seconds) rate of change of Zodiacal orbital period for the satellite with the number j ΔTA Zodiacal orbital period for the satellite with the number j.
Acknowledgment
The author would like to thank Sergey Karutin and Dmitry Lerner for help in translation of this paper.
Alexander Povalyaev is deputy head of division in JSC Russian Space Systems and a professor at the Moscow Aviation Institute. He has been developing methods and algorithms for GNSS carrier-phase measurements processing for more than 30 years. Currently he focuses on developing new code-division GLONASS signals.
The Business section of the November 2013 issue of GPS World (Download the PDF).
Focus on Timing
Includes: Orolia to Supply Atomic Clocks for Galileo Satellites; Symmetricom Expands SyncWorld Program to Power Utilities; Product Showcase. Plus: NextNav and Broadcom Partner for Indoor Accuracy; Events.
The FTS500 Xenith TBR (Time Base Reference) by Connor-Winfield is designed for DVB/DAB, wireless communications, time-stamping, or any other timing vital application.
The Xenith TBR module is a GPS-driven, mixed-signal phase lock loop, providing a 1PPS CMOS output and generating a 10-MHz SINE output from an intrinsically low jitter voltage controlled crystal oscillator (VCXO). The 10-MHz output is disciplined from an on-board GPS receiver, which drives the long-term frequency stability. Its on-board CW25 timing GPS receiver along with a dual-oven system provides the highest quality timing and synchronization signals combined with superb hold-over characteristics. The unit is housed in a 106 x 125 x 56 millimeter strong aluminum enclosure.
The CW46S GPS sensor by NavSync is a fully integrated module that includes a CW25 GPS receiver, DC/DC converter, RS232 or RS422 interface options, and active GPS antenna housed in a small weatherproof (IP67-rated) enclosure.
When mounted with a good sky view, the CW46S receiver can provide high-quality timing and synchronization. The 1 pulse per second (PPS) timing signal can provide accuracies to within 30nS RMS of Coordinated Universal Time (UTC).
The 1PPS is transmitted via RS422 signal format; this two-wire method allows the pulse to be transmitted with cable lengths exceeding 100 meters.
The CW46S utilizes the CW25TIM GPS receiver, which allows the CW46S to act as a complete timing module capable of outputting a GPS-disciplined 10-MHz frequency.
Delta IV, the current GPS launch vehicle, awaits a date with space at Cape Canaveral.
Launch Delays Ground GPS IIF and Galileo FOC
The scheduled October 23 launch of GPS IIF-5, the fifth in the current “follow-on” generation of GPS satellites, has been postponed in order to complete a review of an adjustment made to the rocket’s upper stage engine. A loss of thrust by a Delta IV rocket upper stage during a GPS launch last year worried the Air Force and the United Launch Alliance (ULA), though the satellite successfully reached its intended orbit.
A subsequent investigation identified a fuel leak in the engine system as the culprit. Two medium Delta IV rockets and one heavy version have launched since then, but ULA said further investigation had produced new information about the engine’s first start.
While no new launch date has been set, the ULA released a statement:
“The ongoing Phase II investigation has included extremely detailed characterization and reconstructions of the instrumentation signatures obtained from the October 2012 launch and these have recently resulted in some updated conclusions related to dynamic responses that occurred on the engine system during the first engine start event.
“The GPS IIF-5 Delta IV launch is being delayed to allow the technical team time to further assess these updated conclusions and improvements already implemented and determine whether additional changes are required prior to the next Delta IV launch.
“The Delta IV booster for the GPS IIF-5 mission has completed the standard processing and checkout on the launch pad and will be maintained in a ready state for spacecraft mate and launch pending completion of this assessment. A new launch date will be established when the assessment of the updated dynamic response information is completed in the coming weeks.”
A Soyuz rocket (right) will carry Galileo FOC satellites, but no sooner than June 2014.
Galileo. Continuing delays in ground testing of the first two fully operational Galileo satellites have postponed their launch to June 2014 at the earliest.
According to European officials, the European Space Research and Technology Centre (ESTEC) thermal vacuum chamber for testing satellites under orbit conditions was not ready for the two FOC satellites delivered by OHB in late summer.
The satellites thus cannot ship to the Guiana spaceport in South America in time for a planned 2013 launch on a Soyuz rocket. The Galileo schedule is also running into bottlenecks with scheduled launches by other satellite programs aboard Guiana Soyuzes.
A six-week test of the first Galileo satellite at ESTEC reportedly got under way in October.
Svalbard station on Spitsbergen in the Norwegian Arctic.
Ground Network Supports Galileo for SAR
Completion of a pair of European Space Agency dedicated ground stations at opposite ends of that continent has enabled Galileo satellites in orbit to participate in global testing of the Cospas–Sarsat search and rescue system.
The Maspalomas station, in mid-Atlantic Canary Islands, was activated in June. In September, the Svalbard site on Spitsbergen in the Norwegian Arctic activated. The two sites can now communicate and will soon undertake joint tests.
The International Cospas-Sarsat Programme is a satellite-based search and rescue (SAR) distress alert detection and information distribution system, established by Canada, France, Russia, and the United States, with participation by 33 other countries.
Activation of the two new stations enables participation of the latest two Galileo satellites in a worldwide test campaign for Cospas-Sarsat expansion.
The program is introducing a new medium-orbit SAR system to improve coverage and response times, with the Galileo satellites in the vanguard.
The second pair of Europe’s Galileo satellites — launched together in October 2012 — are the first of the constellation to host SAR payloads. These can pick up UHF signals from emergency beacons aboard ships or aircraft or carried by individuals, which are then relayed to ground stations. There, the source is pinpointed and automatically passed on to a control center, which then routes it to local authorities for rescue.
“The Galileo satellites, tested in combination with the same SAR payloads on Russian GLONASS satellites as well as compatible repeaters on a pair of U.S. GPS satellites, showed an ability to pinpoint simulated emergency beacons down to an accuracy of 2–5 kilometers in a matter of minutes,” explained Igor Stojkovic, ESA Galileo SAR engineer.
“Our in-orbit validation tests so far have been in line with expectation and beyond, giving us a lot of confidence in the performance of the final system, once completed. And using a combination of satellites is just how the upgraded system will operate in practice, in order to localize distress signals.”
Localization test performed from Maspalomas MEOLUT as part of Galileo’s SAR in-orbit validation. Beacon locations obtained with four satellites are shown in black, while those using three satellites are shown in grey. More than 93 percent of all beacon locations, after only a single beacon burst has been received, are within the required five kilometers from actual beacon position.
System Briefs
GLONASS Seeks UK Ground. According to the website of the Russian magazine GLONASS Messenger, the Russian Federal Space Agency communicated its proposals for specific areas in the United Kingdom (or, more likely, its territories) to accommodate stations of the GLONASS System for Differential Correction and Monitoring (SDCM). Apparently, an offer was made by the deputy head of Roscosmos, Oleg Frolov, in discussions with David Parker, the director of the British Space Agency. The desired locations for the stations will not be disclosed until the approval of their establishment by the British side, the website reported.
Head Rolls. After repeated satellite launch failures and rumblings about embezzlement and corruption within the Russian space program Roscosmos, Vladimir Popovkin was let go as director and replaced by Oleg Ostapenko, a colonel general in the Russian Military, deputy minister of Defence, and former commander of the Aerospace Defence Forces. The Russian government also announced formation of new agency, the United Rocket and Space Corporation, to manage satellite and rocket manufacturing facilities heretofore supervised by Roscosmos.
Analysis of Signal Tracking Techniques for Multipath Mitigation
By Antonio Fernández, Mariano Wis, Pau Closas, Carles Fernández-Prades, José A. García, Francesca Zanier, and Massimo Crisci
INNOVATION INSIGHTS by Richard Langley
GETTING RID OF A NUISANCE. No, I’m not talking about your neighbor’s barking dog or the IT guy when he shows up to fiddle, yet again, with your computer. I’m talking about multipath. What is multipath, you ask? Herewith, Multipath 101. When a radio signal travels from a transmitting antenna to a receiving antenna, it will follow a direct line-of-sight path. But the signal might also travel to the receiving antenna after being reflected off a nearby building, say, resulting in a delayed signal or echo along with the line-of-sight one. Those of us of a certain age will remember ghost images on the screens of TVs connected to “rabbit ears” or outdoor antennas. That was multipath. These days, with TV signals primarily delivered by cable and satellite, we don’t see multipath much anymore. But we do hear it in our cars, from time to time, while listening to FM radio. Although the FM “capture effect” provides some margin against multipath, it is not uncommon to lose stereo reception or to experience fading out of the signal while driving in built-up areas as a result of reflections.
This same multipath phenomenon also affects GNSS signals. Unlike satellite TV antennas, the antennas feeding our GNSS receivers are omnidirectional. So we have the possibility of not only receiving a direct, line-of-sight signal from a GNSS satellite but also any indirect signal from the satellite that gets reflected off nearby buildings or other objects or even the ground. The related phenomena of diffraction and scattering can also generate multipath signals.
In a GNSS receiver, the line-of-sight and multipath signals combine to corrupt tracking of the line-of-sight signal resulting in increased pseudorange and carrier-phase measurement errors.
GNSS antenna and receiver manufacturers have developed techniques to minimize some of the impact of multipath on the GNSS observables. And tracking of some of the newer GNSS signals is a bit more resistant to multipath. But multipath, at some level, is still a problem looking for a better solution.
This brings us to ARTEMISA, which stands for Advanced Receiver Techniques: Multiprocessing Algorithms. It’s a European initiative to develop techniques to minimize the effects of multipath in GNSS receivers. For those of you who are a little rusty on your Greek mythology, Artemis (or Artemisa in Spanish — after all, she was a woman) was the Greek goddess of the hunt. You might better know her Roman equivalent: Diana. Her parents were Zeus and Leto, and Apollo was her twin brother. She is often depicted carrying a bow and arrows. How appropriate a name for a project whose goal is to try to kill off the effects of multipath in GNSS receivers.
In this month’s column, the team of researchers involved with ARTEMISA describe their efforts to generate synthetic multipath for GPS L1 and Galileo E1 signals and to test different signal tracking techniques in a simulated receiver to see which techniques best minimize the effects of multipath on positioning solutions and which might be feasible candidates for incorporating in real receivers. The hunt is on.
GNSS navigation in urban environments is usually challenged by a number of effects such as multipath and weak signal conditions. In particular, the pernicious effects of multipath on signal tracking and system accuracy are widely known. To mitigate these effects, there is a series of techniques that range from modified antenna design to combining the GNSS receiver with other sensors or subsystems. Another possibility is to implement advanced tracking techniques specifically designed for these purposes. Such techniques usually impose computational load and implementation complexity, which make them hard to implement in an application-specific-integrated-circuit-based receiver. However, given the current advances in computer technology and the possibilities of field-programmable-gate-array- (FPGA-)based hardware, it is possible to implement these new techniques in an operational receiver.
We have studied this possibility as part of the ARTEMISA project, carried out by DEIMOS Space and the Centre Tecnològic de Telecomunicacions de Catalunya, and supervised by the European Space Agency’s European Space Research and Technology Centre. We have implemented and tested a series of innovative techniques that are able to cope with, and even to estimate, multipath (MP) parameters, using a simulated software receiver based on the GRANADA (Galileo Receiver Analysis and Design Application) GNSS blockset for MathWork’s Simulink graphical programming language tool. These techniques are based on the maximum likelihood principle (as implemented in the Multipath Estimating Delay Lock Loop) or on online Bayesian techniques for the estimation of multipath (as implemented in the Multipath Estimating Particle Filter), involving architectural modifications of the tracking loops (as in vector tracking loops), or even constituting a new paradigm in receiver design (direct position estimation).
Our effort in this project has focused on two main tasks. The first task is the design and development of the simulation platform, the techniques to be tested, and a multipath model representative of the urban environment. The second task involves a simulation campaign that has been carried out to test the different techniques and to contrast the results obtained against the legacy delay lock loop / phase lock loop (DLL/PLL) tracking loop schemes. This article describes these tasks and some of the results we have obtained so far.
Keep in mind that at the time of writing, ARTEMISA is still ongoing. Therefore, more results are expected up until the end of the project.
Simulation Platform
The simulation platform has been developed in Matlab/Simulink with DEIMOS Engenharia’s GRANADA GNSS Blockset. This blockset is a collection of Simulink models and blocks that can be used to design and simulate any kind of GNSS receiver. The main block is the Factor Correlator Model (FCM), which implements the set of correlators through an analytical (set of equations) model. Carrier phase, code misalignment, autocorrelation function, and even the correlated noise and the multipath at the output of every virtual correlator are simulated for a given input trajectory. On the other hand, the tracking loops are implemented as independent modules representative of an actual receiver. This semi-analytical approach has the advantage of performing a fast simulation of the correlator output without the need for implementing the baseband correlation operation. In addition, its implementation in Simulink allows for the development of innovative tracking schemes. This approach also matches with the ARTEMISA project concept, where a series of innovative tracking loops has been implemented with the aim of replacing or improving conventional PLL/DLL schemes.
The main architecture of the simulation platform is shown in FIGURE 1. We use an external trajectory file to generate the reference data that will be used with the FCM block to generate the correlator output that will feed the tracking loop blocks. These trajectory files are also used to feed the multipath scenario generators, to test each technique under a number of defined scenarios so that we can assess their performances and find their limitations under multipath.
We used two statistical models for the description of the signal propagation: the Controlled Stochastic Channel Model (CSCM), which is a modification of the Land Mobile Satellite channel developed by Pérez-Fontán, and the well-known Land Mobile Channel Model, developed by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt or DLR); see Further Reading. These models generate a series of scenario files, which can be loaded into the FCM to introduce the multipath effects in the correlator output.
These two statistical models are complementary. The CSCM model allows the user to set the multipath channel characteristics to be able to stress the tracking technique, while the DLR model permits evaluation of the technique’s response in realistic conditions.
A deterministic user-defined multipath generator was implemented to check the response of the tracking techniques under well-defined multipath conditions. The Multipath Error Envelope (MPEE) was also computed to evaluate the response of the technique under one echo with varying delay conditions.
The purpose of this article is not to explain all the developments performed with the platform. It focuses on the results obtained with the deterministic and statistical channel model. For this reason, the development of the CSCM generator will be explained in detail.
Controlled Stochastic Channel Model
The CSCM module was specifically created for this project. Its purpose is to generate a stochastic channel, but with the capability to control the multipath power levels and the number of echoes generated in the scenario, thus creating a set of realistic multipath signals, but with the capability of being easily controlled by the user. Time series for multipath echoes are generated, following a Rice or Rayleigh stochastic model, but the mean power levels, the amplification K factors, and the power switching times are chosen by the user.
The model allows the selection of the number of satellites (channels) that are generated in the model, the sampling period (related to the loop integration time), the length of the simulation, the receiver speed, the signal carrier frequency, and channel specific parameters such as the number of echoes, the average delay of the echoes and the decay slope (echo power loss ratio with signal delay).
One of the parameters the user can set implicitly is the multipath echo lifetime. What the user really sets is the channel transition time, or the time during which the channel keeps the multipath configuration. Every time the transition time is changed, a series of multipath echoes are canceled and other ones appear. This set of disappearing/appearing echoes is performed in pairs in such a way that the transition between echoes is smooth. FIGURE 2 illustrates this mechanism.
Figure 2. CSCM echoes smooth transition.
When an echo is disappearing (a red one in the figure), its associated echo is at its maximum value (a blue scatterer). In the next interval, a new echo appears in a different delay position (a green one) and the associated scatterer begins to decrease its power. This mechanism allows the user to easily associate the transition time to half the multipath echo lifetime.
Simulation Plan
The simulation plan is structured into different stages. The first stage of the simulation plan is based on the controlled multipath environment, with specific tests for each technique. The purpose of this stage is the tuning up of the techniques. As an example, for the Multipath Estimating Delay Lock Loop (MEDLL) technique, parameters such as the precorrelation bandwidth, the number of MEDLL iterations or the number of assumed multipath echoes are parameters that are adjusted after these tests are carried out. Another purpose of this first stage is to check the performance of the techniques under deterministic, fully controlled multipath. Parameters like signal-to-multipath ratio, multipath delay, and the number of multipath echoes can be controlled at any time.
Another test is the generation of the multipath envelope error plot. The utility of this test is to find the optimal configuration of the correlator position for each kind of signal, which is a trade-off between the obtained root-mean-square error (RMSE) and the number of correlators. This procedure is repeated for each signal considered in the plan.
The next stage of the simulation plan is the test with the CSCM model. This test consists of the generation of a series of scenarios characterized by the stochastic model, the number of echoes, and the user receiver dynamics. Scenarios presented in this article are shown in TABLE 1. Two types of user environments are defined: one for pedestrian and one for vehicular receivers. The dynamics (user speed) define the integration (sampling time) and the multipath Doppler spread. The stochastic model parameters include the type of stochastic model for the line-of-sight signal (LOSS) and multipath echoes, mean power level, and amplification K factor. These scenarios represent a moderate multipath level with four echoes.
Table 1. CSCM scenarios in the simulation plan.
Each scenario was run with different settings of carrier-to-noise-density ratio (C/N0) and with all the signals that were planned for the project: GPS L1 C/A, GPS L5, Galileo E1 and Galileo E5. In this article, we focus on L1 band signals, analyzing the results for GPS L1 and Galileo E1.
Table 2. GNSS signals considered in the analysis.
The final stage in the simulation plan is testing each technique under realistic channel conditions with the DLR model. In this case, an urban scenario is set, assuming two types of receiver dynamics (a pedestrian user and a vehicular user). No more details about this stage are given because these tests are currently ongoing.
Technique Descriptions
After an initial evaluation of the candidate techniques, the following techniques were selected for implementation and testing in the project.
Multipath Estimating Delay Lock Loop. MEDLL was proposed by Van Nee (see Further Reading). It is a robust statistical approach to the multipath problem, where the maximum likelihood principle is applied to a signal model consisting of LOSS and M-1 multipath rays or echoes. The key idea is to perform the estimation of the whole set of parameters of the incoming signals (that is, amplitude, delay, and phase) in an iterative manner. When their amplitudes, time delays, and carrier phases are estimated, the effect of the reflections in the correlation can be removed. Applying standard assumptions, the maximization of the likelihood function yields a set of interrelated equations from which one can estimate iteratively the LOSS and multipath parameters. The implementation of MEDLL considers a similar architecture as conventional DLLs, although in practice more than three correlators per loop are required for effective multipath mitigation. Despite its demanding requirements in terms of a large number of correlators and computational load, MEDLL was successfully implemented in NovAtel receivers because of its excellent performance in multipath mitigation.
The contributions of all signals (that is, LOSS and an unknown number of echoes) are not calculated simultaneously from the outset. First, the contribution of only one signal is calculated, then the contribution of a second signal is added with the contributions of both signals being optimized, then the contribution of a third signal is added and the contributions of all three signals are optimized, and so on. The process is repeated until a suitable stop criterion is fulfilled. A possible approach for deciding when to stop adding more rays to the signal model is to detect when the error increases; that is, observing that the signal-to-residual ratio when considering an extra path decreases with respect to the case of not considering it, or reaching a maximum number of considered paths, which is a design parameter of this technique.
Multipath Estimating Particle Filter. The MEPF shares with MEDLL the philosophy of estimating the parameters of multipath to mitigate its effect. The main difference is that in this case the statistical principle is not the ML (as in MEDLL) but Bayesian filtering. Here the term Bayesian means that the algorithm is using some sort of a priori information regarding these parameters (such as interdependencies and time evolution models). Therefore, instead of assuming that each integration period is independent of the others, a first-order Markov process is assumed for the unknown parameters (that is, amplitude, delay, and phase). The resulting problem is formulated as a nonlinear state-space model and can be solved by means of a Rao-Blackwellized particle filtering method.
The MEPF has a relatively high computational load, which is a function of the number of particles (Np), the number of correlation points, and the number of rays to be estimated (M). In all cases, the larger the values the more computationally demanding the algorithm is. According to the simulations performed, configurations with Np larger than 2000 are not worthwhile, because it implies a high computational cost and the results do not improve significantly the accuracy of a GNSS receiver. Also notice that the dimension of the state-space to be estimated is 3 × M ([amplitude, phase, delay] × M), and thus estimating an additional multipath ray implies augmenting the dimensionality by three. Theoretically, the convergence results of particle filters are independent of the dimensionality of the problem. However, it is agreed that in practice these methods fail when the problem increases in dimension. Therefore, setting M larger than 5 is not reasonable since the number of particles required for convergence would be too large to consider its implementation in a receiver.
Vector Tracking Loops. A conventional GNSS receiver consists of several parallel scalar DLLs, each of which independently estimates the individual pseudoranges. The parallel set of measured pseudoranges (plus Doppler or accumulated delta range measurements on the carrier) are then fed to a Kalman filter estimator, and thus each DLL effectively produces an independent estimate for each of the N pseudoranges for each of the N satellites. However, not all of their measurements are truly independent, although they are treated as such by the DLL, and if there are more than four measurements being made and four or fewer unknowns, the system is overdetermined. Furthermore, the geometry of the satellite-user paths generally prevents the measurements from being truly independent.
The concept of vector tracking loops firstly appeared in the early 1980s. Recently, the method has attracted the attention of many researchers (see Further Reading) due to its good performance in weak signal scenarios.
In this article, we present the implementation of a vector tracking loop that works with pseudorange and pseudorange-rate measurements and provides estimations for position, velocity, receiver clock error, and receiver clock drift. The vector loop has been integrated in a basic receiver based on the classical DLL and PLL/frequency lock loop (FLL)-assisted architecture, acting as an overlay procedure, which activates automatically once the basic receiver has obtained the first position fix. Then, the position/velocity/time solution is used to jointly estimate the synchronization parameters (time delay and carrier phase) for each of the received GNSS signals by means of an extended Kalman filter, and those values are re-injected into the tracking loop. By using position for deriving such synchronization parameters, the algorithm exploits the problem’s inherent geometric constraints, processing all the channels jointly and providing robustness in scenarios with weak receiving power, high multipath, or fast fading.
Direct Position Estimation. Although the conventional two-step position determination is the approach taken traditionally, it is seen to have a number of drawbacks. In contrast, direct position estimation (DPE for short) proposes an alternative where the estimation of a user’s position is performed directly from the received and sampled signal, thus avoiding intermediate steps and jointly considering signals from all satellites when estimating the position solution. By merging the two-step approach into a single estimation problem, DPE addresses some of the inherent drawbacks of the conventional approach where the dependencies between channels are efficiently exploited, in the sense that signals from visible satellites are jointly processed to obtain the user’s position. At the time of writing, this technique is being implemented and its analysis is left for future publications.
Results
Not all the results that we have obtained have been included in this article; only the most relevant ones for L1/E1 CBOC and BPSK signals are given.
MEDLL. Optimal Correlator Configuration. As stated previously, the test consisted of executing different correlator configurations. TABLE 3 shows the correlator configuration ID, the number of correlation samples, and the location of the late correlators with respect to the prompt one and normalized to the chip time (Tc). The corresponding early samples are defined analogously. For all these configurations, it is assumed that there is a correlator at zero chips.
Table 3. MEDLL correlator configurations tested.
These configurations were selected in order to test different possibilities as regular spacing between correlators (MP31, MP61, or MP91), setting the correlators to inflexion points of the autocorrelation function (ACF) (NC37 or NC43 for CBOC modulation), or variable spacing (as configurations AR01 to AR05 and GE01) that optimize the number of correlators. The results of the tests can be observed in FIGURE 3 for a BPSK(1) signal and for a CBOC(6,1,1/11) signal.
Figure 3. MPEE for MEDLL with BPSK and CBOC signals for different correlator configurations (Tc=chip time).
In general, it is observed that the greater the number of correlators, the smaller is the spacing and therefore, the area covered by the multipath error envelope is smaller. However, the greater the number of correlators, the greater is the processing time. Therefore, it is necessary to find an optimal configuration that best fits the multipath variation.
After having analyzed the tests for these configurations, we found that the optimal configurations are AR01 for the BPSK signal and NC37 for the CBOC(6,1,1/11) signal. These configurations are shown in FIGUR 4, and were used for the remaining tests.
Figure 4. Correlator configurations for CBOC and BPSK signals.
CSCM. As mentioned in the simulation plan description, the tests for a pedestrian and a vehicular user in a moderate multipath environment were performed with a scenario file generated with the CSCM tool. These scenarios are characterized by a series of multipath echo levels, number of echoes, and specific stochastic models that are detailed in Table 1. In this table, the pedestrian scenario is known as SP1 and the vehicular scenario is shown as SV1. The results with these scenarios are presented in this article.
The RMSE for range estimates in these scenarios is shown in FIGURE 5 for SP1 and SV1. For comparison, these plots show also the theoretical lower bound (Nunes bound) computed for each technique.
Figure 5. Range RMSE for MEDLL in pedestrian and vehicular scenarios.
The plots show that MEDLL outperforms the conventional DLL results above a minimum C/N0 value. This happens beyond 32 dB-Hz for a CBOC signal and 35 dB-Hz for a BPSK(1) signal for low dynamics (pedestrian) environments.
In the case of vehicle scenarios such as SV1, it is observed that it requires significantly higher values of C/N0 in order to outperform the DLL RMSE. This result makes the technique impractical for vehicular applications.
Time Performance. A collateral result that has been obtained with CSCM scenarios is the time performance of the technique, compared to the legacy DLL/PLL scheme. The ratios of the execution times needed for the techniques have been computed. This is an indicative measure of the computational load. The time performance of MEDLL with the selected correlator configuration against DLL is 10:1. That is, 10 times more time is required for the MEDLL technique than the DLL to run the same scenario. It is also observed that the ratio for BPSK modulation is slightly larger than the ratio for the CBOC signal, because more correlators are used in that case.
MEPF. Performance Under Controlled Channel. FIGURE 6 shows an example of the performance of the MEPF technique tested in a controlled channel environment.
Figure 6. Results of the MEPF in the controlled multipath environment with 2 and 3 estimated rays including the line-of-sight. For comparison purposes, DLL/PLL results are also included (blue line in upper plot).
It can be observed how the DLL/PLL technique has an error, which grows as the number of rays is increased. However, when the MEPF is applied, it can be observed how the technique is capable of dealing with a multipath-changing environment despite the fact that it is varying in time with the number of rays increasing. It can also be noticed that the response of the technique is practically the same independent of the number of estimated rays (M). These results were achieved with a BPSK signal and 500 particles.
Covariance Matrix Adjustment. Before starting the specific test for the evaluation of the scenario performance, it is necessary to calibrate the particle filter. The calibration procedure consists of the adjustment of the process covariance matrix. The observation covariance can be adjusted, simply by analyzing the raw observables, or it can even be done automatically.
The critical point is the adjustment of the states’ covariance. It has been observed that an improper adjustment of the covariance may cause an increment in the range RMSE or even the divergence of the filter. For that reason, a systematic procedure for the adjustment of the covariance matrix has been followed. This procedure evaluates the performance using different configurations of the covariance matrices of the line-of-sight and multipath parameters in two stages, and allows us to find a set of optimal values for each scenario.
Optimal Correlator Configuration. We also analyzed the optimal correlator configuration for the MEPF technique. A number of configurations in Table 3 were used under CSCM scenarios. It was found that the correlator configuration does not have a strong influence on the performance of the MEPF technique. It is worth mentioning that in cases where fewer than five correlators were used, the performance degraded. Despite the weak influence of the range RMSE with the correlator configuration, it was observed that there is a minimum for the BPSK signal that can be chosen for the MEPF technique (MP31). For the CBOC signal, the same configuration used for MEDLL is finally chosen (NC37), provided that it is the optimal configuration that balances range RMSE and the number of correlators.
Number of Particles. It is important to notice that an initial set of tests was performed with 500 particles. However, it was found that by increasing the number of particles to 2000, the performance of the technique with CSCM was remarkably better. Because of this, for the remaining tests using the CSCM, this number of particles was the default value. Using a higher number is not worthwhile since results are not much improved and the computational load becomes too high.
CSCM Results. The range RMSE results for the pedestrian scenario SP1 with the MEPF technique are shown in FIGURE 7. In this case, it is observed that for the CBOC signal, MEPF outperforms the DLL/PLL technique for low C/N0 values. This result could not be reproduced for the BPSK signal because of the adjustment of the covariance matrix, which in this test was optimized for CBOC.
Figure 7. Range RMSE for MEPF and pedestrian scenario.
These promising results for CBOC open the possibility of using this technique in applications where the LOSS is very weak or where the multipath signals are very strong. More tests with this technique are currently being executed for a better adjustment of covariance settings for BPSK.
Time Performance. The previous time performance analysis was performed with the MEPF technique. The performance ranges among 180:1 and 340:1, when compared to DLL/PLL schemes using three correlation samples. The extremely long time required to simulate the scenario makes this technique inadvisable for implementation within an operational receiver using current technology.
VTL.CSCM Results. The same CSCM scenarios were performed for the VTL technique, but using a multi-channel receiver. Results for pedestrian SP1 and vehicular SV1 scenarios are shown in FIGURE 8.
Figure 8. Position RMSE for VTL with pedestrian and vehicular scenarios.
It must be noted that, in order to perform fair comparisons, the results of ordinary DLL/PLL tracking used a conventional KF for the computation of the position instead of the least squares module available in the GRANADA GNSS Blockset.
In our numerical simulations, a significant improvement in the performance of VTL with respect to the DLL/PLL+KF scheme was not observed in pedestrian environments, due to the low dynamics. Under those dynamic conditions, both systems exhibited similar (statistically equivalent) behavior.
In the case of scenario SV1, the velocity applied to the receiver was higher than in the pedestrian case, and the VTL exhibited a remarkable improvement over the DLL/PLL+KF-based receiver. For the BPSK modulation, it was observed that for low values of C/N0 , the VTL performs better than DLL/PLL+KF. However, for stronger signals, both techniques have the same behavior as shown for the pedestrian scenario results. On the other hand, for the CBOC modulation, it is observed that VTL performs better than DLL/PLL+KF for the whole range of C/N0 values. The precorrelation bandwidth was set to 8 MHz in the case of BPSK, while for CBOC it was set to 14 MHz. That wider bandwidth of the CBOC receiver has an impact in the estimation of the measurements covariance matrix. In can be observed that in such higher dynamic stress conditions, the VTL outperformed DLL/PLL+KF in our numerical simulations.
It must be mentioned that for all the simulations, we assumed the same C/N0 for all satellites. However, we plan to run tests with LOSS fading (variation of C/N0). It is expected that the VTL technique will show its advantage in those simulations.
It was also observed that settings of the KF covariance have an important effect on the results. This covariance must be adjusted according to the multipath signal level. Therefore, a calibration operation, which adapts the KF to a particular scenario should be performed. While the measurement covariance matrix can be adaptively estimated from the output of the DLLs and the PLLs, the process covariance matrix should be adjusted depending on the trajectory characteristics.
Time Performance. The time performance analysis also shows that the time performances of VTL and DLL/PLL+KF are very similar. Only a small increment of 15% of the computational cost for vehicular scenarios has been observed. This makes this technique a good candidate to be implemented in an operational software-based receiver.
Results Overview
After analyzing the current results from the different techniques, some general conclusions on their performance can be made.
MEDLL can be useful to mitigate and improve the pseudorange measurement provided that the C/N0 value is greater than a threshold value. An intuitive reasoning for this is that the estimation of multipath rays is easier at high C/N0 values. Below this value, the MEDLL technique does not present an important advantage over ordinary DLL/PLL given the time performance it has (a ratio of 10:1). MEDLL is especially well-suited for static and pedestrian environments.
MEPF is a technique that still needs research before it can be used operatively. Results show that it works quite well for low C/N0 values when compared to DLL/PLL. Results show that for the CBOC signal, at least 2000 particles are needed to give good results for low C/N0 values. However, its time performance is very poor (around 200:1 with respect to DLL with 2000 particles)
VTL does not present an advantage in the pseudorange measurement domain, but it clearly improves the position solution with respect to a classical DLL/PLL+KF tracker. This improvement is remarkable under dynamic conditions. It is also observed that the time performance is very similar to the DLL/PLL+KF one. Therefore this technique is a good candidate for implementation in a receiver prototype based on embedded hardware, an FPGA implementation, or software-based radio.
For the sake of completeness, a performance comparison in the range domain of the different techniques with the BPSK and CBOC signals for the pedestrian SP1 scenario are presented in FIGURE 9. The metric used is the range RMSE.
Figure 9. Compared range RMSE for different techniques: pedestrian scenario with BPSK(1) and CBOC(6,1,1/11) signals.
Conclusions and Future Work
This article has presented a series of innovative multipath-estimating techniques using non-conventional approaches in the tracking algorithms. These non-conventional approaches are based on maximum likelihood (MEDLL) or non-linear online filtering algorithms (MEPF). Alternative approaches in the positioning algorithms have also been analyzed (VTL and DPE).
To check the performance of these techniques, we have developed a simulation platform, able to carry out deterministic multipath simulations, in which the multipath environment can be controlled deterministically, and stochastic simulations based on tested multipath models (CSCM and DLR). The CSCM model is capable of simulating realistic multipath environments but with the capability to control the multipath ray parameters and the number of these rays. The DLR model allows us to perform simulations based on the conditions in realistic urban environments.
This article has focused on the CSCM model. It shows simulations for a pedestrian and a vehicular scenario that represent typical dynamic conditions for each kind of user.
These results show that the MEDLL technique performs very well in a static multipath environment under low dynamics with good visibility conditions.
Concerning the MEPF, it has been found that the adjustment of the covariance for the observables is very important for achieving good results for the range RMSE. If this adjustment is done well, the results for low values of C/N0 outperform the ordinary DLL/PLL technique. This adjustment has been successfully achieved for a CBOC signal, but a BPSK signal still requires additional work. This may open the possibility of using this technique in applications in which the LOSS is very weak.
It has also been observed that the VTL technique is very effective in high dynamics applications and noisy environments, provided that the internal KF process noise covariance has been properly estimated. VTL also shows a performance very similar to the DLL/PLL+KF scheme in mild-condition scenarios. That makes this technique a good candidate for implementation in a real-time software-based receiver.
Finally, it is necessary to remark that ARTEMISA is still a work in progress. An extensive simulation campaign in a realistic urban environment under different conditions using the DLR multipath model is ongoing. In addition to the techniques presented in this article, other advanced techniques such as direct position estimation are under evaluation.
Acknowledgments
The ARTEMISA project, funded by the European Space Agency (ESA) is being carried out by DEIMOS Space, with the Centre Tecnològic de Telecomunicacions de Catalunya as subcontractor. The content of the present article reflects solely the authors’ views and by no means represents official ESA policy.
The authors of this article would like to thank Tiago Peres, Joao Silva, and Pedro Silva from DEIMOS Engenharia; José Antonio Pulido from DEIMOS Space; and Roberto Prieto-Cerdeira from ESA’s European Space Research and Technology Centre for their support in the adaptation of the GRANADA GNSS Blockset and the simulation platform to the requirements of the techniques and multipath environments tested in the project.
ANTONIO FERNANDEZ co-founded DEIMOS Space with headquarters in Madrid, in 2001, where he is currently in charge of the GNSS Business Unit.
MARIANO WIS is currently working for DEIMOS Space as a project engineer in the GNSS Business Unit. He is also a Ph.D. candidate in the Aerospace Science and Technology Program of Universitat Politècnica de Catalunya in Barcelona.
PAU CLOSAS is a senior research associate and head of the Statistical Interference Department in the Communications Systems Division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) in Barcelona.
CARLES FERNANDEZ–PRADES is serving as head of the Communications Systems Division at CTTC, where he holds a position as senior researcher.
JOSE A. GARCIA is with the Radio Navigation Systems and Techniques Section at the European Space Agency’s European Space Research and Technology Centre (ESA/ESTEC) in Noordwijk, The Netherlands.
FRANCESCA ZANIER is also with the ESA/ESTEC Radio Navigation Systems and Techniques Section.
MASSIMO CRISCI is the head of the ESA/ESTEC Radio Navigation Systems and Techniques Section.
FURTHER READING
• ARTEMISA
“ARTEMISA: New GNSS Receiver Processing Techniques for Positioning and Multipath Mitigation” by A.J. Fernandez, J.A. Pulido, M. Wis, F. Zanier, R. Prieto-Cerdeira, M. Crisci, P. Closas, and C. Fernández-Prades in Proceedings of Navitec 2012, the 6th ESA Workshop on Satellite Navigation Technologies, and the European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands, December 5–7, 2012, doi: 10.1109/NAVITEC.2012.6423092.
• GRANADA GNSS Blockset
“Factored Correlator Model: A Solution for Fast, Flexible, and Realistic GNSS Receiver Simulations” by J.S. Silva, P.F. Silva, A. Fernández, J. Diez, and J.F.M. Lorga in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 25–28 2007, pp. 2676-2686.
• Signal Propagation Statistical Models
“A Location and Movement Dependent GNSS Multipath Error Model for Pedestrian Applications” by A. Steingass, A. Lehner, and F. Schubert in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation, Savannah, Georgia, September 22–25, 2009, pp. 2284-2296.
“Statistical Modeling of the LMS Channel” by F.P. Fontan, M. Vazquez-Castro, C.E. Cabado, J.P. Garcia, and E. Kubista in IEEE Transactions on Vehicular Technology, Vol. 50, No. 6, November 2001, pp. 1549–1567, doi: 10.1109/25.966585.
• Multipath Estimating Delay Lock Loop
“The Multipath Estimating Delay Lock Loop: Approaching Theoretical Accuracy Limits” by R.D.J. Van Nee, J. Siereveld, P. C. Fenton, and B. R. Townsend in Proceedings of PLANS 1994, the Institute of Electrical and Electronics Engineers Position, Location and Navigation Symposium, Las Vegas, Nevada, April 11–15, 1994, pp. 246–251, doi: 10.1109/PLANS.1994.303320.
• Multipath Estimating Particle Filter
“Nonlinear Bayesian Tracking Loops for Multipath Mitigation” by P. Closas, C. Fernández-Prades, J. Diez, and D. de Castro in International Journal of Navigation and Observation, Vol. 2012, Article ID 359128, 15 pages, 2012, doi:10.1155/2012/359128.
“A VDLL Approach to GNSS Cell Positioning for Indoor Scenarios” by F.D. Nunes, F.M.G. Sousa, and N. Blanco-Delgado in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22–25, 2009, pp. 1690–1699.
• Direct Position Estimation
“Maximum Likelihood Estimation of Position in GNSS” by P. Closas, C. Fernández-Prades, and J.A. Fernández-Rubio in IEEE Signal Processing Letters, Vol. 14, No. 15, May 2007, pp. 359-362, doi: 10.1109/LSP.2006.888360.
• Some Previous Innovation Columns on Multipath Mitigation
Here are a few things about your colleagues that perhaps you did not know: they are a quite colorful, varied, and shall we even say motley crew. Hidden backgrounds came to light during the magazine’s Leadership event in Nashville, during a game called “Guess Who’s Spoofing the Dinner?” One person at each table, secretly recruited in advance, lied freely in response to three questions, while everyone else was bound to tell the strict truth. The table then had to identify the spoofer in their midst.
The truths turned out to be stranger than the fictions. As ever. This is what’s known, appropriately, as a truism.
The questions posed:
What is the farthest from your birthplace that you have traveled?
What was the shortest time you ever held a job? What job?
Who is the most famous person you have met?
One person had met Hillary Clinton, another the first lady of China, and two people had met the Queen.
One met Janis Joplin (throwing that table into a total tizzy), another had an audience with two popes, Benedict and John Paul II (not simultaneously), while yet another had met John Paul II and Sophia Loren (again, presumably, not on the same occasion).
But the most elevated encounter was described by a soft-spoken gentleman who taught the Dalai Lama to play frisbee. His Holiness had never done, and evinced some curiosity as to how it worked.
Janis Joplin’s crony claimed his shortest employment was installing fire alarms at a Catholic home for girls in a delicate way in the early Sixties. His table declared him the spoofer. But they were wrong. They were wrong.
The shortest employment for one engineer at the dinner was also his longest, not to mention his most current: 30 years. He has never held another job.
One young researcher worked briefly as a shepherdess, until getting trampled by a flock of sheep. Imagine your lab-coated colleague in a long white frock, ruffled cap, and crook stick.
In their travels, folks had reached Tierra del Fuego, Tasmania, Everest Base Camp, China (and conversely, Nashville from China by a select few), and Capetown, South Africa, but the furthest flung had landed on Antarctic ice in a Hercules C-130, on skis.
Ironically, one travel tale was challenged not because of the furthest destination but the start point. A well known GNSS scientist vowed that he came from Texas, but a gentleman from the European Commission — the same who had met John Paul and Ms. Loren — doubted this severely, because the teller did not sport cowboy boots nor a big belt buckle. Worse, he could not recall what Sam Houston’s boys cried out as they went into the Battle of San Jacinto, winning glory and Texas independence.
Italians, it seems, are quite well versed in Texan history.
There is a lesson in this for all of us, though our scientist claims it’s all just an invention of the movies.
Astrium has announced that the geo-information activities of Astrium Services have entered into a partnership agreement with IHS, a source of defense and security information, to deliver satellite imagery and services for intelligence analysis and reporting.
Leveraging this partnership will enable IHS to provide new insights and even more up-to-date and detailed information, meeting demand for fused intelligence sources to tap easily accessed information, Astrium said. Geospatial data plays a critical role in developing intelligence, formulating strategic policy and driving operational interventions.
Astrium Services brings to IHS the capability to deliver imagery and defense-oriented services based on unique access to the only multi-resolution imaging satellite. Astrium’s offering has been specifically developed to support high-currency requirements, including the varied challenges faced by military and intelligence agencies.
Under the agreement, Astrium Services will provide newly acquired imagery from its Pléiades, SPOT and TerraSAR-X satellites for use as primary sources and fusion with open source information, for actionable intelligence. It will also provide access to the GO Monitor service, which delivers reliable surveillance and change information anywhere on Earth.
By fusing IHS insight with Astrium Services imagery, IHS analysts will be able to deliver broader contextual analysis and more granular insight to meet the needs of business and national security professionals, Astrium said.
With more than 100 years of history as Jane’s, IHS has a reputation built on products such as IHS Jane’s Fighting Ships, IHS Jane’s All the World’s Aircraft and IHS Jane’s Defence Weekly.
By Miguel Torroja, Steve Malkos, and Christophe Verne
Users of smartphones, tablets, and other devices expect position with the highest level of accuracy, always available, with the least amount of power consumed. One recent improvement fulfilling this demand involves operating-system services for location on smartphones, and the evolution towards lower power solutions.
“Please connect to a charger — The battery is getting low: less than 15 percent remaining.”
Handsets are battery-supplied devices, and a user’s tolerance for features is driven by battery consumption. There are many examples of technologies where users do not run certain hardware or features because it will consume the battery and make the phone useless within a short period of time.
The application processor (AP) of a handset device is very powerful, and is the part that consumes most of the battery life. Today’s smartphone multicore application processor is faster than many desktop computers that are just a few years old. Whatever the application, when it uses the AP, it can draw up to hundreds of milliamperes (mAs).
For the last few years, the trend for GNSS has been host-based positioning. Host-based designs have less logic on the GNSS integrated circuit (IC) and employ the host AP for a portion of the positioning computation. This strategy has three advantages:
Shares memory and code resources with the application processor.
Reduces the cost of the dedicated GNSS hardware.
Sharing the processor makes sense since it is already running.
Traditionally, when the GNSS solution was running, a navigation application that utilized the AP was also running.
However, when we only want to compute GNSS positions in the background, and we do not need a third-party application running on the AP, a host-based IC architecture is not the optimal solution with regard to system power consumption. This article explains some of the technologies used to compute a GNSS position using an ultra-low power (ULP) hybrid solution that combines the classic host-based GNSS architecture with a host-offload architecture that minimizes the use of the AP.
We discuss here two applications that benefit from a host-offload architecture: geofencing and position batching.
We will review the requirements for a platform to support a new hybrid GNSS positioning solution. Different host-offload technologies for geofence, such as GNSS, Wi-Fi, and Cell-ID, will be compared. Broadcom’s ultralow-power host-offload GNSS solution supports any operating system. We focus here on Android’s operating system because it is the most open OS.
Always-on Applications
Geofencing is an application that sends reports or triggers alarms when a predefined area is crossed. For example, users can be alerted to discounts with e-coupons when walking through a mall, or to “don’t forget the milk” — users can set their own reminder notifications based off of location; also, social networking. One example of location-based reminders is through Google Keep, which uses Android’s Geofence APIs on platforms that support hardware geofencing; this application will automatically take advantage of the hardware geofence solution.
Geofencing applications run in the background for long periods of time, and their main task is to compute positions (fixes) without the need of assistance from other applications. An ultra-low-power GNSS position solution, or always-on positioning solution, is desirable for these scenarios. Typical applications require notifications when entering or exiting a geofence area, or require periodic reporting of user positions relative to the fence.
Geofencing is not something new. API support has been provided in mobile OS for many years, but only now can it be used without draining the battery, thanks to this new host-offload architecture.
Figure 1 shows a circular geofence boundary and an alarm. In that example, the alarm was triggered when entering the fence.
Figure 1. Alarm when the vehicle enters a geofence area.
Breadcrumbing or position batching pertains to storing of positions, referred to as crumbs, which are accumulated for a certain amount of time and then pushed all at once to the application. Examples would be fleet or asset tracking applications, or people that wants to track their position while they are running.
Currently, Android does not support breadcrumbing as a native feature. There is some ongoing work, and APIs are being defined.
GNSS Positioning Models
Before smartphones, the dominant GNSS hardware architecture employed a system-on-chip solution. The position/velocity/time (PVT) comes directly from the hardware, and all the computations are done in the GNSS IC.
On-Chip Positioning requires two things: a powerful-enough central processing unit (CPU) and lots of memory. The increase in CPU and memory performance are not free; they translate directly into more power and higher manufacturing costs.
The RF block in Figure 2 is intentionally drawn with a similar size to the CPU and memory, to emphasize the need for higher resources for a complete on-chip solution.
Figure 2. On-chip solution.
Host-Based Solution. GNSS positioning requires dedicated hardware, complex software, and protocols. This complexity led GNSS providers to move parts of the software out of the IC to the AP.
Using a mobile phone’s AP for position computation is one method of reducing the CPU and memory power footprint from the GNSS IC. At the same time, it also increases the power consumed by the platform needed to compute GNSS position, since part of the computation is not performed on the host-based IC. APs may consume approximately 100 mA just to be operational.
Figure 3 shows a typical configuration with dedicated GNSS hardware and a generic AP. In host-based mode, both the AP and the GNSS IC run in parallel when computing positions. The AP controls the GNSS hardware.
Figure 3. I/O connections in on-host positioning.
With this type of shared architecture, shown in Figure 4, the CPU and the memory on the GNSS IC are reduced, shrinking the size of the chip and reducing power consumed by the chip. In Figure 4 we see that the AP is communicating with the dedicated hardware, and the final PVT is computed by the AP. This solution fits well in many applications, such as navigation, where the AP has to run a mapping application at the same time.
Figure 4. Host-based solution.
Hybrid Positioning. For geofencing, we need a hybrid model, one which keeps GNSS IC complexity similar to the host-based architecture, but also offloads some of the host-based positioning so that the host can go to sleep.
In Broadcom’s hybrid mode, the AP does not need to run when GNSS positions are computed. Broadcom’s hybrid IC does not invoke the host AP often, and thus achieves an even lower power footprint. The CPU on the GNSS IC used for computing position is a dedicated one. It needs to be carefully chosen because it has to be powerful enough to compute positions and be as power efficient as possible. All this is done while keeping the GNSS IC area size in mind, to control cost.
Detailed analysis and steps were considered to ascertain the minimum requirements for the CPU and other resources to best accomplish the on-chip positioning task.
Other considerations: the GNSS IC must be powered even when the AP is suspended, and the GNSS IC must be capable of waking up the AP. Figure 5 shows a possible implementation using a dedicated I/O signal controlled by the IC to wake up the host AP.
Figure 5. I/O connections in hybrid positioning.
With this architecture, the host AP will still be needed to provide some assistance data to the GNSS IC. The assistance provided allows the GNSS IC to not invoke the host AP often and thus achieve an even lower power footprint.
Geofencing Methods
Certain OS application APIs have been supporting geofencing for many years. Currently, we can find geofencing APIs in most of the mobile OSs in the market.
There are four main types of geofencing: GNSS software geofencing, GNSS hardware geofencing, network software geofencing, and network hardware geofencing (Table 1).
Table 1. Geofencing methods.
GNSS Hardware Geofencing. In this method, the one described in detail in this article, the OS initiates a request and offloads the areas of interest to the hardware. After that, the AP can go to sleep and the hardware is responsible for computing positions and checking the areas of interest. This method basically relies on GNSS hardware to compute positions and check the programmed fences.
GNSS Software Geofencing. Here, the OS initiates regular fixes to a host-based GNSS IC design. Then it invokes both the AP and the GNSS IC at the same time to check against the defined fence areas.
Network Geofencing. In this method, the OS requests network IDs from the hardware (that is, baseband modem Cell-ID and Wi-Fi access points). The OS uses different positioning technologies to compute position. This usually requires a connection to a server to retrieve location information about the different IDs. The position is used to check the geofences.
In network hardware geofencing, a set of network IDs is offloaded from the OS to the network hardware ICs. The hardware can poll for these IDs, and wake up the host when found.
Network versus GNSS Geofencing
A good geofencing solution combines both network and GNSS methods because each solution benefits from each other.
GNSS positioning solutions compute positions in open-sky environments with accuracy to a few meters and have worldwide coverage. However, they cannot work in deep indoor spaces.
Network geofencing using cell IDs is quite inaccurate, but works very well indoors. Network geofencing using a Wi-Fi access point provides reasonable accuracy, but location of the access points is not always known and it does not have full coverage.
Geofencing in Android 4.3. The API for applications supports geofencing. Starting from the first version of Android, the application just initiates a proximity alarm and will get an event when its boundaries are crossed. The OS is responsible for notifying the application when such an event occurs, and can use any technologies it sees fit.
The API that applications use is very simple. The monitoring is handled by the OS and is hidden to the application (for example, technologies, periodicity of checks, and accuracies).
Software Geofence in Android. Software geofencing has been the default method until recently, as there was no native hardware support. In this mode, the host-based GNSS positioning engine is started like any other position request. The Android framework is the one dealing with the monitoring of the geofences, and therefore, the AP must run continuously to handle periodic position checks. That means the software-geofencing logic is mainly in the framework layer of Android (see basic layers diagram shown in Figure 6).
Figure 6. Android framework.
More recent versions of Android dropped the support for software-based geofencing in favor of a host-based GNSS system, likely because of the big impact on the battery. Broadcom developed a low-power GNSS hardware solution for geofencing.
Hardware Geofence in Android. Starting from Android 4.3, a new interface is available to use hardware geofencing. This interface is not visible to the application, and it is only used as a low-level interface. To support the new hardware-geofence interface, the native driver only has to register to a new GNSS interface defined in the native hardware abstraction layer (HAL) of Android.
There are other protocols known to support geofencing. Table 2 provides a short list.
Table 2. Geofencing support on different platforms.
Broadcom Hybrid Positioning
Android defines interfaces to the hardware, referred to as the HAL.
GNSS Host Software. GNSS providers need to comply to the HAL interface, which is at the Java native interface (JNI) level. Below the JNI lies the GNSS host software (Figure 7).
For the host-based solution, the GNSS host software handles most of the heavy computing.
For the hybrid solution, the GNSS host software does some of the heavy computing, but positions are computed inside the GNSS IC.
To support this new hybrid solution, two main changes are required compared to the usual host-based solution, as described below.
First, the hybrid GNSS IC must be autonomous while the host AP is sleeping. This implies that some power domains are maintained when the GNSS is in use. This typically means at least one of the outputs of the power management unit (PMU) should be dedicated to the GNSS only (Figure 8).
Figure 8. Power domains.
Second, the GNSS IC must be able to wake up the host AP so as to send geofence notifications, or to request assistance data. This is usually done through a dedicated pin.
Acquisition and Sleep Period. Most of the power in the GNSS IC is used by the radio and analog part. To reduce power, this part is switched on only during acquisition. As soon as enough measurements are observed, the radio part is switched off while the digital part computes a fix.
After each computed position, the GNSS IC can go into a deep power-saving mode until the next acquisition. The distance to the closest fence in conjunction with the user speed is used to determine when to compute the next position (Figure 9):
Figure 9. Start fix decision logic.
Once the GNSS IC starts computing positions, the AP can go into sleep mode (Figure 10). Total power per position computed is reduced, and the time between fixes is no longer constant, as shown in Figure 11.
Figure 10. Sleep time between fixes.Figure 11. Duty cycling.
In Figure 12, the lower square-shaped pattern corresponds to a position computation from the hardware GNSS IC. Once we have an alarm, the host has to be woken up and we can see the impact in power in the big peaks after a position is computed.
Figure 12. Power graph.
Alarm Triggering
When a geofence area is crossed, the GNSS IC needs to wake up the AP. This is achieved using a dedicated interrupt pin. After asserting it, an alarm and geofence status is sent to the AP.
Power Consumption. We calculate the total average current by splitting it into three components, as shown in the following formula:
Some of these parameters are set by the host: for example, how often the fix should be computed. The extra current drained by the GNSS IC is the one defined by
∆I is the change in current drain when computing positions.
We can also express this formula based on the average number of position attempts:
where Tp is the average time between fixes (the time the GNSS IC stays in sleep).
Table 3 illustrates some theoretical ∆I current savings with respect to Tp.
Conclusion
As APs become faster and faster, their power consumption goes up. A novel hybrid GNSS receiver has been presented, which offloads some of the host-based processing into the GNSS hardware, offering ultra-low system power consumption versus the traditional methods. The new hybrid positioning solution is a good approach for always-on applications that need to have location information always available, without requiring the host to be running, as is the case with geofencing and breadcrumbing.
References
We would like to thank Jason Goldberg, Frank van Diggelen, and Manuel del Castillo, all of Broadcom, who reviewed this article and spent many hours with us discussing the topics point by point.
Miguel Torroja is a principal software developer at Broadcom. He has an M.Sc. in electrical engineering from Ramon Llull University, Barcelona. Since 2011, he has been working on the design and development of algorithms for optimizing power consumption in GNSS host-offload solutions.
Steve Malkos is a senior program manager at Broadcom. He has a B.S. in computer science from Purdue University. He has been active in the development of A-GNSS technologies such as hybrid location services, long-term predicted orbits (LTO), Broadcom’s worldwide reference network (WWRN), and secure user-plane location (SUPL). He has five patents issued and 16 pending.
Christophe Verne is a manager of software engineering at Broadcom. He has an M.S. in electrical engineering from Ecole Centrale, Paris. He has been involved in the development of GNSS and A-GNSS technologies at EADS, Sagem, Global Locate, and Broadcom, where he has been working on low-power host-offload positioning.
Linx Technologies announces its launch of the self-contained, high-performance FM GPS receiver modules. At 15 x 13 millimeters in size, the MediaTek MT3339-based FM Series gives the module fast lock times and high position accuracy even at low signal levels, the company said.
The module’s very low power consumption helps maximize run times in battery powered applications, such as positioning and navigation, location tracking, marine, and asset management, according to Linx Technologies.
Using the built-in MediaTek MT3339 chipset, The FM module can simultaneously acquire on 66 channels and track on up to 22 channels, providing standard NMEA data messages through a UART interface. A simple serial command set can be used to configure optional features.
The GPS receiver is completely self-contained and only requires an antenna. It powers up and outputs position data without any software set-up or configuration. As a result, the FM Series is easy to integrate, the company said.
With built-in hybrid ephemeris prediction technology, the FM Series predicts satellite positions for up to three days and delivers start times of less than 15 seconds under most conditions.
In addition, the available GPS Master Development System connects a FM Series Evaluation Module to a prototyping board with a color display that shows coordinates, speedometer and compass for mobile evaluation. A USB interface allows simple viewing of satellite data and Internet mapping, as well as custom software application development.
Ten years ago, scientists watching the skies experienced a Halloween fright of cosmic proportions, when space weather degraded GPS signals, affecting land and ocean surveys, and commercial and military aircraft navigation.
The most extreme of what became known as the Halloween Storms hit on October 30, 2003 — ten years ago today. According to the National Oceanic and Atmospheric Agency, the Earth could experience a repeat performance this Halloween, with a 35 percent chance of a major storm at high latitudes.
In mid-October 2003, a bundle of concentrated magnetic energy emerged from the Sun’s interior, forming a large sunspot, a site of seething activity. Enormous solar flares soon followed.
Then, on October 28, the sunspot abruptly ejected a concentrated mass of electrically conducting solar wind, flinging it out into interplanetary space toward the Earth. Less than a day later, on October 29, a geomagnetic storm was initiated as the solar wind disrupted the Earth’s protective magnetosphere.
Over the next three days, the “Halloween magnetic storm” would evolve and grow to become one of the largest such storms in half a century. Magnetic storms are global phenomena, and their effects can be easily seen around the world. During the Halloween storm, for example, magnetic direction in Alaska quickly changed by more than 20 degrees. In other words, the storm was so large that it could be measured with a simple compass. The Halloween magnetic storm also produced spectacular aurora, with green phantom “northern lights” seen as far south as Texas and Florida.
“The aurora was exciting,” said Richard Langley, GPS World’s Innovation editor. “I’ve never seen a better one since.”
This full-sky aurora was observed near Fredericton, New Brunswick, Canada (46 degrees north latitude) on October 30, 2003. (Photo courtesy of Richard Langley.)
Langley explained the effect of the phenomenon in his introduction to the October 2004 Innovation article, “Combating the Perfect Storm: Improving Marine Differential GPS Accuracy with a Wide-Area Network.”
It was previously thought that the mid-latitude North American ionosphere was reasonably benign, with minimal storm effects of relevance for marine DGPS users. However, during ionospheric storms in May and October, 2003, [single-frequency] marine DGPS horizontal position accuracies were degraded by factors of 10–30.These degraded accuracies persisted for hours and were well beyond system tolerances specified for marine DGPS users. Such ionospheric activity is not unusual during the years following solar maximum, and is expected to persist for several years.
Langley provides background on what scientists learned from the Halloween Storms in his February 2011 Innovation column, “GNSS and the Ionosphere: What’s in Store for the Next Solar Maximum?”:
The current solar cycle is referred to as cycle 24. During the last solar cycle, cycle 23, the GNSS community was alert and aware of what could happen, and therefore many events were observed and analyzed. Among the most well-known events is a sequence of storms during October and November 2003, commonly referred to as the Halloween Storms.
The most extreme was the storm on October 30, 2003, which resulted from a CME on October 29 at 20:49 UTC, which subsequently impacted Earth’s magnetic field at 16:20 UTC on October 30 and produced a great geomagnetic storm, which lasted for many hours.
Effects on GPS positioning of this storm have been documented by the GNSS research group of the Royal Observatory of Belgium, where kinematic analyses of data from 36 GNSS stations in Europe showed position errors of more than 10 centimeters in the horizontal and up to 26 centimeters in the vertical between 21:00 and 22:00 UTC on October 30. The position errors were largest for locations in northern Europe including Sweden and Norway. The data analysis was carried out using high-quality carrier-phase data, and the processing was based on using an ionosphere-free linear combination of observations from the L1 and L2 frequencies, whereby the first-order effect of the ionosphere is removed from the results. The position errors are thus caused by mainly higher order ionospheric effects.
For navigation-grade GPS positioning, a U.S. National Atmospheric and Oceanic Administration technical memorandum reported that the Wide Area Augmentation System (WAAS) vertical error limit of 50 meters was exceeded for a period of about 11 hours on October 30, 2003. This means that, in practice, WAAS was not available for precision aircraft approaches during that time. The European Geostationary Navigation Overlay Service (EGNOS) was not transmitting during the storm, but simulations carried out later by ESA showed that the boundary regions of the EGNOS coverage area would have been especially affected by a reduction in service availability of about 20–60 percent during that day.
The simulations also showed, however, that in the center of the EGNOS coverage area (in the vicinity of northern Italy), the effect would have been much smaller with a reduction in service availability of only 5–6 percent over the day.
Such large storms are also often accompanied by displays of aurora (aurora borealis and aurora australis) at lower latitudes than normal.
Another shot of the Halloween 2003 aurora, as seen near Fredericton, New Brunswick. (Photo courtesy of Richard Langley)
Other Innovation columns assessing the ionosphere’s effect on GPS include:
Microsemi Corporation has entered into a definitive agreement with Symmetricom to acquire the precision time and frequency company for $230 million. Microsemi is a provider of semiconductor solutions differentiated by power, security, reliability and performance.
Microsemi, headquartered in Aliso Viejo, California, will pay $7.18 per share through a cash tender offer, representing a premium of 49 percent based on the average closing price of Symmetricom’s shares of common stock during the 90 trading days ended October 18. The board of directors of Symmetricom unanimously recommends that Symmetricom’s stockholders tender their shares in the tender offer. The total transaction value is approximately $230 million, net of Symmetricom’s projected cash balance at closing.
Headquartered in San Jose, California, Symmetricom provides highly precise timekeeping technologies and solutions that enable next-generation data, voice, mobile and video networks and services. It provides timekeeping in GPS satellites, national time references, and national power grids as well as in critical military and civilian networks.
“The acquisition of Symmetricom will create the largest and most complete timing portfolio in the industry today,” stated James J. Peterson, Microsemi president and chief executive officer. “From source to synchronization to distribution, Microsemi will offer an end to end timing solution for an expanded range of markets, driving increased dollar content opportunity and revenue growth.”
“The acquisition of Symmetricom by Microsemi will create a powerful combination,” said Elizabeth Fetter, Symmetricom’s chief executive officer. “I believe Microsemi is the ideal company to leverage Symmetricom’s technology and capabilities further into the communications market along with the scale to accelerate the adoption of the company’s innovative new chip scale atomic clock (CSAC) technology into broader markets.”
Microsemi expects significant synergies from this immediately accretive transaction. Based on current assumptions, Microsemi expects the acquisition to be $0.22 to $0.25 accretive in its first full calendar year ending December 2014.
Microsemi reaffirms its fiscal fourth quarter guidance included in its fiscal third quarter earnings release issued on July 25. Microsemi currently intends to announce its fiscal fourth quarter results on November 7. Further details will be forthcoming.
Tender Offer and Closing. Under the terms of the definitive acquisition agreement, Microsemi will commence a cash tender offer to acquire Symmetricom’s outstanding shares of common stock at $7.18 per share, net to each holder in cash. Upon satisfaction of the conditions to the tender offer and after such time as all shares tendered in the tender offer are accepted for payment, the agreement provides for the parties to effect, as promptly as practicable, a merger which would result in all shares not tendered in the tender offer being converted into the right to receive $7.18 per share in cash. The tender offer is subject to customary conditions, including the tender of at least a majority of the fully diluted shares of Symmetricom’s common stock and certain regulatory approvals, including the expiration or termination of the applicable waiting period under the Hart-Scott-Rodino Antitrust Improvements Act, and is expected to close in Microsemi’s fiscal first quarter, ending Dec. 29, 2013. No approval of the stockholders of Microsemi is required in connection with the proposed transaction. Terms of the agreement were unanimously approved by the boards of directors of both Microsemi and Symmetricom.
Under the terms of the merger agreement, Symmetricom may solicit superior proposals from third parties for a “go shop” period that extends through November 8. It is not anticipated that any developments will be disclosed with regard to this process unless and until Symmetricom’s board of directors makes a decision to pursue a potential superior proposal. Jefferies LLC, which is acting as Symmetricom’s financial adviser, will assist Symmetricom with Symmetricom’s go-shop process. There are no guarantees that this process will result in a superior proposal. The merger agreement provides Microsemi with a customary right to match a superior proposal. The agreement also provides for certain termination fees payable to Microsemi in connection with the termination of the agreement in certain circumstances.
Conference Call. Microsemi will host a conference call, solely to discuss details of the transaction. A live webcast relating to the transaction will be available in the “Investors” section of Microsemi’s website at www.microsemi.com in advance of the conference call.
