Ultra-Low-Power, High-Accuracy Location for Wearable GNSS Devices: From Host-Based to On-Chip
Photo: Steve Malkos, Manuel del Castillo, and Steve Mole, Broadcom Inc., GNSS Business Unit
As location penetrates smaller and smaller devices that lack memory and computation power, GNSS chips must reacquire the standalone capability that they shed when first going to small form factors such as phones. A new chip with a new architecture demonstrates navigation and tracking and avoids burdening its main processor with heavy software.
By Steve Malkos, Manuel del Castillo, and Steve Mole, Broadcom Inc., GNSS Business Unit
End users first experienced the amazing capabilities of GPS 12 years ago with early mass-market GPS devices. The focus was on navigation applications with specific tracking devices like personal navigation devices and personal digital assistants (PNDs, PDAs). With the advent of smartphones, GPS became a must-have feature. Other constellations were added to improve performance: GLONASS, QZSS, SBAS, and very recently, BeiDou. In the current phase, the focus is shifting to fitness applications and background location. This is not an insignificant change.
Always-on connected applications, high-resolution displays, and other such features do not improve battery life. This article describes new ultra-low-power, high-accuracy location solutions for wearables’ power consumption.
Impact of Always-On Connected Applications
New applications require frequent GNSS updates with regard to user position. Sometimes the application will be open and other times it will not. The chips need to keep working in the background, buffering information and taking predefined actions. The GNSS chips need to be able to cope with these new requirements in a smart way, so that battery life is not impacted. Saving power is now the name of the game.
Furthermore, GNSS is penetrating small devices: the Internet of Things (IoT) and wearables. They do not have the luxury of large resources (memory, computation power) as smartphones do. GNSS chips cannot leverage the resources in those devices; they need to be as standalone as possible. In summary, the new scenario demands chips that:
do not load device’s main processor with heavy software;
use less power while maintaining accuracy;
can be flexibly configured for non-navigation applications.
New GNSS Chip Architectures
The industry is designing chips to meet these requirements by including the following features:
measurement engine (ME) and positioning engine (PE) hosted on the chip;
accelerometer and other sensors directly managed by the chip;
new flexible configurations, duty cycling intervals, GNSS measurement intervals, batching, and so on.
These features require hardware and software architectural changes. The new chips need more RAM than that required for smartphones, as they must now host the ME and PE. Wearables and IoT devices are small, cheap, and power-efficient. They do not have large processors and spare memory to run large software drivers for the GNSS chip. In many cases, the device’s microcontroller unit (MCU) is designed to go into sleep mode if not required, that is, during background applications. Therefore, new GNSS chips with more RAM are much better adapted to this new scenario.
New chips must tightly integrate with sensors. The accelerometer provides extremely valuable information for the position update. It can detect motion, steps, motion patterns, gestures, and more. However, as a general rule, the MCU’s involvement in positioning should be minimized to reduce power consumption. For power efficiency, the new GNSS chips must interface directly with the sensors and host the sensor drivers and the sensor software.
Finally, new chips must adapt to different human activities as they are integrated into wearable devices. This is the opposite approach from past developments where GNSS development was focused on one use case: car navigation. Now they must adapt to walking, running, cycling, trekking, swimming, and so on. All these activities have their particularities that can determine different modes in which new GNSS chips can work. Electronics must now conform to humans instead of the other way around. New wearable-chip GNSS tracking strategies include dynamic duty cycling and buffering, which contribute to the goal of reducing power consumption without compromising accuracy.
Satellite positioning embedded in devices over the last few years first saw on-chip positioning before the era of smartphones, where you had dedicated SoCs that supported the silicon used to compute the GNSS fix. These expensive chips had lots of processing power and lots of memory. Once GNSS started to be integrated into cellphones, these expensive chips did not make sense. GNSS processing could be offloaded from the expensive SoCs, and part of the GNSS processing was moved onto the smartphone application processor directly.
Since navigation is a foreground type of application, the host-based model was, and is still, a very good fit. But with advances in wearable devices, on-chip positioning will become the new architecture. This is because the host processor is small with very limited resources on wearables; and because energy must be minimized in wearables, reducing the processor involvement when computing GNSS fixes is critical.
Some vendors are taking old stand-alone chips designed for PNDs and repurposing them for wearable devices. This approach is not efficient, as these chips are large, expensive, and use a lot of power.
GNSS Accuracy
While the new fitness and background applications in wearables have forced changes in GNSS chips’ hardware and software architectures, GNSS accuracy cannot be compromised. Customers are used to the accuracy of GNSS; there’s no going backwards in performance in exchange for lower power consumption.
Figure 1. Software architecture for wearables.
A series of tests shown here demonstrate how a new wearable, ultra-low-power GNSS chip produces a comparable GNSS track to existing devices using repurposed full-power sportwatch chips, while using only a fraction of the power.
Speed Accuracy. Not only does the ultra-low-power solution produce a comparable GNSS track, it actually outperforms existing solutions when it comes to speed and distance, thanks to close integration with sensors and dynamic power saving features (Figures 2 and 3).
Figure 2. Ultra-low-power versus full power.Figure 3. Full-power sportwatch, left, and ultra-low power chip, right, in more accuracy testing.
GNSS Reacquisition. GNSS-only wearable devices face a design challenge: to provide complete coverage and to avoid outliers. This is seen most clearly when the user runs or walks under an overpass (Figure 4). Familiar to urban joggers everywhere, the underpass allows the user to cross a busy road without needing to check for traffic, but requires the GNSS to reacquire the signals on the tunnel exit. See the GNSS track in Figure 5: when the device reacquires the signals, the position and speed accuracy suffers.
Figure 4. Position accuracy on reacquisition, emerging from overpass.Figure 5. GNSS speed accuracy on reacquisition.
Using the filtered GNSS and sensors, however (Figure 6), enables smooth tracking of speed and distance through the disturbance.
Figure 6. Sensors provide smooth speed estimate.
Urban Multipath. The pace analysis in Figure 7 shows a user instructed to run at a constant 8-minute/mile pace, stopping to cross the street where necessary. The red line on each plot shows the true pace profile. The commercial GNSS-only sportwatch on top shows frequent multipath artifacts, missing some of the stops and, worse for a runner, incorrectly showing erroneously high pace. The ultra-low-power chip captures all the stops and shows a constant running pace when not stopped.
Figure 7. Urban multipath tests.
It is well known in the community that regular sportwatches give unreliable speed and distance estimates in urban environments — where most organized running races are held! There’s nothing worse, as a runner, than to hear the distance beep from your watch going off earlier than expected: how demoralizing! The major benefit of this solution is that the speed estimate is much more reliable in the presence of multipath. At the same time, battery life can be extended because the GNSS is configured to use significantly less power.
fSpeed in existing solutions is computed in two different ways: indirectly from two consecutive, time-stamped GNSS position estimates, each derived from range measurements to the satellites, and directly from the Doppler frequency offset measurements to the satellites. Both range and frequency measurements are subject to significant error when the direct path to the satellite is blocked and a reflection is acquired.
The effects of multipath mean that the range error may in typical urban environments be hundreds of meters. The frequency error is also a function of the local geometry and is typically constrained by the magnitude of the user’s horizontal speed.
In either case, the GNSS device alone, in the presence of signal multipath, generates a velocity vector that fluctuates significantly, especially when there is a change in the satellites used or signal propagation path between the two consecutive positions. A variety of real-life cases generate this sudden fluctuation in velocity vector:
Running along a street in an urban canyon and turning a 90-degree corner.
Running along a pedestrian lane and taking a short road underpass.
Running under tree cover and suddenly arriving at an open area.
Running under an elevated highway and turning 90 degrees to a wide-open area.
In each case, the chips are using a certain set of satellites, and suddenly other, higher signal-strength satellites become available. A typical situation is for the position to be lagging the true position (while under tree cover, going through an underpass) and needing to catch up with the true position when arriving to the wide-open area. A jump in position is inevitable in that situation. This is not too bad for the GNSS track, but it will mean a noticeable peak in the speed values that is not accurate. Fitness applications save all of the computed speed values and generate a report for each workout. These reports are not accurate, especially the maximum speed values, for the reasons explained above.
Figure 8 describes a typical situation where the actual speed of the runner is approximately constant. GNSS fixes are computed regularly; however, the speed computed from subsequent GNSS fixes have sudden peaks that spoil the workout speed reports.
Figure 8. Sudden peaks spoil workout speed reports.
The new ultra-low-power solutions for wearables solve this problem by deriving speed and accumulated distance from the sensors running in the device. This avoids incorrect speed peaks, while still being responsive to true pace changes by the runner.
In running biomechanics, runners increase pace by increasing step cadence and/or increasing step length. Both methods depend on the runner’s training condition, technique, biomechanics, and so on. As a general rule, both step cadence and step length increase as the running speed increases from a jogging speed to a 1,500-meter race speed.
A runner may use one mechanism more than the other, depending on the moment or on the slope (uphill or downhill). In the case of male runners, the ratio of step length to height at a jogging speed is ~60 percent.The ratio of step length to height in a 1,500 meter race speed is ~100 percent. For female runners, the respective ratios are ~55 percent and ~90 percent.
The ultra-low-power chips take into account both mechanisms to derive the speed values. The sensor algorithms count the number of steps every time interval and translates the number of steps into distance multiplying by the step length. The reaction time of the GNSS chip to speed changes based on a higher cadence is immediate.
Speed changes due to longer steps are also measured by the ultra-low-power chips. The step length is constantly calibrated by the GNSS fixes when the estimated GNSS position error is low. The reaction time of the GNSS chip to speed changes based on longer steps has some delay, as it depends on the estimated error of the GNSS fixes.
Manufacturer
The ultra-low-power, high-accuracy, 40-nanometer single-die BCM4771 chip was designed by Broadcom Corporation. It is now being manufactured in production volumes and is focused on the wearables and IoT markets.It consumes five times less power than conventional GNSS chips (~10 mW) and needs 30 KBytes of memory in the MCU for the software driver. It features tight integration with the accelerometer and innovative GNSS tracking techniques for extremely accurate speed, accumulated distance, and GNSS tracking data.
Steve Malkos is an associate director of program management in the GPS Business Unit at Broadcom, responsible for defining GPS sensor hub and indoor positioning features. He has a B.S. in computer science from Purdue University, and currently holds eight patents,10 more pending, in location.
Manuel del Castillo is an associate director of marketing for Broadcom in the GNSS group. He has an MS in electronic engineering from the Polytechnic Universityand an MBA from the Instituto de Empresa, both in Madrid, Spain. He holds three patents in location with five more pending.
Steve Mole is a manager of software engineering for Broadcom in the GNSS group. He received his bachelor’s degree in physics and astrophysics from the University of Manchester.
Topcon Positioning Group said that its latest GNSS reference receiver, the NET-G5, is capable of tracking a new signal from the GLONASS constellation.
The GLONASS-M 55 satellite was launched in June 2014 and is equipped with the experimental payload capable of transmitting signals in the L3 frequency band. Engineers successfully tracked the signal with the NET-G5 receiver during a series of recent tests at the Topcon Technology Center in Moscow. The use of signals in L3 band alongside L1 and L2 bands is expected to further enhance the competitiveness of the GLONASS system.
“Topcon is committed to continually investing in research and development to offer end-users and the industry the most up-to-date solutions,” said Ivan Di Federico, chief strategy officer for Topcon Positioning Systems. “Our premier engineers, scientists and designers bring world’s first products and technologies to market, and the ability of the NET-G5 to track the latest signal — a first for the industry — is an excellent example of that dedication.”
Using Vanguard and Universal Tracking technologies, the NET-G5 receiver incorporates 452-channels capable of tracking the full GNSS signal spectrum, including modernized GPS, GLONASS, Galileo, BeiDou, QZSS and SBAS signals.
The GPS Reflections Group of University of Colorado-Boulder has been awarded the prestigious Prince Sultan Bin Abdulaziz International Creativity Prize for Water. The prize is awarded biannually to acknowledge innovative work that contributes to the sustainable availability of water and the alleviation of the global problem of water scarcity.
The awards will be presented in a ceremony in Riyadh, Saudi Arabia, on December 16, concurrently with the 6th International Conference on Water Resources and Arid Environments (ICWRAE 6), December 16-18, 2014.
Professors Kristine Larson and Eric Small developed a new method to measure water at the Earth’s surface. The research team discovered that standard geodetic GPS instruments are sensitive to hydrological influences. They subsequently developed a cost-effective technique, GPS Interferometric Reflectometry (GPS-IR), to measure soil moisture, snow depth, and vegetation water content around GPS antennas. GPS-IR has the advantage of relying on an existing GPS infrastructure installed by surveyors and geoscientists that covers an increasingly large portion of the global surface.
Larson and Small collaborated with scientists at the University Corporation for Atmospheric Research and the National Atmospheric and Oceanic Administration, also in Boulder.
The team uses the GPS-IR technique to analyze data streams from existing GPS networks in near real-time. Data from hundreds of operational GPS sites are downloaded and processed, yielding estimates of hydrologic variables within 24 hours.
Scientists and government agencies can access this information at the team’s web portal and use the data to improve monitoring and forecasting of hydrologic variables.
Telit Wireless Solutions has debuted the Jupiter SL871-S, designed for easy migration between a full-GNSS solution for top-ranked applications and a simple GPS-only solution for less demanding applications. The Jupiter SL871-S is designed to track and navigate GPS and QZSS constellations while ensuring pin-to-pin and protocol compatibility with its multi-constellation companion module, the SL871.
The module comes in a 9.7 x 10.1 millimeter LCC package with an ARM7 baseband processor, embedded ROM memory, and integrated LNA. It delivers geolocation data using NMEA protocol through a standard UART port. It supports ephemeris file injection (A-GPS) as well as Satellite Based Augmentation System (SBAS) for increased position accuracy.
In addition, its extremely low power consumption in all conditions is suited to applications requiring long battery life.
SL871-S has been designed to ensure hardware and software compatibility with SL871, allowing customers to design once and take advantage of the xL871 common form factor. Benefits include:
Pin-to-pin compatibility with SL871 family,
Same protocol used in SL871,
Straightforward migration between full-GNSS solutions and GPS-only solutions,
Satellite Based Augmentation System (SBAS) support for increased position accuracy, and
Assisted GPS.
The SL871-S can replace the SL871, allowing customers to design once and interchangeably mount the appropriate solution depending on the required features.
“The new SL871-S module designed to be easily swapped with other xL871 modules for enhanced simplicity and scalability,” said Taneli Tuurnala, CEO of Telit GNSS Solutions. “It is an ideal example of how buying a module from Telit enables our customers to avert the need to keep track of the latest chipset technology on their own. We keep them on top of the best available technology, pre-packaged in a module that is easy to replace as needed, without having to redesign their entire application to stay up to date.”
The new NEO-M8L Automotive Dead Reckoning (ADR) module by u-blox has integrated motion, direction and elevation sensors. The module integrates gyro and accelerometer with u‑blox’ GNSS platform u-blox M8 to achieve high indoor/outdoor positioning performance for road vehicle and high-accuracy navigation applications.
In addition to accessing the integrated module’s gyro and accelerometer data, accident reconstruction systems can provide the location of an accident to facilitate insurance claims even if a collision occurs in a tunnel or park house. High-end navigation devices are able to guide drivers through tunnels of several kilometers because of the accuracy of u-blox’ ADR system. Stolen vehicles can be located instantly due to continuous monitoring of sensor data and storage of location in non-volatile memory.
“Devices for usage-based insurance, stolen vehicle recovery, road pricing, fleet management, emergency services, and vehicle navigation depend on reliable, uninterrupted positioning including tunnels, park houses and stacked highways,” said Thomas Nigg, vice president of product strategy at u-blox. “The NEO-M8L is the ideal solution for all road vehicle based applications, able to calculate a position in all circumstances based on its own internal sensors, regardless of satellite visibility and end-device orientation.”
The NEO-M8L module will be demonstrated at the u-blox stand at electronica 2014 in Munich, November 11-14, Hall A4 Stand 219.
The compact module is 12.2 x 16.0 x 2.5 mm, requires minimum host integration resulting in no risk, is low cost, and provides fast time-to-market design, u-blox said. With uncritical orientation of the installed module, odometer function and autonomous data logging, it is an all-in-one solution for all road vehicle applications requiring reliable and uninterrupted position in challenging environments such as urban canyons, tunnels and underground parking.
The NEO-M8L embeds u-blox’ 3D Automotive Dead Reckoning (3D ADR) chip technology. Using the vehicle’s speed information and the module’s onboard sensors enables accurate positioning in three dimensions, even when satellite signals are completely lost and the end-device installation is not horizontal, u-blox said. An odometer function, based on the ADR technology, also provides accurate and continuous distance traveled.
The module is able to track all visible GNSS satellites including GPS, GLONASS, BeiDou, QZSS and all SBAS (European’s Galileo will be supported in a future firmware version). Concurrent reception of two GNSS systems is supported. The NEO-M8L module can output a position up to 20 times per second.
The module uses u‑blox’ M8 GNSS chip and is available in Professional Product grade. This grade includes qualification according to the ISO16750 standard “Road vehicles — Environmental conditions and testing for electrical and electronic equipment” and manufacturing in ISO/TS 16949 automotive-certified factories.
Samples and evaluation kits will be available in December 2014.
Photo: Mark L. Psiaki, Brady W. O’Hanlon, Steven P. Powell, Jahshan A. Bhatti, Todd E. Humphreys, and Andrew Schofield
Spoofing Detection with Two-Antenna Differential Carrier Phase
By Mark L. Psiaki, Brady W. O’Hanlon, Steven P. Powell, Jahshan A. Bhatti, Todd E. Humphreys, and Andrew Schofield
A new method detects spoofing attacks that are resistant to standard RAIM technique and can sense an attack in a fraction of a second without external aiding. The signal-in-space properties used to detect spoofing are the relationships of the signal arrival directions to the vector that points from one antenna to the other. A real-time implementation succeeded against live-signal spoofing attacks aboard a superyacht, the White Rose of Drachs shown above, cruising in international waters.
Concerns about spoofing of open-service GNSS signals inspired early work on simple receiver-autonomous integrity monitoring (RAIM) methods based on the consistency of the navigation solution. Work on new classes of defense techniques began in earnest after the demonstration of a powerful spoofer that is undetectfable by simple pseudorange-based RAIM methods. There has been a sense of urgency to solve the spoofing problem since the Iranians captured a classified U.S. drone in 2011 and made unsubstantiated claims to have spoofed its GPS. Two dramatic field demonstrations of the spoofer developed by author Humphreys and colleagues at the University of Texas, Austin, heightened interest in spoofing detection: one involved deception of a small airborne unmanned autonomous vehicle (UAV), causing it to dive towards the ground; another sent a superyacht off course without raising any alarms on its bridge.
One class of spoofing detection methods uses encrypted signals, their known relationships to the open-service signals, and after-the-fact availability of encryption information. Such techniques require a high-bandwidth communication link between the potential victim of a spoofing attack and a trusted source of after-the-fact encryption information, and may involve significant latency between attack and detection.
Another class of methods uses advanced RAIM-type techniques. Instead of considering only pseudorange consistency, these RAIM techniques examine additional signal characteristics such as absolute power levels, distortion of the PRN code correlation function along the early/late axis, the possible existence of multiple distinct correlation peaks in signal-acquisition-type calculations, and other signal or receiver characteristics. Such methods are relatively simple to implement because they do not require much additional hardware, if any, but some of these strategies can have trouble distinguishing between multipath and spoofing or between jamming and spoofing.
A third class proposes the addition of Navigation Message Authentication bits. These are encrypted parts of the low-rate navigation data message. Such techniques require modification of the navigation data message and can allow long latencies between the onset of a spoofing attack and its detection.
A fourth class exploits the differing signal-in-space geometry of spoofed signals in comparison to true GNSS signals. All spoofed signals typically arrive from the same direction, but true signals arrive from a multiplicity of directions. Some of these methods use receiver antenna motion to achieve direction-of-arrival sensitivity. Others use an array of two or more receiver antennas.
The most powerful of these detection strategies exploit models of the effects on carrier-phase data of antenna motion or antenna-array geometry. This knowledge may be partial because an unknown antenna-array attitude may need to be determined as part of the detection calculation. Their power derives from the high degree of accuracy with which a typical GNSS receiver can measure beat carrier phase.
Goals. This research follows on moving-antenna/carrier-phase-based spoofing detection work. One of our goals has been to remove the necessity for moving parts by using two antennas and processing their carrier-phase data.
A second goal has been to achieve real-time operation. An earlier prototype moving-antenna system (see “GNSS Spoofing Detection,” GPS World, June 2013) used post-processing and completed its spoofing detection calculations days or weeks after the recording of wide-band RF data during live-signal attacks.
A third goal has been to test this system against actual live-signal spoofing attacks to prove its real-time capabilities and evaluate its performance during the two phases of an attack: the initial signal capture and the post-capture drag-off to erroneous position and timing fixes.
Two-Antenna System Architecture
The system consists of two GNSS patch antennas, GPS receiver hardware and software, and spoofing detection signal-processing hardware and software. Figure 1 shows two versions. The left-hand version connects its two patch antennas to an RF switch. The single analog RF output of the switch is input to a GNSS receiver that is standard in all respects, except for two features. First, it controls the RF switch or, at least, has access to the switching times. Second, it employs a specialized phase-locked loop (PLL) that can track the beat carrier phase of a given signal through the phase jumps that occur at the switching times. The right-hand version connects each antenna to an independent GPS receiver, likely connected to a common reference oscillator.
Figure 1. Two configurations:, the RF-switched-signal/single-receiver configuration (left) and the two-receiver configuration (right).
The last element of each system is a spoofing detection signal-processing unit. Its inputs are the single-differenced beat carrier phases of all tracked signals, with differences taken between the two antennas. In the switched antenna system, each difference is deduced by the specialized PLL. In the two-receiver system, the single-differences are calculated explicitly from each receiver’s beat carrier-phase observables.
Except for the final spoofing detection unit, the two-receiver system on the right-hand side of Figure 1 is already available commercially. Typical applications are CDGPS-based attitude/heading determination. Thus, this is the easiest version to implement.
This system could include more than two antennas. A multi-antenna system could have a dedicated RF front-end and a dedicated set of receiver channels for each antenna, as on the right of Figure 1. Alternatively, a multi-antenna system could include an RF switch between any one of the multiple antennas at the command of the receiver. The latter design would entail a slight modification to the specialized PLL to track multiple independent phase jumps for the independent antenna switches.
Principles. The principles used to detect spoofing can be understood by considering and comparing the signal-in-space and antenna geometries shown in Figure 2, the two-antenna system and three GNSS satellites for a typical non-spoofed case, and Figure 3, a spoofed case. The salient difference is that the different GNSS signals arrive from different directions for the non-spoofed case, namely and . They all arrive from the same direction, the direction of the spoofer , for the spoofed case. For detection purposes, the important geometric feature is the projection of each direction of arrival onto the known separation vector between the two antennas, bBA. This projection has a direct effect on the beat carrier-phase difference between the two antennas. In the non-spoofed case, this effect will vary between the different received signals in ways consistent with the attitude of the vector. In the spoofed case, all of these carrier-phase differences will be identical. The spoofing detection algorithm decides between two hypotheses about the carrier-phase differences, one conjecturing a diversity consistent with authentic signals and the other conjecturing the sameness that is characteristic of spoofed signals.
Figure 2. Geometry of two-antenna spoofing detection system and GNSS satellites for non-spoofed case.Figure 3. Spoofed-case geometry of two-antenna spoofing detection system and GNSS spoofer.
Hypothesis Test
The PDF paper on which this article is based presents the non-spoofed and spoofed signal models that form the basis of a hypothesis test, develops optimal estimation algorithms that fit the observed differential beat carrier phases to the two models, and shows how these estimates and their associated fit error costs can be used to develop a sensible spoofing detection hypothesis test. Download the PDF here.
Offline and Live-Signal Testing
We tested a prototype version of the two-antenna system as depicted on the righthand side of Figure 1. The antennas connect to two independent RF front-ends that run off of the same reference oscillator. These RF front-ends provide input to two independent receivers that track each signal using a delay-lock loop (DLL) and a PLL. Figures 4 and 5 show system elements: two GPS patch antennas mounted on a single ground plane with a spacing of 0.14 meters, two RF front-ends — universal software radio peripherals (USRPs) — with a common ovenized crystal oscillator. Digital signal-processing functions are implemented in real-time software radio receivers (SWRX) running in parallel on a Linux laptop, written in C++. Spoofing detection calculations are performed on the same laptop using algorithms encoded in Matlab.
Figure 4. The two antennas of the prototype spoofing detection system mounted on a common ground plane.Figure 5. Signal processing hardware of the prototype spoofing detection system.
A key feature of this architecture is the ability of its real-time software radios’ C++ code to call the spoofing detector’s Matlab tic function and to pass carrier-phase and other relevant data to the tic function. This feature served to shorten the implementation and test cycle for the prototype system by eliminating the need to translate the original Matlab versions of the spoofing detection algorithms into C++. This enabled rapid re-tuning and redesign of the spoofing detection calculations, exploited during the course of live-signal testing.
The Matlab package displays real-time signal authentication information. Figure 6 shows the version of the display used for this study’s culminating live-signal tests. All displays are updated in real time. The upper left, upper right, and lower left plots scroll along their horizontal time axes to keep the most recent 4.5 minutes of data available. The lower right compass updates each time a new spoofing detection calculation is performed. The green dots in the upper left plot indicate that the time between spoofing detections, Δtspf , is nominally 1 second, though sometimes the gap is longer due to lack of a sufficient number of validated single-differenced carrier phases to carry out the calculation. Thus, the nominal update time for all of the plots in this display is 1 second. Faster updates are possible with the Matlab software, but Δtspf was deemed sufficiently fast for this study’s experiments.
The most important panel in Figure 6 is the upper left spoofing detection statistic time history. The magenta plus signs on the plot show the spoofing detection threshold chosen for this case, γth. The computed γ values are plotted as green o’s if they lie above γth and as red asterisks if they lie below. If γ is above γth, the message “GPS Signals Authenticated” is displayed on the plot; if below, the message switches to the spoofing alert: “GPS SPOOFING ATTACK DETECTED!”
Figure 6. Spoofing detector real-time display. Clockwise from top left: the spoofing detection statistic time history γ(t); four diagnostic time histories that include time histories of the number of satellites used for spoofing detection L(t) (blue asterisks), their corresponding GDOP(t) values (magenta o’s), the time increment between spoofing detection tests Δtspf(t) (green dots), and the compass heading ψ(t) as determined from the two-antenna non-spoofed-case solution (black dots); Compass display; and time history of GPS PRN number availability.
The other three panels proved helpful in diagnosing system performance. A low L value (near 4) or a high GDOP value in the upper right panel indicated poorer reliability of the spoofing detection calculations. A correct compass heading in the absence of spoofing provided a check on the system. During spoofing attacks, the compass heading became jumpy, thereby providing another possible indicator of inauthentic signals.
The vertical scale of the lower left panel lists the possible GPS PRN numbers. The presence of a green or red dot at the level corresponding to a given PRN number indicates that one or both receivers is seeing something from that satellite at the corresponding time. If the dot is red, then the returned data are incomplete or are deemed to be insufficiently validated for use in the spoofing detection calculation. If the dot is green, then the data from that PRN have been used in the detection that has been carried out at that time.
Another feature of the prototype spoofing detection system is its ability to record the wide-band RF data from its two antennas. For each spoofing scenario, the raw samples from both USRPs were recorded while the real-time software receiver was performing its signal-processing operations and while the real-time spoofing detector was doing its calculations. These recorded data streams will allow off-line analysis and testing of a re-tuned or completely redesigned spoofing detection system.
Red Team Receiver/Spoofer. The UT Austin spoofer’s attack strategy overlays the spoofed signal on top of the true signals, ramps up the power to capture the receiver tracking loops, and finally drags the pseudorange, beat carrier phase, and carrier Doppler shift off from their true values to spoofed values. Figure 7 shows the pseudorange part of a spoofing attack: cross-correlation of the receiver’s PRN code replica with the total received signal (blue solid curve); the receiver’s early, prompt, and late correlations (red dots); and the spoofer signal (black dash-dotted curve). In the top plot, the spoofer has zero power, and the receiver sees only the true signal. The second and third plots show the spoofer ramping up its power while maintaining its false signal in alignment with the true signal. The spoofer power in the middle/third plot is sufficient to capture control of the three red dots of the receiver’s DLL. In the fourth and fifth plots, the spoofer initiates and continues a pseudorange drag-off, an intentional falsification of the pseudorange as measured by the victim receiver’s DLL.
Figure 7. Receiver/spoofer attack sequence as viewed from a channel’s code offset cross-correlation function. Spoofer signal: black dash-dotted curve; sum of spoofer and true signals: blue solid curve; receiver early, prompt, and late correlation points: red dots.
The spoofer performs drag-off simultaneously on all spoofed channels in a vector spoofing attack that maintains consistency of all spoofed pseudoranges. After the initiation of drag-off, the victim receiver computes a wrong position, a wrong true time, or both, but the residual pseudorange errors in its navigation solution remain small. Therefore, this type of attack is not detectable by traditional pseudorange-based RAIM calculations.
The receiver spoofer hardware consists of a GNSS reception antenna, the receiver spoofer signal-processing unit, and the spoofer transmission antenna (Figure 8).
Figure 8a. Receiver/spoofer hardware: GPS reception antenna on ship’s rear upper deck.Figure 8b. Receiver/spoofer hardware: directional transmission antenna pointed at the ship’s GPS antenna and the detector antenna pair near the defended ship’s antenna. The orientation of the spoofing transmission antenna, combined with its remote location from the receiver/spoofer’s reception antenna, ensured that the spoofer did not self-spoof.Figure 8c. Receiver/spoofer hardware: spoofer electronics, located amidships.
The receiver/spoofer requires tuning of its transmission power levels. If the power is too high, its spoofing attacks will be too obvious. A very high transmitted power could also saturate the front-end electronics of the intended victim, causing it to jam the system rather than spoof it. If transmitted power is too low, it will not capture the victim’s tracking loops, and its spoofing attack will fail. The proper power level depends on the gain patterns of the spoofer transmission antenna and the victim receiver antenna and on their relative geometry.
Attack Test Scenarios. Three sets of tests were conducted to develop and evaluate the spoofing detection system. The first tests started by recording wideband RF GPS L1 data using USRPs. These data were post-processed in two software receivers that recorded the outputs of their signal tracking loops. Afterwards, the Matlab spoofing detection calculations were run using the recorded tracking loop data as inputs. These preliminary tests at Cornell and Austin proved the efficacy of the spoofing detection algorithms. They did not, however, test system performance during the transition from non-spoofed to spoofed signals that takes place at the initiation of a spoofing attack.
The second set of tests was carried out using the first real-time version of the system, after the Matlab spoofing detection calculations were repackaged into a tic function and linked to the C++ real-time software receivers. This set of tests also was unable to probe the system’s performance at the onset of a spoofing attack, before the signal drag-off.
The final set of tests was conducted aboard the White Rose of Drachs in the Mediterranean’s international waters.
The power adjustment tests on June 27 needed a means to decide whether a given attack had captured the tracking loops of the ship’s GPS receiver. The strategy for confirming capture was to perform a noticeable drag-off after the initial attack. We settled on a vertical drag-off as providing the most obvious indication of a successful capture. Successful attacks dragged the receiver’s reported altitude as high as 5,000 meters.
The tests that evaluated spoofer and spoofing detector antenna placements relative to the ship’s GPS antenna were also important to achieving sensible results. Various placements were tried. The most successful relative geometry is depicted in Figure 8.
The placement of the detector antennas relative to the defended antenna is atypical of likely real-world detection scenarios. It is expected that a real-world spoofing detector will be integral with the defended GNSS receiver.
The culminating live-signal attack involved a 50-minute spoofing scenario in which the attacker took the ship — apparently — from the Adriatic to the coast off of Libya. The scenario’s long distance and short duration required a mid-course speed in excess of 900 knots. This spoofing scenario was designed in the simplest possible way, by taking a straight-line course in WGS-84 Cartesian coordinates from the true location to the spoofed location off of Libya. This course took the spoofed yacht position across the Italian and Sicilian land masses and below the Earth’s surface to a maximum depth of more than 23 kilometers.
Obviously, the White Rose was physically unable to execute this maneuver. Its crew would not have needed spoofing detection to realize that its GPS receiver was returning false readings. The main points of this last test were to dramatize the potential errors that can be caused by a spoofer and to check whether the spoofing detector could continue to function under these drastic conditions.
Figure 9 highlights this unusual scenario with two displays from the ship’s bridge, photographed during the attack. The GPS display shows the speed, 621 kn (knots), and the altitude, 7376 m. The chart display shows the yacht on (or rather, below) dry land and halfway across the “insole” of Italy’s boot. It also shows a tremendously long velocity vector, extending beyond the chart.
Figure 9a. The ship’s bridge GPS receiver display during the Libya spoofing scenario.Figure 9b. The GPS-driven chart during the Libya spoofing scenario.
Spoofing Detection Test Results
Various signal output time histories (Figure 10) illustrate the attack sequence and suggest means to evaluate the spoofing detection system. The upper panel plots the fractional portions of the two-antenna spoofing detector’s single-differenced beat carrier-phase time histories, Δϕ1BA, …, ΔϕLBA for the L = 7 tracked PRN numbers 16, 18, 21, 22, 27, 29, and 31. The middle panel plots the amplitude time history of the 100 Hz prompt [I;Q] accumulation vector for PRN 16, as received at Antenna A of the detection system. The bottom panel plots the PRN 16 carrier Doppler shift time history.
Figure 10. Indicators of initial capture and drag-off during Libya spoofing attack, as measured by the spoofing detection receiver.
This was a strong attack in which the spoofer power was 10.7 dB higher than the power of the real signal for PRN 16. The other spoofed signals had power advantages over their corresponding true signals that ranged from 3.3 dB to 13.6 dB, and the spoofer’s mean power advantage was 10.4 dB. Therefore, the onset of the spoofing attack at 196.1 sec is clearly indicated by the sudden jump in (I2+Q2)0.5 on the middle panel. The upper panel shows a corresponding sudden coalescing of the single-differenced beat carrier phases, which implies that the spoofing detection algorithm should have been able to detect this attack.
The spoofer drag-off started at 321.5 sec, as evidenced by the sudden change in the slope of the carrier Doppler shift time history on the lower panel. The period after the initial attack and before the drag-off is delimited by the vertical magenta and cyan dash-dotted lines. During this interval the spoofer waited to capture the receiver’s tracking loops.
The single-differenced phase time histories in the upper plot appear somewhat noisier during the interim pre-drag-off period of the attack than after the start of the drag-off at 321.5 sec. The grey dotted curve for PRN 27 is an exception because it becomes noisy again starting at about 450 sec due to decreased signal power. The increased noisiness of the differential phase time histories during the interim period is probably the result of interference between the true and spoofed signals, which are likely beating slowly against each other. The response of the spoofing detection algorithm during this phase is uncertain because this multipath-like beating between the two signals is not modeled.
Figure 11 demonstrates performance of the spoofing detection algorithm for the Libya attack scenario. The upper panel of the figures is a repeat of the upper panel of the single-differenced beat carrier-phase time histories from Figure 10, except that they are plotted for a longer duration. The lower panel shows the γ(t) spoofing detection statistic time history. It plots the same information that appeared in the upper left panel of Figure 6 during the corresponding real-time detection tests. At 196 sec γ(t) is clearly above the blue dash-dotted spoofing detection threshold γth. At 196.4 sec it is clearly below γth , which indicates a spoofing detection. It remains below γth for the duration of the attack. In this reprocessed version of the detection calculations, γ(t) has been updated at 5 Hz. Therefore, the earliest possible detection point would have been 196.2 sec, which is 0.1 sec after the onset of the attack. This point corresponds to the green dot in the lower panel of Figure 11 that lies slightly above the blue dash-dotted γth line. Theoretically, the system might have detected the attack at this time, but the finite bandwidth of the two receivers’ PLLs caused lags in the transitions of the single-differenced phases in the top plot, which led to the 0.3 sec lag in the detection of the attack. It is encouraging, however, that the spoofing detector worked well during the initial pre-drag-off phase of the attack, from 196.1 to 321.5 sec, despite the added noisiness of the single-differenced carrier phases in the top plot, likely caused by beating between the true and spoofed signals.
Figure 11. Single-differenced carrier-phase time histories (top plot) and corresponding spoofing detection statistic time history (bottom plot) for Libya spoofing attack scenario.
Figure 12 plots the same quantities as in Figure 11, but for a different spoofing attack, a little less overt than the Libya attack. The power advantage of the spoofer ranged from 3.0 to 14.0 dB for the different channels with a mean power advantage = 9.2 dB. It was detected by the system, as evidenced by the convergence of the single-differenced carrier phases at the onset of the attack at 397.5 sec. The spoofing detection statistic in the bottom panel dives near to the γth detection threshold at the onset of the attack and sometimes passes below it, but it does not stay permanently below the threshold until after the time of drag-off, after 531 sec.
Figure 12. Single-differenced carrier phase time histories (top plot) and spoofing detection statistic time history (bottom plot) for a spoofing attack with a slightly lower power advantage than the Libya attack.
The large oscillations of the single-differenced carrier phases during the pre-drag-off initial capture interval from 397.5 to 531 seconds is likely due to beating between the true and spoofed signals. The largest variations occur for PRNs 12 and 31, which are the ones with the lowest spoofer power advantages, 3.2 and 3.0 dB, respectively. Apparently these oscillations cause γ(t) sometimes to take on values slightly above γth during the interval 397.5 sec < t < 531 sec. Thus, the spoofing detector can experience problems in the initial phases of an attack.
Note that the spoofer failed to capture the tracking loops of the ship’s GPS receiver. This is surprising, given the average spoofer power advantage of 9.2 dB above the true signals. We conjecture that the ship’s GPS antenna had lower gain in the low-elevation direction toward the spoofer transmission antenna than did the detector’s antennas. A lower gain would reduce the spoofer power advantage in the ship’s receiver and could explain why the spoofer failed to deceive it.
Many additional spoofing attacks were carried out aboard the ship. The spoofing detector proved finicky. It took quite some time to get the spoofing detection two-antenna system positioned in a sensible place relative to the ship’s GPS antenna so as to be sensitive to nearly the same spoofing signals. In addition, the spoofing detector’s GPS receiver tended to lose lock at the initiation of an attack, prior to signal drag-off. This was likely caused by the large power swings of the received signals due to beating of the true signals against the spoofed signals. This problem went away at higher spoofer power levels. When lock was lost, the software receiver would attempt to re-acquire the signal. Often a reacquisition would succeed only after signal drag-off by the spoofer. Typically, the spoofing detector immediately detected the attack once it had reacquired the spoofed signals that were no longer beating against the true signals due to having been dragged sufficiently far away from them, as in Figure 7. Re-analysis of the recorded data indicated that poor PLL tuning may have caused the losses of lock during the initial attacks. Spoofing detection calculations carried out on the reprocessed data have proved more reliable when implemented with a better PLL tuning.
Two attacks were carried out with only a subset of the visible GPS satellites being spoofed. The first involved spoofing 7 of 9 visible satellites, and the second test spoofed only 4 of 9. The spoofing detection system had trouble maintaining signal lock during the initial part of the first attack. It subsequently reacquired signals and was able to detect the attack successfully after reacquisition. The first attack also succeeded in capturing the ship receiver’s tracking loops as evidenced by spoofing of the yacht to climb off the sea surface. The second attack, with only four spoofed satellites, was not detected by the prototype system, but it succeeded in deceiving the ship’s GPS receiver about its altitude. This latter result indicates a need to modify the detection calculations to allow for the possibility of partial spoofing. In their current form, they assume that all signals are either spoofed or authentic. Of course, in the partial spoofing case it may also be possible to use traditional pseudorange-based RAIM techniques to detect an attack.
Improved detection during pre-drag-off initial phase of attack;
Detection when only a subset of signals are spoofed;
Advanced RAIM techniques;
A real-time prototype of the switched-antenna version;
Detection of a spoofer that uses multiple transmission antennas;
Reacquisition of true signals to recover from a spoofing attack.
Conclusions
A new prototype GNSS spoofing detection system has been developed and tested using live-signal spoofing attacks. The system detects spoofing by using differences in signal direction-of-arrival characteristics between the spoofed and non-spoofed cases as sensed by a pair of GNSS antennas. A spoofing detection statistic has been developed that equals the difference between the optimized values of the negative-log-likelihood cost functions for two data-fitting problems. One problem fits the single-differenced beat carrier phases of multiple received signals to a spoofed model in which the fractional parts of these differences are identical -— in the absence of receiver noise — because the spoofed signals all arrive from the same direction. The other problem fits the single-differenced carrier phases to a non-spoofed model. This second optimal data-fitting problem is closely related to CDGPS attitude determination. The simple difference of the two optimized cost functions equals a large positive number if there is no spoofing, but it equals a negative number if the signals are being spoofed. Monte Carlo analysis of the probability distributions of this difference under the spoofed and non-spoofed assumptions indicates that it provides a powerful spoofing detection test with a low probability of false alarm.
A real-time version of this system has been implemented using USRPs and real-time software radio receivers, and it has been tested against live-signal spoofing attacks aboard a yacht that was cruising around Italy. Successful detections have been achieved in many spoofing attack scenarios, and detections can occur in as little as 0.4 seconds or less. One scenario spoofed the yacht’s GPS receiver into believing that it had veered off of a northwesterly course towards Venice in the Adriatic to a southwesterly course towards the coast of Libya, and at the incredible speed of 900 knots. The spoofing detector, however, warned the crew on the bridge about the attack before the yacht’s spoofed position was 50 meters away from its true position.
The live-signal tests revealed some challenges for this spoofing detection strategy. They occur primarily during the initial attack phase, before the spoofer has dragged the victim receiver to a wrong position or timing fix. If the spoofer power is not much larger than that of the true signals, then beating occurs between the spoofed and true signals during this initial period. This beating can cause difficulties for the receiver tracking loops, making single-differenced carrier phase unavailable. Even when single-differenced phase is available, both the spoofed and non-spoofed models of this quantity can be inadequate for purposes of designing a reliable spoofing detection test.
This article’s new two-antenna spoofing detection system has generated promising real-time results against live-signal spoofing attacks, but further developments are needed to produce a sufficiently reliable detection system for all anticipated attack scenarios. The best defense will likely employ a multi-layered approach that uses the techniques described in this paper along with advanced RAIM techniques that detect additional signal anomalies that are characteristic of spoofing.
Acknowledgments
The authors (brief bios given in online version) thank the owner of the White Rose of Drachs for the loan of his vessel to conduct the live-signal GNSS spoofing detection tests reported here. The crew of the White Rose aided and supported this project in many ways.
Red Team, White Team, Blue Team
Background
Before March 2013, members of the UT Radionavigation Lab and the Cornell GPS Lab didn’t know about gold-plated sinks and spiral staircases at sea. They did know something about spoofing navigation systems and detecting spoofer attacks. The UT group had hacked a helicopter drone at White Sands Missile Range in June 2012, coaxing it to dive towards the ground. The Cornell group had developed a prototype system that could reliably detect all UT Austin attacks, but it was clumsy, having an oscillating antenna and requiring hours of post-processing.
Andrew Schofield, master of the White Rose of Drachs, attended Todd Humphreys’ 2013 South-by-Southwest conference talk on the drone hack and challenged him to go big — bigger than a 1.3-meter drone helicopter. How about a 65-meter superyacht? The result: a summer 2013 Mediterranean cruise that produced intriguing, provocative results.
The UT team had implemented a feedback controller for their spoofer, but they were unable to control the spoofed drone in a smooth, reliable manner. The White Rose cruise offered a chance to test a next level of sophistication: a controlled sequence of lies leading the victim on a precise course selected by the spoofer, different from the one intended by the captain.
The UT team was able to induce inadvertent turns while the ship’s bridge thought it was steering a straight course. They could nudge the yacht onto a wrong course paralleling the desired course. The crew remained unaware of the yacht’s true course because its GPS receiver and GPS-driven charts indicated that she was on her intended route.
The Push for Protection
Andrew Schofield quickly began advocating for a follow-up experiment: a UT Red Team attack against the White Rose GPS and a simultaneous Cornell Blue Team demonstration of real-time spoofing detection.
The Cornell Team, however, faced challenges in transitioning from its initial prototype to a more sophisticated system, one that eliminated the moving parts and that operated in real time. Team members thought they could produce the next system, but had never been quite sure they could make good on their boast.
Development of a second prototype system began with implementation of a new Cornell detection algorithm in Matlab. The first tests of this algorithm involved UT recording and pre-processing of transmissions in an RF chamber that housed the two antennas of Cornell’s second prototype. Cornell applied its new Matlab algorithm to these data and demonstrated off-line spoofing detection.
The remaining hurdle was real-time operation. The original development plan called for translation of the Matlab algorithm to C++ followed by integration with a UT Austin/Cornell real-time software radio. It would be understatement to say that this was an ambitious task for the two-month window that remained until the White Rose cruise.
UT Ph.D. student Jahshan Bhatti steered the team around this hurdle by proposing the direct use of Cornell’s Matlab code in the real-time system. Prior to this, no one had realized that it could be practical to call Matlab from C++ in real time. Mark Psiaki packaged the Matlab spoofing detection software into a single tic function, Jahshan coded the calling C++/Matlab interface, and the team was on track to test spoofing detection in late June 2014.
Spoofer, Detector Clash at Sea
The White Rose would sail from southern France on June 26, setting a course around Italy to Venice. The Cornell Blue Team would have three full days in international waters to demonstrate and evaluate their real-time spoofng detection system. A Ph.D. graduate from UT’s Radionavigation Laboratory would operate the Red Team spoofer, aka the Texas Lying Machine.
In preparation for the voyage, the two teams converged in the White Roses’s home port of Cap-d’Ail. They performed initial shake-down tests of their systems in port. They could not do full live-signal tests in Cap d’Ail because they were still in French territorial waters. Transmission of live spoofing signals in the GPS L1 band is permitted only in international waters, and only if conducted for scientific purposes.
The spoofing and detection tests started in earnest on the morning of June 27 off the southern coast of Italy. The White Rose had passed through the Strait of Messina between Italy and Sicily earlier that day. The initial tests were concerned with antenna geometries and spoofer power levels. Later tests concentrated on serious deception of the White Rose regarding its true course and location.
During the tests, the UT Red team and its spoofer were situated on the White Rose Sun Deck, above and behind the bridge. The Cornell Blue team and its electronics were on the bridge with its two antennas on the roof. A walkie-talkie link between the teams provided coordination of detector operation with spoofing attacks along with feedback about spoofer and detector performance.
Hijacked to Libya!
For the final day of tests, Andrew Schofield suggested sending the spoofed White Rose to Libya as she cruised the Adriatic from Montenegro to Venice — a difference of 600 nautical miles. The target trip time of 50 minutes necessitated a peak speed over 900 knots (1,667 kilometers/hour) after factoring the need to limit initial acceleration and final deceleration; if too large, they might cause the victim receiver’s tracking loops to lose lock and, therefore, the spoofed signals.
The Cornell and UT Austin teams programmed the spoofer for a trip to Libya, and they initiated the attack. The White Rose bridge soon became a scene of excitement. The ship started veering sharply to port, and its velocity vector lengthened until it literally went off the charts. The GPS receiver showed the ship hurrying towards Libya on a collision course with the back of Italy’s boot. The bridge’s GPS receiver displayed speeds that increased through 100 knots, 200 knots, 300 knots — for a yacht with a speed capability of about 15 knots.
The Cornell detector issued a spoofing alert at the onset of the attack, long before the White Rose veered off course. After a few minutes, the detector’s continued successful operation became boring. Of course, boring success is better than exciting failure.
The Cornell system had not been as successful during some of the preceding attacks, and the results from the June voyage suggested avenues for improvement. If new live-signal tests become necessary to evaluate planned improvements, the Red and Blue teams stand ready for a future superyacht cruise.
Mark L. Psiaki is a Professor of Mechanical and Aerospace Engineering. He received a B.A. in Physics and M.A. and Ph.D. degrees in Mechanical and Aerospace Engineering from Princeton University. His research interests are in the areas of GNSS technology and applications, spacecraft attitude and orbit determination, and general estimation, filtering, and detection.
Brady W. O’Hanlon is a graduate student in the School of Electrical and Computer Engineering. He received a B.S. in Electrical and Computer Engineering from Cornell University. His interests are in the areas of GNSS technology and applications, GNSS security, and space weather.
Steven P. Powell is a Senior Engineer with the GPS and Ionospheric Studies Research Group in the Department of Electrical and Computer Engineering at Cornell University. He has M.S. and B.S. degrees in Electrical Engineering from Cornell University. He has been involved with the design, fabrication, testing, and launch activities of many scientific experiments that have flown on high altitude balloons, sounding rockets, and small satellites. He has designed ground-based and space-based custom GPS receiving systems primarily for scientific applications.
Jahshan A. Bhatti is pursuing a Ph.D. in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin, where he also received his M.S. and B.S. He is a member of the UT Radionavigation Laboratory. His research interests are in the development of small satellites, software-defined radio applications, space weather, and GNSS security and integrity.
Todd E. Humphreys is an assistant professor in the department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin, and Director of the UT Radionavigation Laboratory. He received a B.S. and M.S. in Electrical and Computer Engineering from Utah State University and a Ph.D. in Aerospace Engineering from Cornell University. He specializes in applying optimal estimation and signal processing techniques to problems in radionavigation. His recent focus is on radionavigation robustness and security.
Andrew Schofield is a career Yacht Captain. After completing his degree in Applied Biology and working in the bio-science industry for a year, he left all that behind in 1991 and found a deck hand’s job on a sailing yacht in the Caribbean. Since then he has worked on various yachts in various locations. He has been Captain of the White Rose of Drachs since launch in June 2004. He is President of the Professional Yachting Association, the large yacht professional body, and focuses on the training and certification of crew. In his time at sea GPS has transformed navigation. He feels that the relevance of the work done to detect GPS spoofing cannot be overstated with regard to the safety of life at sea, and he is delighted to have facilitated the voyage during which spoofing detection was proven.
Testing the Feasibility of Positioning Using Ambient Light
By Jingbin Liu, Ruizhi Chen, Yuwei Chen, Jian Tang, and Juha Hyyppä
INNOVATION INSIGHTS by Richard Langley
AND THEN THERE WAS LIGHT. Well, the whole electromagnetic (EM) spectrum, actually. Visible light occupies only a small portion of the spectrum, which extends from below the extremely low frequency (ELF) 3 to 30 hertz band with equivalent wavelengths of 100,000 to 10,000 kilometers through infrared, visible, and ultraviolet light and x-rays to gamma rays in the 30 to 300 exahertz band (an exahertz is 1018 hertz) with wavelengths of 10 to 1 picometers and beyond. The radio part of the spectrum extends to frequencies of about 300 gigahertz or so, but the distinction between millimeter radio waves and long infrared light waves is a little blurry.
Natural processes can generate electromagnetic radiation in virtually every part of the spectrum. For example, lightning produces ELF radio waves, and the black hole at the center of our Milky Way Galaxy produces gamma rays. And various mechanical processes can be used to generate and detect EM radiation for different purposes from ELF waves for communication tests with submerged submarines to gamma rays for diagnostic imaging in nuclear medicine.
Various parts of the EM spectrum have been used for navigation systems over the years. For example, the Omega system used eight powerful terrestrial beacons transmitting signals in the range of 10 to 14 kilohertz permitting global navigation on land, in the air, and at sea. At the other end of the spectrum, researchers have explored the feasibility of determining spacecraft time and position using x-rays generated by pulsars — rapidly rotating neutron stars that generate pulses of EM radiation.
But the oldest navigation aids, lighthouses, used the visible part of the EM spectrum. The first lighthouses were likely constructed by the ancient Greeks sometime before the third century B.C. The famous Pharos of Alexandria dates from that era. And before the construction of lighthouses, mariners used fires built on hilltops to help them navigate. The Greeks also navigated using the light from stars, or celestial navigation. Records go back to Homer’s Odyssey where we read “Calypso, the lovely goddess had told him to keep that constellation [the Great Bear] to port as he crossed the waters.” By around 1500 A.D., the astrolabe and the cross-staff had been developed sufficiently that they could be used to measure the altitudes of the sun or stars to determine latitude at sea. Celestial navigation was further advanced with the introduction of the quadrant and then the sextant. And determining longitude was possible by observing the moons of Jupiter (but not easily done at sea), measuring distances between the moon and other celestial bodies and, once it was developed, using a chronometer to time altitude observations.
How else is light used for positioning and navigation? Early in the space age, satellites were launched with flashing beacons or with large surface areas to reflect sunlight so that they could be photographed from the ground against background stars with known positions to determine the location of the camera. We also have laser ranging to satellites and the moon and the related terrestrial LiDAR technology, as well as the total stations used by surveyors. And in this month’s column, we take a look at the simple, innovative method of light fingerprinting: the use of observations of the artificial light emitted by unmodified light fixtures as well as the natural light that passes through windows and doorways in a technique for position determination inside buildings.
“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas.
Over the years, various localization technologies have been used to determine locations of people and devices in an absolute or relative sense. Relative positioning methods determine a location relative to another one in a local coordinate framework, while absolute positioning techniques fix an absolute location in a specific coordinate framework.
In the past, people observed the positions (orientation angles) of a celestial body (such as the sun, the moon, or a star) to determine their locations on the Earth, which is known as celestial navigation (see FIGURE 1). The locations are resolved by relating a measured angle between the celestial body and the visible horizon to the Nautical Almanac, which is a knowledge base containing the coordinates of navigational celestial bodies and other relevant data. Other than an observation device, celestial navigation does not rely on any infrastructure, and hence it can be used virtually anywhere on the globe at anytime, weather permitting. Nowadays, an increasing number of applications, location-based services, and ambient intelligence largely require positioning functions across various environments due to increasing mobility of people and devices. In particular, the development of robotics for a number of purposes requires the support of localization capability in various conditions where positioning infrastructure may be missing.
Various positioning technologies share an intrinsic characteristic that a positioning solution is resolved by using the dependency between spatial locations and a set of physical observables. The dependency may be expressed in the form of either a deterministic function model or a probabilistic model. A deterministic model expresses the dependency between locations and observables in a closed-form function, while a probabilistic model defines the dependency between locations and observables in the Bayesian sense. Depending on the form of dependency, different mathematical models have been used for position resolution.
For example, satellite-based GNSS positioning derives the location of a user’s receiver based on radio frequency (RF) signals transmitted by the satellite systems. GNSS positioning is grounded in accurate time determination: the time differences between the transmitted and the received radio signals denote signal travel times (observables), which are then converted into distance measurements between the satellite and the user antenna. Using the distance measurements between the user antenna and four different satellites, the receiver can obtain three-dimensional receiver coordinates in a global reference frame and the time difference between the receiver and satellite clocks. The dependency between user location and a set of distance observables can be expressed in a simplified equation:
(1)
where ρi is an observed range between the ith satellite and the receiver, (x,y,z)i is the position of the ith satellite, (x,y,z) is the position of the receiver to be estimated, γ denotes errors in the range observable, δt and c are receiver clock error and the speed of light, respectively (the sign of the clock term is arbitrary, but must be used consistently).
It is obvious that GNSS positioning relies strongly on the visibility of the GNSS constellation — the space infrastructure — as it requires line-of-sight visibility of four or more satellites. The positioning capability is degraded or totally unavailable in signal-blocked environments, such as indoors and in urban canyons.
An example of Bayesian positioning is to use various signals of opportunity (SOOP) — signals not originally intended for positioning and navigation. They include RF signals, such as those of cellular telephone networks, digital television, frequency modulation broadcasting, wireless local area networks, and Bluetooth, as well as naturally occurring signals such as the Earth’s magnetic field and the polarized light from the sun. Indicators of these signals, such as signal strengths and signal quality, are dependent on locations in the Bayesian sense. The dependency between signal indicators and locations is expressed in a probabilistic model:
(2)
where signifies a dependency between a set of physical signals and locations, I denotes indicators of SOOP signals, L denotes location, and P(i|l) is the probability that signal indicators (i) are observed at location (l).
Positioning resolution involves finding a location that yields the maximum a posteriori probability given a specific set of observables. Bayes’ Rule for computing conditional probabilities is applicable in the positioning estimation, and a family of Bayesian inference methods has been developed (see Further Reading).
An inertial navigation system (INS) is a typical relative positioning technology, and it provides the estimation of moved distance, direction, and/or direction change. A commonly used INS consists of accelerometers, gyroscopes, and a compass. It is self-contained and needs no infrastructure in principle to operate. However, the sensors yield accumulated positioning errors, and they need extra information for calibration. For example, in a GNSS/INS combined system, the INS needs to be calibrated using GNSS positioning results. To achieve an enhanced positioning performance in terms of availability, accuracy, and reliability, different positioning technologies are commonly integrated to overcome the limitations of individual technologies in applicability and performance.
This article discusses the feasibility of ambient light (ambilight) positioning, and we believe it is the first time that ambilight has been proposed as a positioning signal source. We propose the use of two types of observables of ambient light, and correspondingly two different positioning principles are applied in the positioning resolution. Our solution does not require any modifications to commonly used sources of illumination, and it is therefore different from other indoor lighting positioning systems that have been proposed, which use a modulated lighting source.
Ambilight positioning does not require extra infrastructure because illumination infrastructure, including lamps and their power supply and windows, are always necessary for our normal functioning within spaces. Ambilight exists anywhere (indoor and outdoor), anytime, if we consider darkness as a special status of ambient light. Ambilight sensors have been sufficiently miniaturized and are commonly used. For example, an ambilight sensor is used in a modern smartphone to detect the light brightness of the environment and to adaptively adjust the backlight, which improves the user vision experience and conserves power. Additionally, ambilight sensors are also widely used in automotive systems to detect the light intensity of environments for safety reasons. Therefore, ambilight positioning can use existing sensors in mobile platforms. This article presents the possibilities and methods of ambilight positioning to resolve both absolute and relative positioning solutions, and which can be integrated as a component in a hybrid positioning system.
Absolute Positioning Using Ambilight Spectral Measurements
The essence of localization problems is to resolve the intrinsic dependency of location on a set of physical observables. Therefore, a straightforward idea is that the type of observables applicable to positioning can be determined once the location-observables dependency is established. The feasibility is validated when the location-observables dependency is confirmed in the sense of necessary and sufficient conditions.
Ambient light is a synthesis of artificial light sources and natural light. The light spectrum is defined by the distribution of lighting intensity over a particular wavelength range. Researchers have reported development of sensor technology that has a spectral response from 300 to 1450 nanometers (from ultraviolet through infrared light). The spectrum of ambient light is mainly determined by colors of reflective surfaces in the circumstance, in addition to that of artificial and natural light sources. Therefore, intensity spectrum measurements are strongly correlated with surrounding environments of different locations. The traditional fingerprinting method can be used to resolve the positioning solution.
The fingerprinting approach makes use of the physical dependency between observables and geo-locations to infer positions where signals are observed. This approach requires the knowledge of observable-location dependency, which comprises a knowledge database. The fingerprinting approach resolves the most likely position estimate by correlating observed SOOP measurements with the knowledge database. The related fingerprinting algorithms include K-nearest neighbors, maximum likelihood estimation, probabilistic inference, and pattern-recognition techniques. These algorithms commonly consider moving positions as a series of isolated points, and they are therefore related to the single-point positioning approach. In addition, a “hidden Markov” model method has been developed to fuse SOOP measurements and microelectromechanical systems (MEMS) sensors-derived motion-dynamics information to improve positioning accuracy and robustness.
In the case of ambilight positioning, prior knowledge is related to structure layout information, including the layout of a specific space, spatial distribution of lighting sources (lamps), types of lighting sources, and windows and doors where natural light can come through. Spatial distribution of lighting sources is normally set up together with power supplies when the structure is constructed, and their layout and locations are not usually changed thereafter. For example, illumination lamps are usually installed on a ceiling or a wall in fixed positions, and the locations of doors and windows, through which light comes, are also typically fixed throughout the life of a building. Therefore, the knowledge database of lighting conditions can be built up and maintained easily through the whole life cycle of a structure.
In practice, a specific working region is divided into discrete grids, and intensity spectrum measurements are collected at grid points to construct a knowledge database. The grid size is determined based on the required spatial resolution and spatial correlation of spectrum measurements. The spatial correlation defines the degree of cross-correlation of two sets of spectrum measurements observed at two separated locations.
We measured the spectrum of ambient light with a two-meter grid size in our library. The measurements were conducted using a handheld spectrometer. FIGURE 2 shows a set of samples of ambilight spectrum measurements, and the corresponding photos show the circumstances under which each spectrum plot was collected. These spectral measurements show strong geo-location dependency. Spectrum differences of different locations are sufficiently identifiable. TABLE 1 shows the cross-correlation coefficients of spectral measurements of different locations. The auto-correlation coefficients of spectral measurements of a specific location are very close to the theoretical peak value of unity, and the cross-correlation coefficients of spectra at different locations are significantly low. Therefore, the correlation coefficient is an efficient measure to match a spectrum observable with a geo-referred database of ambilight spectra.
FIGURE 2. Ambilight spectral measurements of nine locations in the library of the Finnish Geodetic Institute (arbitrary units). The photos below the spectrum plots show the circumstances under which the corresponding spectral measurements were collected.TABLE 1. Correlation coefficient matrix of spectral measurements of different locations.
Relative Positioning Using Ambilight Intensity Measurements
Total ambilight intensity is an integrated measure of the light spectrum, and it represents the total irradiance of ambient light. In general, a lamp produces a certain amount of light, measured in lumens. This light falls on surfaces with a density that is measured in foot-candles or lux. A person looking at the scene sees different areas of his or her visual field in terms of levels of brightness, or luminance, measured in candelas per square meter. The ambilight intensity can be measured by a light detector resistor (LDR), and it is the output of an onboard 10-bit analog-to-digital converter (ADC) on an iRobot platform, which is the platform for a low-cost home-cleaning robot as shown in FIGURE 3.
FIGURE 3. The iRobot-based multi-sensor positioning platform, which is equipped with a light sensor and other versatile positioning sensors as marked in the figure.
We designed a simple current-to-voltage circuit based on an LDR and a 10-kilohm resistor, and the integrated analog voltage is input into the iRobot’s ADC with a 25-pin D-type socket, which is called the Cargo Bay Connector. FIGURES 4 and 6 show that the LDR sensor was not saturated during the test whenever we turned the corridor lamps on or off. Since the output of the light sensor was not calibrated with any standard light source, the raw ADC output rather than real values of physical light intensity was used in this study. During the test, the iRobot platform ran at a roughly constant speed of 25 centimeters per second, and the response time of the LDR was 50 milliseconds according to the sensor datasheet. The sampling rate of light intensity measurements was 5 Hz. Thus, the ADC could digitalize the input voltage in a timely fashion.
FIGURE 4. Total irradiance intensity measurements of ambient light in a closed space. The estimated lamp positions (magenta points) can be compared to the true lamp positions (green points).FIGURE 6. Total irradiance intensity measurements of ambient light in the open corridor of the third floor.
We conducted the experiments with the iRobot platform in two corridors in the Finnish Geodetic Institute building. The robot was controlled to move along the corridors, and it collected measurements as it traveled. The two corridors represent two types of environment. The corridor of the first floor is a closed space where there is no natural light, and the corridor of the third floor has both natural light and artificial illuminating light. The illuminating fluorescent lamps are installed in the ceiling. In a specific environment, fluorescent lamps are usually installed at fixed locations, and their locations are not normally changed after installation. Therefore, the knowledge of lamp locations can be used for positioning.
Ambilight positioning is relatively simple in the first case where there is no natural light in the environment and all ambilight intensity comes from artificial light. Because the fluorescent lamps are separated by certain distances, the intensity measurements have a sine-like pattern with respect to the horizontal distance along the corridor. The sine-like pattern is a key indicator to be used for detecting the proximity of a lamp. As shown in Figures 4 and 6, raw measurements of ambilight intensity and smoothed intensity have a sine-like pattern. Because raw intensity measurements have low noise, either raw measurements or smoothed intensity can be used to detect the proximity of a lamp. Figure 4 also shows the results of detection and the comparison to the true lamp positions. There are four fluorescent lamps in this corridor test. The first three were detected successfully, and the estimated positions are close to true positions with a root-mean-square (RMS) error of 0.23 meters. The fourth lamp could not be detected because its light is blocked by a shelf placed in the corridor just below the lamp as shown in FIGURE 5. Figure 4 shows the sine-like intensity pattern of the fourth lamp did not occur due to the blockage.
FIGURE 5. The light of the fourth lamp in the corridor is blocked by shelves, and the corresponding sine-like light pattern does not appear.
On the third floor, the situation is more complicated because there is both natural light and incandescent lamps in the corridor. Natural light may come in from windows, which are located at multiple locations on the floor. In addition, the light spectrum in the corridor may be interfered with by light from office rooms around the floor. To recover the sine-like intensity pattern of the lamps, the intensity of the background light was measured when the incandescent lamps were turned off. Therefore, the calibrated intensity measurements of illuminating lamps can be calculated as follows:
(3)
where Ia is the intensity measurements of composite ambient light, Ib is the intensity measurements of background light, and Ic is the intensity measurements of the calibrated ambient light of the illuminating lamps.
Figure 6 shows the intensity measurements of composite ambient light, background light, and calibrated lamp light. In addition, the intensity measurements of calibrated lamp light are smoothed by an adaptive low-pass filter to mitigate noise and interference. The intensity measurements of smoothed lamp light were used to estimate the positions of the lamps according to the sine-like pattern. The estimated lamp positions were compared to the true lamp positions, and the errors are shown in FIGURE 7. The estimated lamp positions have a mean error of 0.03 meters and an RMS error of 0.79 meters. In addition, for the total of 15 lamps in the corridor, only one lamp failed to be detected (omission error rate = 1/15) and one lamp was detected twice (commission error rate = 1/15).
Discussion and Conclusion
Ambilight positioning needs no particular infrastructure, and therefore it does not have the problem of infrastructure availability, which many other positioning technologies have, limiting their applicability. For example, indoor positioning systems using Wi-Fi or Bluetooth could not work in emergency cases when the power supply of these devices is cut off. What ambilight positioning needs is just the knowledge of indoor structure and ambilight observables. The lighting conditions of an indoor structure can be reconstructed based on the knowledge of the layout structure whenever illuminating lamps are on or off. Thus, ambilight observables can be related to the layout structure to resolve positioning estimates as we showed in this article.
Besides indoor environments, the methods we have presented are also applicable in many other GNSS-denied environments, such as underground spaces and long tunnels. For example, the Channel Tunnel between England and France has a length of 50.5 kilometers, and position determination is still needed in this kind of environment. In such environments, there is usually no natural light, and the intensity of illuminating lamps has a clear sine-like pattern.
In particular, ambient light positioning is promising for robot applications when a robot is operated for tasks in a dangerous environment where there is no infrastructure for other technical systems such as Wi-Fi networks. Given the knowledge of the lighting infrastructure acquired from the construction layout design, the method of ambilight positioning can be used for robot localization and navigation. Our tests have shown also that the proposed ambilight positioning methods work well with both fluorescent lamps and incandescent lamps, as long as the light intensity sensor is not saturated.
A clear advantage of the technique is that the illuminating infrastructure and the structure layout of these environments are kept mostly unchanged during their life cycle, and the lighting knowledge can be constructed from the structure design. Hence, it is easy to acquire and maintain these knowledge bases. The hardware of ambient light sensors is low-cost and miniature in size, and the sensors can be easily integrated with other sensors and systems.
Although a spectrometer sensor is not currently able to be equipped with a mobile-phone device, the proposed ambilight positioning techniques can still be implemented with a modern mobile phone in several ways. For example, an economical way would be to form a multispectral camera using a selection of optical filters of selected bands or a miniature adjustable gradual optical filter. The spectral resolution then is defined by the bandwidth of the band-pass optical filters and the optical characteristics of the gradual optical filter. Other sensors, such as an acousto-optic tunable filter spectrometer and a MEMS-based Fabry-Pérot spectrometer, could also be used to measure the spectrum of ambilight in the near future. With such techniques, ambilight spectral measurements can be observed in an automated way and with higher temporal resolution.
Acknowledgments
The work described in this article was supported, in part, by the Finnish Centre of Excellence in Laser Scanning Research (CoE-LaSR), which is designated by the Academy of Finland as project 272195. This article is based on the authors’ paper “The Uses of Ambient Light for Ubiquitous Positioning” presented at PLANS 2014, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium held in Monterey, California, May 5–8, 2014.
JINGBIN LIU is a senior fellow in the Department of Remote Sensing and Photogrammetry of the Finnish Geodetic Institute (FGI) in Helsinki. He is also a staff member of the Centre of Excellence in Laser Scanning Research of the Academy of Finland. Liu received his bachelor’s (2001), master’s (2004), and doctoral (2008) degrees in geodesy from Wuhan University, China. Liu has investigated positioning and geo-reference science and technology for more than ten years in both industrial and academic organizations.
RUIZHI CHEN holds an endowed chair and is a professor at the Conrad Blucher Institute for Surveying and Science, Texas A&M University in Corpus Christie. He was awarded a Ph.D. degree in geophysics, an M.Sc. degree in computer science, and a B.Sc. degree in surveying engineering. His research results, in the area of 3D smartphone navigation and location-based services, have been published twice as cover stories in GPS World. He was formerly an FGI staff member.
YUWEI CHEN is a research manager in the Department of Remote Sensing and Photogrammetry at FGI. His research interests include laser scanning, ubiquitous LiDAR mapping, hyperspectral LiDAR, seamless indoor/outdoor positioning, intelligent location algorithms for fusing multiple/emerging sensors, and satellite navigation.
JIAN TANG is an assistant professor at the GNSS Research Center, Wuhan University, China, and also a senior research scientist at FGI. He received his Ph.D. degree in remote sensing from Wuhan University in 2008 and focuses his research interests on indoor positioning and mapping.
JUHA HYYPPA is a professor and the head of the Department of Remote Sensing and Photogrammetry at FGI and also the director of the Centre of Excellence in Laser Scanning Research. His research is focused on laser scanning systems, their performance, and new applications, especially those related to mobile laser scanning and point-cloud processing.
FURTHER READING
• Authors’ Conference Paper
“The Uses of Ambient Light for Ubiquitous Positioning” by J. Liu, Y. Chen, A. Jaakkola, T. Hakala, J. Hyyppä, L. Chen, R. Chen, J. Tang, and H. Hyyppä in Proceedings of PLANS 2014, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Monterey, California, May 5–8, 2014, pp. 102–108, doi: 10.1109/PLANS.2014. 6851363.
• Light Sensor Technology
“High-Detectivity Polymer Photodetectors with Spectral Response from 300 nm to 1450 nm” by X. Gong, M. Tong, Y. Xia, W. Cai, J.S. Moon, Y. Cao, G. Yu, C.-L. Shieh, B. Nilsson, and A.J. Heeger in Science, Vol. 325, No. 5948, September 25, 2009, pp. 1665–1667, doi: 10.1126/science.1176706.
• Light Measurement
“Light Intensity Measurement” by T. Kranjc in Proceedings of SPIE—The International Society for Optical Engineering (formerly Society of Photo-Optical Instrumentation Engineers), Vol. 6307, Unconventional Imaging II, 63070Q, September 7, 2006, doi:10.1117/12.681721.
• Modulated Light Positioning
“Towards a Practical Indoor Lighting Positioning System” by A. Arafa, R. Klukas, J.F. Holzman, and X. Jin in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 17–21, 2012, pp. 2450–2453.
• Application of Hidden Markov Model Method
“iParking: An Intelligent Indoor Location-Based Smartphone Parking Service” by J. Liu, R. Chen, Y. Chen, L. Pei, and L. Chen in Sensors, Vol. 12, No. 11, 2012, pp. 14612-14629, doi: 10.3390/s121114612.
• Application of Bayesian Inference
“A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS” by J. Liu, R. Chen, L. Pei, R. Guinness, and H. Kuusniemi in Sensors, Vol. 12, No. 12, pp. 17208–17233, 2012, doi:10.3390/s121217208.
“Hybrid Positioning with Smartphones” by J. Liu in Ubiquitous Positioning and Mobile Location-Based Services in Smart Phones, edited by R. Chen, published by IGI Global, Hershey, Pennsylvania, 2012, pp. 159–194.
“Non-GPS Navigation for Security Personnel and First Responders” by L. Ojeda and J. Borenstein in Journal of Navigation, Vol. 60, No. 3, September 2007, pp. 391–407, doi: 10.1017/S0373463307004286.
Ambiguity and Environmental Data: Two Further Key Challenges of Multisensor Positioning
By Paul D. Groves, Lei Wang, Debbie Walter, and Ziyi Jiang, University College London
The coming requirements of greater accuracy and reliability in a range of challenging environments for a multitude of mission-critical applications require a multisensor approach and an over-arching methodology that does not yet exist. Part 1 of this article, in the October issue, examined the two key concepts of complexity and context. In this continuation, we complete our overview with exploration of the requirements of ambiguity and environmental data.
Ambiguity occurs when measurements can be interpreted in more than one way, leading to different navigation solutions, only one of which is correct. Any navigation technique can potentially produce ambiguous measurements. The likelihood depends on both the positioning method and the context, both environmental and behavioral. Urban and indoor positioning techniques that do not require dedicated infrastructure are particularly vulnerable to ambiguity. Poor handling of ambiguity results in erroneous navigation solutions and the navigation system can become “lost,” whereby it is unable to recover and may even reject correct measurements.
There are six main causes of ambiguity: feature identification, pattern matching, propagation anomalies, geometry, system reliability, and context ambiguity. Each of these is described in turn below.
Feature Identification Ambiguity. The proximity, ranging, angular positioning, and Doppler positioning methods all use landmarks for positioning. These may be radio, acoustic, or optical signals, or natural or man-made features of the environment. For reliable positioning, these signals or features must be correctly identified.
Digital signals intended for positioning incorporate identification codes. However, where a signal is weak and/or interference is high, it may be possible to use the signal for positioning but not decode the identification information. For signals of opportunity — that is, not designed for positioning — the identification codes may be encrypted, while analog signals do not typically have identifiers. These signals must be identified using their frequencies and an approximate user position, in which case there may be multiple candidates. Even where a signal of opportunity is identifiable, the transmission site may change without warning. For example, Wi-Fi access points are sometimes moved and mobile phone networks are periodically refigured. Thus, there is a risk of false landmark identification.
Environmental features are difficult to identify uniquely. In image-based navigation, man-made features, such as roads, buildings, and signs, are easiest to identify in images due to their line and corner features. However, similar objects are often repeated in relatively close proximity. For example, Figure 18 shows the locations of the five “no entry” signs in a 1,200-meter circuit of Central London streets. Two of the signs are within 20 meters of each other. (Figure numbering continues the sequence beginning in Part 1, October issue.)
Figure 18. “No entry” signs in a 1,200-meter circuit of Central London. (Background image courtesy of Bing maps | Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)
Pattern-Matching Ambiguity. The pattern-matching positioning method maintains a database of measurable parameters that vary with position. Examples include terrain height, magnetic field variations, Wi-Fi signal strengths, and GNSS signal availability information. Values measured at the current unknown user position are compared with predictions from the database over a series of candidate positions. The position solution is then obtained from the highest scoring candidate(s).
An inherent characteristic of pattern matching is that there is sometimes a good match between measurements and predictions at more than one candidate position. Figure 19 and Figure 20 show GNSS shadow-matching scoring maps based on smartphone measurements taken at the same location 40 seconds apart. The scores are obtained by comparing GNSS signal-to-noise measurements with signal availability predictions derived from a 3D city model. In Figure 19, maximum scores (shown in dark red) are only obtained in the correct street, whereas in Figure 20, there is also a high-scoring area in the adjacent street, giving two possible position solutions.
Figure 19. GNSS shadow-matching scoring map – unambiguous case (the cross shows the true position and white areas are indoor locations). (Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)Figure 20. GNSS shadow-matching scoring map – unambiguous case (the cross shows the true position and white areas are indoor locations). (Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)
Figure 21 presents another example, showing the height of a road vehicle derived from a barometric altimeter at three different times. Provided the altimeter is regularly calibrated, it may be used for terrain-referenced navigation (TRN), determining the car’s position along the road by comparing the measured height with a database. However, if only the current height is compared, it will typically match the database at multiple locations within the search area, as the figure shows. The ambiguity can be reduced by comparing a series of measurements from successive epochs, known as a transect, with the database. This approach is applicable to any pattern-matching technique. However, increasing the transect length to reduce the ambiguity also reduces the update rate, and the ambiguity problem can never be eliminated completely.
Figure 21. Height of a car derived from a barometric altimeter at three different times; readings of around 235 m are highlighted. (Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)
Signal Propagation Anomalies. The ranging, angular positioning, and Doppler positioning methods all make the assumption that the signal propagates from the transmitter (or other landmark) to the user in a straight line at constant speed. Significant position errors can therefore arise when these assumptions are not valid due to phenomena such as non-line-of-sight reception, multipath interference, and severe atmospheric refraction. In challenging environments, such as dense urban areas and indoors, multiple signals are typically affected by propagation anomalies, and it is not always easy to determine which signals are contaminated.
Where the position solution is overdetermined (that is, more than the minimum number of signals are received), different combinations of signals will produce different position solutions when there are significant propagation anomalies.
Figures 22 and 23 illustrate this for conventional GNSS positioning using a Leica Viva geodetic receiver, showing the position errors obtained using different combinations of GPS and GLONASS signals. In Figure 22, the receiver is located on a high rooftop and the majority of position solutions are within 15 meters of the mean, with the remainder easily dismissible as outliers. However, in Figure 23, where the receiver is located in a dense urban location, the candidate position solutions are spread over more than 100 meters, and the correct position solution is not clear. The densest cluster of positions is far from both the centroid and the truth. Therefore, anomalous signal propagation may be treated as an ambiguity problem.
Figure 22. GNSS position errors using different combinations of signals in a rooftop environment. (Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)Figure 23. GNSS position errors using different combinations of signals in a dense urban environment. (Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)
Geometric Ambiguity. Geometric ambiguity occurs when more than one position solution may be derived from a set of otherwise unambiguous measurements. Figure 24 shows two examples. On the left, two ranging measurements in two dimensions produce circular lines of position that intersect in two places. On the right, a ranging measurement and a direction-finding measurement are made using the same signal. As direction finding has a 180° ambiguity, the lines of position also intersect at two places.
Figure 24. Geometric ambiguity in two dimensions from two ranging measurements (left), and a ranging and direction-finding measurement (right). (Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)
System Reliability. Navigation subsystems can produce incorrect information for a host of different reasons. Some examples include:
user equipment hardware and software faults;
transmitter hardware and software faults;
out-of-date databases used for pattern matching, including TRN, GNSS shadow matching, and map matching;
wheel slips in odometry;
the effects of passing vehicles and animals on environmental feature visibility, availability and strength of radio signals, and Doppler-based dead reckoning.
Some of these failure modes are easily detectable through the measurements failing basic range checks or being absent altogether. In other cases, faults may be detected by consistency checks within the subsystem. For example, wheel slip may be detected by comparing measurements from different wheels, while Doppler radar and sonar systems typically incorporate a redundant beam to enable the interruption of a beam by a vehicle or animal to be detected.
Subsystems can sometimes output incorrect information that is plausible. An ambiguity thus exists where it is uncertain whether or not a measurement may be trusted. An ambiguity also exists where a fault has been detected, but not its source. Thus, some of the information produced by the subsystem must be incorrect, but some of it may be correct.
Context Ambiguity. As discussed in Part 1 of this article (October issue), the optimum way of processing sensor information depends on the context. However, if context information is used, the navigation solution will then depend on the assumed context. For example, if an indoor environment is assumed, indoor radio positioning and map-matching algorithms that are only capable of producing an indoor position solution may be used. Similarly, if an urban environment is assumed, GNSS shadow matching and outdoor map matching may be selected, resulting in an outdoor position solution. Adoption of pedestrian and vehicle motion constraints can also lead to different navigation solutions.
Context determination is not a completely reliable process. Therefore, to minimize the impact of incorrect context assumptions on the navigation solution, the context should be treated as ambiguous whenever there is significant uncertainty.
Possible Solutions
There is no obvious solution to the ambiguity problem. Instead, different approaches to integrating ambiguous information may be adopted depending on the relative priorities of solution availability, reliability, and processing load. The main approaches, illustrated in Figure 25, are discussed below. They all require the subsystems to present the different measurement hypotheses and their associated probabilities to the integration algorithm.
Figure 25. Methods of handling ambiguous measurements in a navigation integration algorithm. (Photo: Paul D. Groves, Lei Wang, Debbie Walter and Ziyi Jiang, University College London)
Accept or reject the lead hypothesis. The simplest way of handling ambiguous information is to maintain a single-hypothesis navigation solution and consider only the most-probable hypothesis from each subsystem. This is then accepted or rejected based on the following criteria:
Whether the probability of the highest scoring hypothesis above a certain threshold.
Whether the probability of the second-highest scoring hypothesis below a certain threshold.
Whether the highest-scoring measurement hypothesis is consistent with the current integrated navigation solution. (Determinable using measurement innovation filtering.)
Context may be incorporated into this approach by accepting the highest-scoring behavioral and environmental contexts where they meet the above criteria and computing a context-independent navigation solution otherwise.
This approach is processor-efficient, but high integrity and availability cannot be achieved simultaneously. Low acceptance thresholds provide high reliability by rejecting most erroneous measurements, but low solution availability as many good measurements are also rejected. Conversely, high acceptance thresholds provide availability at the expense of reliability.
Accept all hypotheses into a single-hypothesis solution. A probabilistic data association filter (PDAF) accepts multiple measurement or context hypotheses, weighting them according to their probabilities, but represents the navigation solution as the mean and covariance of a uni-modal distribution. The measurement update to the state estimation error covariance matrix accounts for the spread in the hypotheses such that the state uncertainties can sometimes increase following a measurement update.
This approach reconciles the demands of integrity and availability at the price of a moderate increase in processing load. However, the uni-modal navigation solution can sometimes be misleading. For example, if a pattern-matching system determines that the user is equally likely to be in one of two parallel streets, the overall position solution will be midway between those streets.
Multi-hypothesis integration accepting all hypotheses. Multi-hypothesis integration deals with multiple measurement and context hypotheses by spawning multiple integration filters, one for each hypothesis. Each filter is allocated a probability based not only on the probabilities of the measurements input to it, but also on the consistency of those measurements with the prior estimates of that filter. This consistency-based scoring is essential; otherwise the filter hypothesis that inputs the highest-scoring measurement hypotheses will always dominate, regardless of whether those measurements are consistent across subsystems and successive epochs.
A fundamental characteristic of multi-hypothesis filtering is that the number of hypotheses grows exponentially from epoch to epoch. This is clearly impractical, so the number of hypotheses is limited by merging the lowest scoring hypotheses into higher scoring neighbors.
The overall navigation solution is the weighted sum of the constituent filter hypotheses. Each individual filter hypothesis describes a uni-modal distribution. However, the combined navigation solution is multi-modal. Thus, the position probability can be higher in two streets than in the buildings between those streets. This is a clear advantage over the PDAF-based approach, but the processing load is higher.
Multi-modal integration accepting all hypotheses. A multi-modal filter is not constrained to model the states it estimates in terms of a mean and covariance. This enables it to process multiple measurement and/or context hypotheses and represent the result as a weighted sum of the probability distributions arising from the individual hypotheses. Suitable data-fusion algorithms include the Gaussian mixture filter and the particle filter. A key advantage over multi-hypothesis integration is that measurements may be treated as continuous probability distributions instead of as a set of discrete hypotheses. This enables pattern-matching measurements to be integrated more naturally and offers greater flexibility in handling signal propagation anomalies.
A Gaussian mixture filter models the probability distribution of the navigation solution as the weighted sum of a series of multi-variate Gaussian distributions. An example is the iterative Gaussian mixture approximation of the posterior (IGMAP) technique, which has been applied to terrain referenced navigation integrated with inertial navigation.
A particle filter models the probability distribution of the navigation solution using a series of semi-randomly distributed samples, known as particles. Between a thousand and a million particles are typically deployed, with a higher density of particles in higher probability regions of the distribution. Particle filters have been used with a number of different navigation technologies, including TRN, pedestrian map matching, Wi-Fi positioning, and GNSS shadow matching.
Multi-modal integration algorithms offer the greatest flexibility in reconciling the demands of solution availability and reliability, but also potentially impose the highest processing load.
Issues to Resolve
The key challenge in handling ambiguous measurements is determining realistic probabilities for each hypothesis. A probability must also be calculated for the null hypothesis, that is, the hypothesis that every candidate measurement output by the subsystem is wrong. The same applies to ambiguous context.
A feature identification algorithm must allocate a score to every database feature that it compares with the sensor measurements. In practice, only features within a predefined search area, based on the prior position solution and its uncertainty, will be considered. Features scoring above a certain threshold will be possible matches. Similarly, pattern- matching algorithms allocate a score to each candidate position in the search area according to how well the sensor measurements match the database at that point. For correct handling of ambiguous matches, these scores should be as close as possible to the probabilities of the feature match or candidate position being correct.
Feature identification and pattern-matching algorithms can also fail to consider the correct feature or candidate position for several reasons. The correct feature or position may be outside the database search area. It may be absent due to the database being out of date. The sensor may also observe or be affected by a temporary feature that is not in the database, such as a vehicle. The null hypothesis probability must account for all of these possibilities. In practice, it will be higher where there is no good match between the measurements and database.
Signal propagation anomalies affect the error distributions of ranging, angle, and Doppler shift measurements, and the positions and velocities derived from them. These error distributions depend on whether the signals are direct line-of-sight (LOS), non-line-of-sight (NLOS), or multipath- contaminated LOS. However, this is not typically known. Signal strength measurements, environmental context, signal elevation (for GNSS), distance from the transmitter (for terrestrial signals), consistency between different measurements, and 3D city models can all contribute useful information. However, their relationship with the measurement errors is complex, so a semi-empirical approach is needed.
Moving on to reliability, virtually any subsystem can produce false information. The overall probability will typically be very low and thus only significant for high-integrity applications. However, the failure probability will be higher in certain circumstances, in which case the relevant subsystem should report a higher null probability. For example, in odometry, the probability of a wheel slip depends on host vehicle dynamics. Similarly, a radio signal is more likely to be faulty if it is weaker than normal. Repeated measurements, changes to the update interval, and sudden changes in a sensor output are also indicative of potential faults.
Geometric ambiguity is easy to quantify as the candidate solutions have equal probability in the absence of additional information.
As proposed in Part 1, the context determination process should produce multiple context hypotheses, each with an associated probability. Therefore, it is important to ensure that all navigation subsystems that use this context information do so in a probabilistic manner. Thus, where different context hypotheses lead to different values of the measurements output by a navigation subsystem, each measurement hypotheses should be accompanied by a probability derived from the context probabilities.
A further issue to resolve is the relationship between discrete and continuous ambiguity. Ambiguities in feature identification, solution geometry, failures, and context categorization are discrete and are suited to integration filters that treat them as a set of discrete hypotheses. However, the position solution ambiguity in pattern-matching is continuous, that is, the probability density is a continuous function of position, albeit sampled at discrete grid points. This probability distribution may be input directly to a particle filter. However, if the integration algorithm is a uni-modal filter or a bank of uni-modal filters, the probability distribution must be converted to a set of discrete hypotheses. This can be done by fitting a set of Gaussian distributions to the probability distribution. For signal propagation anomalies, their presence or absence is discrete. However, the resulting measurement error distribution is continuous, so a similar approach is appropriate.
The same challenging environments that require multiple navigation subsystems to maximize solution availability, accuracy, and reliability can also induce those subsystems to produce ambiguous measurements. Consequently, the modular integration architecture proposed in Part 1 should be capable of handling ambiguous measurements.
This is discussed further in our IEEE/ION PLANS 2014 paper, “The Four Key Challenges of Advanced Multisensor Navigation and Positioning.”
Environmental Data
Position-fixing systems need information about the environment, sometimes known as a “world model,” to operate. Proximity, ranging, and angular positioning all use landmarks that must be identified. For GNSS and other long-range radio systems, identification codes are determined when the system is designed and incorporated in the user equipment. However, this is not practical for shorter range signals, whether opportunistic or designed for positioning, due to the vast numbers of transmitters available worldwide and the fact that many will be installed during the lifetime of the user equipment. The user equipment will also require information on the characteristics of a signal to enable it to use that signal for ranging. A mobile device equipped with a generic radio or transceiver may be required to download software to enable it to use a proprietary indoor positioning system. For environmental feature-matching techniques, the user equipment requires information to enable it to identify each landmark.
Navigation using landmarks also requires their positions and, for passive ranging, their timing offsets. Signals designed for positioning typically provide this information, but it can take a long time to download (30 seconds for GPS C/A code) and can be difficult to demodulate under poor reception conditions. The positions of opportunistic radio transmitters and environmental features must be determined by other means.
For positioning using the pattern-matching method, a measurement of radio signal strength or a characteristic of the environment, such as the terrain height or magnetic field, is compared with a database to determine position. Therefore, a database providing values of the measured parameter over a regular grid of positions is required. Map matching requires a map database to indicate where the user can and cannot go. GNSS shadow matching requires a 3D city model to predict signal visibility.
Finally, as discussed in Part 1 of this article, mapping is required to determine environmental context information from the position solution and to enable location-dependent context connectivity information (for example, the location of train stations) to be used for context determination.
Possible Solutions
We discuss in turn the environmental data collection and its distribution to the user equipment.
Data Collection. Positioning data may be collected either from a systematic survey or by the users. In either case, regular updates will be required. A systematic survey might be conducted by the subsystem supplier, a national mapping agency, or a private third party. The user will need to pay for the data in some way. It could be included in the equipment cost, via a subscription payment, by accepting advertising, or through general taxation (for some national mapping agency data). For mobile devices, such as smartphones, mapping data may be available for some applications, but not others.
Single-user data collection does not involve user charges, but only provides data for places the user has already visited. A simple approach requires a good position solution to collect mapping data. This can work for applications that normally use GNSS, but require backups for temporary outages. However, it does not work for areas where GNSS reception is poor. Simultaneous localization and mapping (SLAM) techniques can perform mapping without a continuous position solution. However, there are several constraints. First, a good position solution that is independent of the data being mapped is required at some point, usually the start. Second, a navigation system including dead-reckoning technology must be used. Third, locations must be visited repeatedly within a short period of time (to achieve “loop closure”). Finally, only features close to the user can be mapped.
Cooperative mapping by a group of users solves many of the problems of single-user mapping. It can provide individual users with data for places they have not visited before. Distant landmarks can also be mapped more easily by multiple users, particularly where it is necessary to determine a timing offset as well as the location. However, a method for comparing and combining data from multiple users is required.
Data Distribution. For data collected by a systematic survey, there are two main data distribution models: pre-loading and streaming. Pre-loading requires sufficient user equipment data storage to cover the area of operation. New data may have to be loaded prior to a change in operating area, and updates will be required. However, a continuous communications link is not needed.
Streaming requires much less data to be stored by the user and provides up-to-date information, but only where a communications link is available. Although buffering can bridge short outages, navigation data is simply not available for areas without sufficient communications coverage. Continuous streaming can also be expensive. One solution is a cooperative approach using peer-to-peer communications for much of the data distribution. A pair of users traveling in opposite directions along the same route will each have data that is useful to the other. A further possibility is to incorporate local information servers in Wi-Fi access points for exchanging information relevant to the immediate locality. This might be best suited to indoor navigation, where there is an incentive for the building operator to provide the service.
For data collected by a single user, no data distribution is required other than a back-up. For cooperative data collection by multiple users, a method of data exchange is needed. This can be via a central server, communicating either in real time or whenever the user returns to base. It can also be through peer-to-peer communications or through local information servers, where there is an incentive to provide them.
Issues to Resolve
Standardization is a major part of the data management challenge. A multisensor navigation system will typically incorporate multiple subsystems with data requirements. This might include road or building mapping, radio signal information, terrain height, magnetic anomalies, visual landmarks, and building signal-masking information for GNSS shadow matching. There will be a different standard for each type of data. Furthermore, different subsystem suppliers will often use different standards for the same type of data. This is sometimes done for commercial and/or security reasons, so the data may be encrypted. There may also be technical reasons for different data standards. For example, in image-based navigation, different feature recognition algorithms require different descriptive data.
Ideally, all navigation data in a multisensor system should be distributed by the same method. This requires agreement of storage and communication protocols that can handle many different data formats, including encrypted proprietary data and future data formats. Open standards for each type of data should also be agreed, noting that consumer cooperative positioning using peer-to-peer communications and/or local information servers is probably only practical with open data formats. Ideally, the standards should be scalable to enable precisions, spatial resolutions, and search areas to be adapted to the available data storage and communications capacity.
Peer-to-peer data exchange requires a suitable communications link. Bluetooth is the established standard for consumer applications. Classic Bluetooth provides sufficient capacity, but it takes longer to establish a connection than passing pedestrians or vehicles remain within range. Bluetooth low energy can establish a connection quickly, but the data capacity is limited to 100 kbit/s. This is sufficient for some kinds of navigation data, but not others. Professional and military users have more flexibility to select suitable datalinks.
Finally, establishing local information servers requires both standardization and an incentive for the hosts. Demand would be greater if there were applications beyond navigation and positioning. Possibilities include product information in shops and exhibit information in museums, both of which might be provided more efficiently from a local server than the Internet. For home users to provide local information servers, they would also have to benefit from them, a potential “chicken-and-egg” problem. For military applications, local information servers are a potential security risk and a target for attack.
Conclusions and Recommendations
Achieving accurate and reliable navigation in challenging environments without additional infrastructure requires complex multisensor integrated navigation systems. However, implementing them presents four key challenges: complexity, context, ambiguity, and environmental data handling. Each of these problems has been explored and solutions proposed.
Conclusions. In Part 1 of this article, a modular integration architecture was proposed to enable multiple subsystems from different organizations to be integrated without the need for whole system expertise or sharing of intellectual property. Furthermore, context-adaptive navigation was proposed to enable a navigation system to respond to changes in the environment and host vehicle (or user) behavior, deploying the most appropriate algorithms. A new probabilistic approach to context determination was proposed and results presented from a number of context detection experiments.
Here, it has been shown that navigation solution ambiguity can arise from feature identification, pattern matching, propagation anomalies, solution geometry, system reliability issues, and context ambiguity. A number of methods for handling ambiguous measurements in a multisensor navigation system have been reviewed.
Finally, methods of collecting and distributing data such as locations of radio transmitters and other landmarks, information for identifying signals and landmarks, road or building mapping, terrain height, magnetic anomalies, and building signal-masking information (for GNSS shadow matching) have been discussed.
Implementing the ideas proposed in this two-part article requires both standardization and further research. Standardization is needed to enable the communication between modules produced by different suppliers of information such as the integrated navigation solution, sensor measurements and characteristics, calibration parameters, performance requirements, context information, mapping, and signal and feature characteristics.
Further research is needed to support this standardization process, including the identification of a set of fundamental measurement types and their error sources, and the establishment of the best set of context categories for integrated navigation.
Extensive research into context detection and determination is needed, including the measurements to use, the statistical parameters to derive from those measurements, and a set of context association and connectivity rules.
An assessment of the different methods for handling ambiguous measurements is needed, comparing accuracy, reliability, solution availability, and processing load. This will enable the community to determine which methods are suited to different applications.
Finally, there is a need for a practical demonstration of the key concepts proposed in this paper, including modular integration, context adaptivity, ambiguous measurement handling, and collection and distribution of environmental data.
Paul D. Groves is a lecturer at University College London (UCL), where he leads a program of research into robust positioning and navigation. He is an author of more than 60 technical publications, including the book Principles of GNSS, Inertial and Multi-Sensor Integrated Navigation Systems, now in its second edition. He is a Fellow of the Royal Institute of Navigation and holds a doctorate in physics from the University of Oxford.
Lei Wang is a Ph.D. student at UCL. He received a bachelor’s degree in geodesy and geomatics from Wuhan University. He is interested in GNSS-based positioning techniques for urban canyons.
Debbie Walter is a Ph.D. student at UCL. She is interested in navigation techniques not reliant on GNSS, multi-sensor integration, and robust navigation. She has an MSci from Imperial College London in physics and has worked as an IT software testing manager.
Ziyi Jiang was a postdoctoral research associate at UCL until 2014, working on urban GNSS and other projects. He holds a bachelor’s degree in engineering from Harbin University and a Ph.D. in rail positioning from UCL. He now works in finance.
All authors are members of UCL Engineering’s Space Geodesy and Navigation Laboratory (SGNL).
Ricoh Americas Corporation has made major improvements to its water-, dust- and shock-resistant G800 camera. The G800SE model includes GPS and a barcode-reading capability that helps streamline workflows across a wide range of industries.
The new Wi-Fi- and Bluetooth-enabled Ricoh G800SE allows for dynamic information capture in industries such as automotive, logistics, government, healthcare, emergency services and more. For example, a car dealership can scan the barcode on a new car, photograph the vehicle, and have the photograph and vehicle information flow directly to the dealership website for customers to see. This new workflow saves hours of painstaking information retrieval and organization, Ricoh said. With a standard camera, clerks would need to manually match images to each car, introducing the possibility of error at every step.
“By embedding critical data into images, the Ricoh G800SE epitomizes information mobility, the ability for employees to access just the information they need in the form they need it to make important decisions,” said Matt Sakauchi, vice president, Technology Marketing, Ricoh Americas Corporation. “This is just one way we’re making customers’ business information work for them in this new world of work.”
The Ricoh G800SE includes a host of upgrades over the previous generation of ruggedized Ricoh wireless-enabled camera, the Ricoh G700SE. For example, a higher-resolution image sensor provides a new ability to capture images under low-light conditions. The Ricoh G800SE also takes advantage of higher wireless transmission speeds and the more secure “enterprise” Wi-Fi increasingly seen in healthcare facilities and government agencies. It leverages the Protected Extensible Authentication Protocol (PEAP), an emerging standard in enterprise wireless security.
A flexible password lock feature enables organizations to create separate passwords for administrators and users with each one unlocking different sets of permissions. A smartphone connection enables users to remotely operate the camera via their mobile devices. Using a smartphone or tablet, they can preview, zoom, shoot and capture image data, including GPS position. The capability is also intended for surveillance applications.
Organizations can manage fleets of Ricoh G800SE cameras via remote management software with the ability to upgrade firmware, adjust camera settings and update memo functions. An alternative for camera configuration is distributing SD cards with prescribed settings.
Mauro Colombi, vice president of operations for Stonex, discusses the new S10 GNSS Receiver while at InterGeo 2014, held October 7-9 in Berlin. The S10 features a new generation of smart and open GPS, where a user can install custom applications directly on the receiver.
Bruce Carlson, president of Carlson Software, and William “Butch” Herter talk about the company’s new BRx5 GNSS Receiver and Surveyor2 data collector, among others, while at InterGeo 2014, held October 7-9 in Berlin.
In recent tests, the IDS ran continuously for 24 hours monitoring for potential interference originating from traffic on a nearby highway, SR-522, passing through Woodinville, Washington. “In a single day, the IDS detected two separate instances of a sweep jammer moving along the highway,” noted Loctronix Founder and CEO Michael Mathews. “These discoveries were unexpected, given the relatively short monitoring period and the fact that SR-522 is not a heavy truck-route.”
“The two interference events were likely caused by sweep jammers installed within a vehicle’s interior. The intercepted signals exhibited significant variations in amplitude probably caused by the jammer antenna non-uniform radiation pattern as the jammer vehicle passed through the IDS antenna beam pattern,” Mathews added.
Intentional interference is designed to prevent a GPS receiver from acquiring and tracking signals. The use of jammers is in the U.S. is illegal; however, they can still be purchased for as little as $30. Thousands of GPS jammers are purportedly in use throughout Europe and several parties have been caught illegally jamming GPS in the U.S.
Loctronix developed the IDS to identify, characterize, and ultimately geolocate GPS interference. When interference is present, the system analyzes the interference for signal structure and notifies operators if the threat is significant.
The IDS is highly portable, simple to use, and cost-effective, Loctronix said. The system is based upon the Loctronix ASR-2300 software defined radio platform, making it readily configurable (from a single mobile detector to a multi-sensor network array) to monitor additional GNSS bands and, potentially, cellular bands.
In the video below, Michael B. Mathews, Ph.D., CEO and founder of Loctronix, tells GPS World about the IDS at the ION GNSS+ Conference, held September in Tampa, Florida.
