GPS World

Category: Mobile

  • Innovation: Precise positioning using raw GPS measurements from Android smartphones

    Innovation: Precise positioning using raw GPS measurements from Android smartphones

    Precision GNSS for everyone

    In this month’s column, we take a look at some initial efforts to independently process smartphone measurements. How good are the results? Read on.

    INNOVATION INSIGHTS with Richard Langley
    INNOVATION INSIGHTS with Richard Langley

    IT WAS 1999. That was the year when the first mobile or cell phones equipped with GPS became available. Garmin introduced the NavTalk Pilot aimed at aviators and Benefon, a former Finnish cellphone manufacturer, offered the Benefon Esc! These devices benefited from the continuing reduction in the size (and power needs) of GPS receivers, which had been shrunk to just a few integrated circuits or chips.

    I documented that progress in GPS technology in an article for this column in April 2000 titled “Smaller and Smaller: The Evolution of the GPS Receiver.” In that article, I also mentioned that receiver modules had been made small enough to be put in a wristwatch. This was something that I and other researchers at the University of New Brunswick had predicted in a paper presented at a meeting in 1983. Talk about prescient.

    In our paper, we said “With the miniaturization and cost reduction being experienced continually, it is surely safe to postulate the limit of this evolution: a cheap ‘wrist locator’ giving instantaneous positions to an accuracy of 1 [millimeter].” Elsewhere in the paper, we suggested a price for this technological wonder of $10, and that it would be available sometime in the twenty-first century.

    Costing about $400 and giving GPS Standard Positioning Service accuracies, the first “wrist locator” also came on the market in 1999 — before the 21st century began. While we may have been a bit overly optimistic in the capabilities and cost of the “wrist locator,” the basic prediction came true earlier than expected. And I said in that April 2000 column that there’s room for further development. No kidding. It wasn’t many years after that GPS World article appeared that we had announcements of single-chip receivers that could be more easily integrated into cell phones and other devices.

    And today we have “system on chip” integrated circuits that combine many of the major functions of a cell phone into a single chip including a multi-core microprocessor, modems for two-way radio communications and most of the functioning of a GNSS receiver. And I say GNSS receiver as the latest chips support not just GPS but GLONASS, BeiDou, Galileo and the Quasi-Zenith Satellite System as well as satellite-based augmentation systems.

    The widespread addition of GPS receivers to cell phones was initially stimulated by E-911 requirements in North America and similar initiatives elsewhere. In the United States, the Federal Communications Commission requires cell-phone carriers to report phone location to within 50 meters for 67 percent of emergency calls, and within 150 meters for 90 percent of calls. Such accuracies are readily achieved in most outdoor locations even with some multipath signal degradation. In fact, positioning accuracies for cell phones in benign environments are often better than 10 meters, even approaching the meter level at times. This allows us to use applications on our GNSS-equipped smartphones for navigation, for example. As a result, some smartphone users are abandoning their vehicle “satnavs” in a move not unlike the abandonment of landline telephones.

    While positioning accuracy at the meter or few-meter level may be adequate for pedestrian and vehicle navigation, sub-meter-level accuracy might be desirable for certain tracking applications and other uses–including some we haven’t even dreamed of yet. So, are such cell-phone positioning accuracies achievable with current technology? How close are we to having personal navigation devices with the one-millimeter accuracy of our futuristic “wrist locator?” Thanks to Google’s recent release of code to permit access to the raw GNSS measurements from smartphones and tablets running a version of the Android operating system, researchers and developers are able to answer that question.

    In this month’s column, we take a look at some initial efforts to independently process smartphone measurements. How good are the results? Read on.

    By Simon Banville and Frank van Diggelen

    The development of low-cost GNSS chips spurred a revolution in positioning, navigation and timing (PNT) devices. Once reserved for military operations and high-end geodetic applications, GNSS positioning eventually found its way into the lives of millions (if not billions) of users with the development of GNSS-enabled car navigation devices and smartphones.

    The meter-level accuracies provided by GNSS receivers in smartphones enabled a wide range of location-based services including social networking, vehicle tracking, weather services and so on. At the other end of the spectrum, more expensive GNSS equipment can provide centimeter- and even millimeter-level accuracies by tracking signals on multiple frequencies and by using high-quality antenna and receiver components. Such GNSS receivers are utilized in a variety of applications such as tectonic motion monitoring, land surveying, precision farming, oil and gas exploration, and machine control.

    During its “I/O 2016” conference held in May 2016, Google announced that raw GNSS measurements from smartphones and tablets running the Android N (“Nougat” = version 7) operating system would be made available to developers. The implications of this initiative are significant for the community since it allows us to move away from the black-box concept of the GNSS receiver providing meter-level accuracies and opens up the possibilities of using pseudorange, Doppler and carrier-phase measurements to derive more accurate positions. Even if the low-cost GNSS antennas and chips contained in smartphones will never outperform high-end geodetic instruments, it is an interesting research avenue to investigate how far these devices can take us. This opportunity could in turn spark the emergence of new applications that would not have been envisioned before.

    Even though the opportunities for high-precision positioning with smartphones were limited prior to this announcement, scientists and engineers have already tried to tackle this issue. For instance, researchers at the University of Texas at Austin used a smartphone antenna to feed GNSS signals into a software-defined receiver built at their facility.

    While carrier phases were affected by significant time-correlated errors such as multipath, centimeter-level differential positioning could still be achieved. Direct access to GNSS measurements from modified smartphone firmware was also reported. In one such experiment, a survey-grade antenna was used to feed GNSS signals to a modified Samsung Galaxy S5 smartphone running a Broadcom GNSS chip. The analysis revealed a nonzero and drifting bias in the carrier-phase measurements that prevented both floating-point-valued-ambiguity and integer-ambiguity-fixed solutions to be computed.

    Microsoft Mobile also produced custom firmware for the Nokia Lumia 1520 “phablet” smartphone, allowing access to raw GNSS measurements from the phone’s internal Qualcomm integrated receiver. This data, analyzed by members of the Finnish Geospatial Research Institute, identified pseudorange measurement noise on the order of tens of meters and carrier-phase observations contaminated by several outliers. As a result, only meter-level positioning could be achieved.

    In the following sections, we first explain how raw GNSS measurements can be accessed from the Android N operating system (os). After performing a preliminary assessment of the data quality, we use state-of-the-art positioning software developed at Natural Resources Canada to assess whether precise positioning can currently be achieved using raw GPS observations collected by a smartphone.

    ACCESSING RAW GNSS MEASUREMENTS

    The Android operating system defines application programming interfaces (APIs), which are a collection of protocols allowing users to access the system’s functionalities. The GNSS raw measurements are contained in the GnssClock and GnssMeasurement software classes, which are described in the android.location APIs. Google has released the GnssLogger application or app along with its source code (see FIGURE 1). You can find the app here (download the file GnssLogger.apk).

    FIGURE 1. GnssLogger screenshot, showing raw measurements from a GPS satellite and a GLONASS satellite.
    FIGURE 1. GnssLogger screenshot, showing raw measurements from a GPS satellite and a GLONASS satellite.

    You can use the app as-is to log the GNSS measurements to a text file, or you can use the source code to build the GNSS measurements into your own app. At the same GitHub repository, you will also find the measurement data used in this article, and Matlab files for reading, processing and plotting the data.

    The GnssLogger app logs the measurement data in comma-separated-value (csv) text format, and sends the file by Internet to your e-mail, Google Drive or some other file-sharing facility. The data fields are described in the GnssClock and GnssMeasurement classes in the online android.location API documentation.

    The app logs the decoded ephemeris data in decimal representations of the bytes defined by the respective constellation interface control documents (ICDs). The android.location format is more aligned with typical mobile devices than existing formats, and includes concepts such as hardware clock discontinuity (to support power-save duty cycling), and received satellite time modulo 1, 2, 4, 10 or 20 milliseconds; 0.6, 1, 2 or 6 seconds; 1 day; or 1 week; depending on the satellite system, and the highest sync state achieved per satellite (such as code lock, bit sync, subframe sync and so on).

    This was done because smartphone fixes are often achieved before bit sync, frame sync or time of day/week have been decoded. Thus one can derive Radio Technical Commission for Maritime Services (RTCM) or Receiver-Independent Exchange (RINEX) formats from the Android raw measurements, but not vice-versa without losing information. Developers are encouraged to create RTCM and RINEX logging apps and publish them on the Google Play Store.

    The first available Android products with GNSS raw measurements are the following devices running the Android N OS: Nexus 9 tablet, Nexus 5x phone, Nexus 6p phone, Pixel phone and the Pixel XL phone. The raw measurements from Nexus 9 include accumulated delta range (that is, carrier-phase measurements) for GPS and GLONASS. The Nexus 5x, Nexus 6p and Pixel phones track GPS and GLONASS, but the raw measurements from these phones are from GPS only, and do not include carrier phase.

    Future Android phones with the Android N (or newer) OS, when paired with GPS chips manufactured in 2016 or later, will support the GNSS raw measurements API.

    The Nexus 9 tablet has duty cycling disabled in the forthcoming Android N 7.1 release, so it is suitable for collecting continuous carrier-phase measurements over periods of many minutes. A more detailed explanation of duty cycling is given in a subsequent section of this article.

    RAW GNSS MEASUREMENTS

    To get a first glance into the quality of the GNSS data provided by a smartphone, a 3-minute data set was collected on August 22, 2016, at the Googleplex, located in Mountain View, California. An engineering build of the Android N OS was used with the Samsung Galaxy S7 smartphone running the Broadcom 4774 GNSS chip. This device enabled logging of carrier-phase, Doppler and pseudorange measurements on the L1 signal for GPS, GLONASS, BeiDou, Galileo and QZSS. However, in the data processing described below, only GPS observations were used.

    The GNSS antenna contained within the smartphone uses linear polarization, making it especially susceptible to multipath effects resulting from GNSS signals bouncing off the ground or nearby surfaces before reaching the antenna. In the process of computing the observations, the GNSS receiver must discriminate between the direct signal and the reflected ones, resulting in noisier and possibly biased measurements.

    FIGURE 2 shows the carrier-to-noise-density ratio (C/N0) for the signal at the antenna input. Differences in the elevation angle of satellites above the horizon typically explains the differences of C/N0 values among satellites. The sudden sharp variations on all satellites simultaneously can be attributed to the operator touching the phone. The C/N0 values measured in this example are approximately 10 dB-Hz lower than typical values obtained from a geodetic-quality antenna and receiver, which, as we expect, impacts the quality of the smartphone measurements.

    For instance, consider GPS satellite G29 that had, on average, the highest C/N0 values in our data set. FIGURE 3 displays, in red, the error in the time variation of the pseudorange with respect to the carrier-phase measurements, computed by differencing both observables between adjacent 1-second epochs. It is clear that, even for the satellite with the strongest signal, the noise level is at the meter level and is about one order of magnitude larger than geodetic-quality measurements. The noise in the Doppler measurements can also be evaluated in a similar fashion, by comparing the mean Doppler value of two epochs with respect to the epoch-difference of carrier phases. Doppler measurements, useful in deriving the velocity of the user (speed and direction), show a much better performance with a precision at the level of a few centimeters per second.

    To obtain a better insight into how noisy measurements propagate into position estimates, we show the position errors in the north (latitude), east (longitude) and up (vertical) components in FIGURE 4. To mitigate satellite-related errors, we used precise satellite orbit and clock corrections computed at Natural Resources Canada (NRCan) instead of the broadcast values transmitted in the navigation message of the GPS satellites. Atmospheric delays affecting the propagation of the signals were also accounted for.

    The tropospheric delay was computed based on temperature and pressure values provided by the Global Pressure and Temperature (GPT) model, while the ionospheric delay was mitigated by using a global ionospheric map, also computed at NRCan. Additional error sources affecting GNSS observations were also accounted for, such as relativistic effects caused by the Earth’s rotation during signal propagation (a dekameter-level effect often referred to as the Sagnac effect) and the satellite orbit eccentricity (a meter-level effect). Earth tides resulting from the gravitational pull of the sun and the moon (a decimeter-level effect) were also considered, although this error source is not quite perceptible at this point. Measurement weighting was performed using the C/N0  values provided by the smartphone.

    Since the exact location of the smartphone is unknown, Figure 4 displays the position estimates with respect to the mean values for each component. With position dilution of precision (PDOP) values between 1.3 and 1.5, an indication of good satellite geometry, the meter-level precisions obtained reflect the quality of the pseudorange measurements. While a meter-level accuracy is sufficient for most applications such as car navigation or finding your friends, the purpose of our study is to determine if it is possible to improve on such results.

    As we have seen from Figure 3, Doppler measurements can provide a better estimate of the smartphone velocity. They can be incorporated into a positioning solution by adding velocity states (in the north, east and up directions) and by defining a maximum acceleration for the phone (in this case, it was set to a conservative value of 4.9 ms-2).

    FIGURE 5 shows the resulting solution, where the position has a much smoother variation due to the velocity information provided by the Doppler measurements. During the first few epochs, larger residuals for some satellites (at the meter level) were observed for the Doppler observations, which resulted in a poor velocity determination. The original csv format generated by the GnssLogger app also contained the precision of the Doppler observables, which could have allowed for the identification of these outliers, although this information was lost when translating this file to the RINEX format used by the positioning software.

    To turn the smartphone into a high-precision positioning tool, it is imperative to make use of carrier-phase measurements, which are at least 100 times more precise than pseudorange measurements. Since a GNSS receiver can only track the change in carrier phases, these measurements contain an unknown offset with respect to a true range measurement, referred to as a carrier-phase ambiguity. This offset is a constant value as long as the receiver continuously tracks the satellite.

    When obstructions such as trees, buildings, overpasses, and so on are present between the satellite and the GNSS receiver’s antenna, signal tracking interruptions are likely to occur. In this case, the initial offset value is changed and the carrier-phase ambiguity needs to be reset in the position filter. During poor signal tracking conditions, such as in urban canyons or under a tree canopy, carrier-phase measurements often suffer from many discontinuities and provide little to no benefit to the solution. However, with continuous signal tracking, a much more precise solution can be obtained.

    FIGURE 6 shows that the number of ambiguity resets in the data set collected were typically low, except for a few epochs where three or four satellites experienced simultaneous discontinuities. In such instances, it is likely that the solution will not be quite as stable as during continuous tracking on all satellites.

    To exploit the full potential of carrier-phase measurements, a careful modeling of all error sources must be achieved. In addition to the error sources discussed earlier, the so-called carrier-phase wind-up effect caused by the rotation of the satellite antennas as the satellites revolve around Earth was accounted for. High-precision GNSS processing strategies also typically include modeling of the user antenna phase-center variations, although this information is not yet available for smartphone antennas.

    As illustrated in FIGURE 7, including carrier-phase measurements in the positioning filter dramatically improved the precision of the position estimates. Notice that the scale of the y-axis has been reduced from ±15 meters in Figure 5 to ± 1 meter in Figure 7. At this point, it should be stressed that the solution is becoming precise, but is by no means accurate. With noisy pseudorange measurements and only three minutes of data, we are still expecting an accuracy of only a few meters. Nevertheless, the displacement measured by the GPS data is now closer to its expected value.

    Now, it is still not clear if some of the position fluctuations observed in Figure 7 are caused by the poor quality of carrier-phase measurements or by residual ionospheric effects. To answer this question, we extracted precise slant ionospheric delays from a nearby permanent GPS station operated by UNAVCO (formerly known as the University Navstar Consortium).

    This station, labeled SLAC, is located approximately 10 kilometers to the west of the Googleplex. The slightly more stable position estimates obtained, and shown in FIGURE 8, confirm that residual ionospheric errors contaminated the solution shown in Figure 7.

    These results demonstrate that, by using carrier-phase measurements and by carefully modeling the error sources affecting GPS observations, it is possible to derive a centimeter-level displacement of the smartphone. Noisier position estimates in Figure 8 correlate well with fluctuations in C/N0  presented in Figure 2 or the ambiguity resets identified in Figure 6, and highlights that careful handling of the phone is required for obtaining such results.

    One of the major challenges for smartphone manufacturers is to increase battery life. Since continuous use of the smartphone’s GNSS receiver would quickly drain the battery, the receiver employs a process known as duty cycling; for example, tracking GNSS signals for 200 milliseconds before shutting down for 800 milliseconds, then repeating.

    As you can imagine, it is not possible for the GNSS receiver to provide continuous carrier-phase measurements with duty cycling enabled. There is, however, an exception to this process: the receiver remains continually active while decoding the navigation message. From a cold start, it takes several minutes to decode the necessary parts of the message for the satellites in view, providing us with a few minutes of continuous carrier-phase tracking. This workaround was exploited to obtain the data set analyzed in this study, but is definitely not a viable option for real-life applications.

    The results presented so far demonstrate that, at this point, precise displacements can be estimated using raw GPS measurements from a smartphone. While this feature can be useful in some applications, it could also be desirable to obtain centimeter-level accuracies with a smartphone.

    So, what are the current limitations to performing real-time kinematic (RTK) positioning with smartphones? To answer this question, we need to invoke the concept of ambiguity resolution, the well-known technique in differential positioning allowing precise identification of the integer carrier-phase ambiguities. Ambiguity resolution is the key to centimeter-level accurate positioning since it effectively transforms carrier-phase measurements into very precise range measurements.

    However, single-epoch ambiguity resolution requires a very good (decimeter-level or better) initial position. It should be obvious when examining Figure 4 that this condition cannot be satisfied with the current quality of pseudorange measurements. The smartphone antenna is definitely the main culprit for this issue, and the use of an external antenna could be a viable, although cumbersome and expensive, solution. Another option for obtaining centimeter-level accuracies would be to average measurement noise for several minutes while benefiting from the continuity of carrier phases.

    In this case, duty cycling is certainly a barrier that needs to be addressed. Smartphone or tablet manufacturers could solve this issue by adding an option to disable duty cycling of the GNSS receiver, such as has been done on the Nexus 9 tablet.

    CONCLUSIONS

    The Android N operating system now allows us to access raw GNSS measurements from smartphones or tablets through various APIs. Making this data available opens up a world of possibilities to developers for the creation of new applications.

    In the study reported in this article, we examined the quality of the data with the purpose of deriving precise positioning information from a smartphone. Our preliminary results confirmed that noisy pseudorange observations can, at the moment, only provide meter-level accuracies. Nevertheless, the current quality of carrier-phase measurements can potentially allow for a precise (centimeter-level) displacement of a smartphone to be computed.

    There are still some obstacles preventing smartphones from competing with low-cost RTK units, namely the quality of the antenna and the duty cycling of the GNSS receiver. We hope that, by exposing these shortcomings, the scientific community will find solutions and improve on the results presented herein.

    Precise positioning with smartphones will also reveal a plethora of new issues associated with using these devices as high-precision instruments. For example, centimeter-level accuracies can only really be achieved after antenna phase centers have been characterized. Centering of the devices over the point of interest also needs further investigation. The handling of the phone to avoid signal blockages or measurement degradation certainly requires special attention. These areas offer lots of room for improvements and could very well mark the beginning of a new research era in high-precision GNSS positioning.

    ACKNOWLEDGMENTS

    We would like to thank Mohammed Khider and Daniel Estrada Alva of Google for creating and publishing the GnssLogger app. We also thank them and Lifu Tang, Marc Stogaitis, Steve Malkos and Wyatt Riley of Google for creating the GNSS raw measurement API. This article is published under the auspices of the NRCan Earth Sciences Sector as contribution number 20160169.

    SIMON BANVILLE has been working for the Canadian Geodetic Survey of Natural Resources Canada (NRCan) in Ottawa since 2010 as a senior geodetic engineer where he is involved in precise point positioning using global navigation satellite systems. He received his Ph.D. in 2014 from the Department of Geodesy and Geomatics Engineering at the University of New Brunswick, Canada, under the guidance of Richard B. Langley.

    FRANK VAN DIGGELEN leads the Android Location Team at Google in Mountain View, California. He is also a consulting professor at Stanford University, Stanford, California, where he created an online GPS course, offered free through Stanford University and Coursera. Van Diggelen is the inventor of coarse-time GNSS navigation, and co-inventor of the extended ephemeris concept for assisted-GNSS (A-GNSS). He holds over 80 issued U.S. patents on A-GNSS. He is the author of  A-GPS, the first textbook on A-GNSS. He received his Ph.D. in electrical engineering from Cambridge University, England.

     

    FURTHER READING

    • Google Announcement

    “User Location Takes Center Stage in New Android OS: Google to Provide Raw GNSS Measurements” by S. Malkos in GPS World, Vol. 27, No. 7, July 2016, p. 36.

    “Google Opens Up GNSS Pseudoranges” by A. Cameron. Online GPS World article.

    • Earlier Work on Smartphone Precise Positioning

    “Precise Positioning for the Mass Market” by T. Humphreys, K. Pesyna, D. Shepard, M. Murrian, C. Gonzalez and T. Novlan, keynote presentation at the International GNSS Service Workshop, GNSS Futures, Sydney, Australia, February 8–12, 2016. Available on line:  (video), (slides)

    “Low-Cost Precise Positioning Using a National GNSS Network” by M. Kirkko-Jaakkola, S. Söderholm, S. Honkala, H. Koivula, S. Nyberg and H. Kuusniemi in the Proceedings of ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Florida, Sept. 14–18, 2015, pp. 2570–2577.

    “Accuracy in the Palm of Your Hand: Centimeter Positioning with a Smartphone-Quality GNSS Antenna” by K.M. Pesyna, R.W. Heath and T.E. Humphreys in GPS World, Vol. 26, No. 2, February 2015, pp. 16–18 and 27–31.

    • Precise Point Positioning

    Improved Convergence for GNSS Precise Point Positioning by S. Banville, Ph.D. dissertation, Department of Geodesy and Geomatics Engineering, Technical Report No. 294, University of New Brunswick, Fredericton, New Brunswick, Canada, July 2014. Recipient of The Institute of Navigation Bradford W. Parkinson Award for 2014.

    “Precise Point Positioning: A Powerful Technique with a Promising Future” by S.B. Bisnath and Y. Gao in GPS World, Vol. 20, No. 4, April 2009, pp. 43–50.

    • Instant GPS Positioning

    “Coarse-Time Navigation: Instant GPS,” Chapter 4 in A-GPS: Assisted GPS, GNSS, and SBAS by F. van Diggelen, published by Artech House, Boston, Massachusetts, 2009.

  • Microsemi’s new time clock protects against GNSS vulnerabilities

    Microsemi’s new time clock protects against GNSS vulnerabilities

    Microsemi Corporation has announced its TimeSource Enhanced Primary Reference Time Clock (TimeSource Enhanced PRTC), a new system protecting against serious threats associated with GNSS vulnerabilities.

    It also enables telecommunications and mobile operators to meet the new G.8272.1 recommendation from the International Telecommunication Union (ITU). The stringent new ITU-T Recommendation G.8272.1 requires accuracy to within 30 nanoseconds (ns) or better when verified against a time standard such as UTC.

    Microsemi's new TimeSource Enhanced Primary Reference Time Clock protects against serious threats associated with GNSS vulnerabilities.
    Microsemi’s new TimeSource Enhanced Primary Reference Time Clock protects against serious threats associated with GNSS vulnerabilities.

    The TimeSource Enhanced PRTC “generates time” by producing its own independent time scale aligned with GNSS, while its phase, time and frequency signal outputs remain autonomous. This provides customers within the communications, power, public safety, data center and government network markets with a secure infrastructure, reducing dependency on GNSS and enabling network operators to retake control of the timing source used for network synchronization.

    “Worldwide telecommunications, power utilities and other infrastructure customers are in critical need of protection against GNSS vulnerability, and Microsemi’s new TimeSource Enhanced PRTC provides a powerful, high performance solution to address this need,” said Randy Brudzinski, vice president and business unit manager of Microsemi’s Frequency and Time division. “In addition, maintaining less than 30 ns performance is important to mobile operators who require a high level of accuracy to support LTE/4G and the upcoming deployment of 5G.”

    Massive deployment of GNSS as a timing source for synchronizing telecommunications networks (both wired and wireless) has created security risks to a point where governments, major telecommunications/mobile operators and enterprises are now urgently looking to protect their networks against both regional GNSS issues as well as the potential of a global GNSS outage. Microsemi’s TimeSource Enhanced PRTC works with the company’s cesium clocks to ensure time is generated in an autonomous manner. Specifically, the TimeSource Enhanced PRTC’s “source of time” aligns accurately with GNSS time without being dependent upon it—avoiding any vulnerability to threats caused by jamming and spoofing.

    According to Research and Markets’ report from market research firm Markets and Markets titled, “Anti-Jamming Market for GPS by Technique (Nulling System, Beam Steering System, Civilian System), Receiver Type (Military & Government Grade, Commercial Transportation Grade), Application, End User, and Geography – Global Forecast to 2022,” the anti-jamming market for GPS is expected to reach $4.8 billion and more than 309,000 units by 2022, at a compound annual growth rate (CAGR) of 7 percent and 10 percent, respectively, between 2016-2022. Demand for secured weapons guided systems and increasing vulnerability of GPS signals due to development of low-cost GPS jammers are the major growth drivers of the market.

    Microsemi’s technical experts will be showcasing the new TimeSource Enhanced PRTC, along with its integrated GNSS Grandmaster (IGM) 1100 series, SyncServerS600 series, TimeProvider 2700 and TimeProvider 5000, in booth #17 at the International Timing & Sync Forum (ITSF), taking place Nov. 1-3 in Prague, Czech Republic.

     

  • Oscilloquartz to unveil new synchronization technology

    Oscilloquartz, an ADVA Optical Networking company, will showcase vital new functionality for its synchronization and distribution technology at the 2016 International Timing & Sync Forum (ITSF) in Prague, Nov. 1-3.

    Demonstrations will reveal additional applications based on enhanced hardware and software in the OSA 5401 Syncplug, an ultra-compact Precision Time Protocol (PTP) grandmaster clock, and the OSA 5420 range of synchronization distribution and assurance devices optimized for edge deployment.

    The new feature set includes the OSA 5401’s capability to be used as a boundary or slave clock and the OSA 5420 series’ availability as a one-box solution for all timing protocols. The advancements will improve accuracy, security and cost-efficiency and create new use cases and deployment scenarios for Oscilloquartz’s timing technology.

    “With these latest innovations we can offer the ultimate timing solution. Thanks to improved security and resiliency, as well as different PTP profiles for different markets, our technology now caters for all synchronization requirements,” said Nir Laufer, director, product line management, Oscilloquartz.

    “Our enhanced OSA 5401 small form-factor pluggable grandmaster will bring major benefits to network operators. Its new slave and boundary clock functionality enables it to be used as an add-on, creating a hybrid synchronization network,” Laufer said.

    “Deployed this way, the OSA 5401 significantly reduces packet delay variation while taking up zero real estate and using very little power. It also protects against outages in the global navigation satellite system (GNSS), delivering superior frequency and phase with better resiliency throughout the network,” Laufer said.

    Further enhancements to the OSA 5401 include Layer 3 multi-cast functionality for financial and enterprise applications, as well as remote authentication and logs for improved manageability and security.

    Improvements have also been made to the OSA 5401’s GNSS capabilities, such as an elevation mask, a signal-to-noise ratio mask and fixed positioning, which enables more accurate time and frequency recovery, even in challenging environments such as urban canyon installations.

    Upgrades to the OSA 5420 series mean that it can now function as a high-capacity Network Time Protocol (NTP) server or PTP grandmaster in the same device, or even over the same port. What’s more, it can now support different types of line cards, including multiple 1Gbit/s ports used for PTP, NTP and Sync-E, as well as BITS, pulse-per-second, time-of-day and clock interfaces.

    This single box for all synchronization applications dramatically reduces cost and enables customers to protect their investment in NTP while planning future migration toward PTP.

    “We’re excited to present our latest advancements to the industry at ITSF. Our demos will show that we’ve created a complete synchronization solution — a family of devices that makes precise, resilient and affordable timing available for every industry,” said Gil Biran, general manager, Oscilloquartz.

    “With the OSA 5420 range, we’ve taken the Swiss army knife strategy even further, so that a single device now supports all sync technologies. It gives operators a one-box solution for overlay networks with different requirements,” Biran said. “With its NTP server and GNSS receiver capability, including multiple legacy and next-generation synchronization fan-out options, our OSA 5420 series is ideal for deployment in legacy synchronization architectures. It also offers the freedom to locate sync devices at any point in the network, which further reduces capital and operational expenditure.”

  • Telit offers new GNSS module

    Telit offers new GNSS module

    Telit Jupiter SE873 GNSS module.
    Telit Jupiter SE873 GNSS module.

    Telit‘s Jupiter SE873Q5 module is now available. The SE873Q5 is an ultra-low-power, high-sensitivity GNSS module with very small physical dimensions, completely compatible with its SE873 module.

    The new module leverages Telit innovation in miniaturization technology to improve power saving and sensitivity, delivering longer battery life and expanding design possibilities for tracking and navigation application areas particularly in wearable devices.

    The multi-constellation receiver module can be set to a number of different power saving modes depending on application requirements and includes an ultra-low noise boosting sensitivity that allows developers to explore a wider variety of device designs, enclosures and relative placement inside personal devices, garments or other space constrained electronics.

    The SE873Q5 is a flash-memory-based GNSS module capable of tracking three constellations simultaneously. Compared to the SE873, the new design reduces power consumption by 20%, while boosting satellite signal reception sensitivity. With complete pin-to-pin compatibility, the SE873Q5 can also be applied to existing designs based on the SE873, instantly boosting device performance as well as creating opportunities for new and upgraded products with very short time-to-market.

    “When it comes to application areas like wearables and others in the commercial and consumer spaces, there is no such thing as ‘too small’ or ‘too power-efficient’,” said Felix Marchal, Telit’s executive vice president of GNSS and short-range wireless. “And when you add to this type of efficiency, a stellar front-end RF performance in a miniature global satellite positioning receiver module, you immediately open up new product and business opportunities because now your antenna requirements are easier to meet; and you can explore more ‘buried’ designs where the module, used with an integrated antenna, can be encased deeper into the physical environment of the consumer, commercial or industrial application.”

    Features

    • The Jupiter SE873Q5 is packaged in a 7x7x1.85 mm QFN-like package, equipped SQI Flash memory, switching power supply and integrated high-performance low-noise amplifier (LNA). It is designed to support GPS, GLONASS, BeiDou and is Galileo-ready, delivering simultaneous tracking in two modes: GPS+Galileo and GLONASS, or GPS+Galileo and BeiDou.
    • To extend battery life, the module includes a low-power tracking mode as well as advanced low-power modes: SmartGNSS 1 and 2, duty cycle, push-to-fix. Because it is flash-memory-based, it enables easy firmware updates, customization of operating parameters and supports ephemeris file injection (A-GPS) for up to 14 days, resulting in faster TTFF.
    • Navigation data is delivered using OSP binary protocol or NMEA through standard UART, SPI or I2C ports. The module supports A-GPS as well as Satellite Based Augmentation System (SBAS) to increase position accuracy. Server-generated and client-generated extended ephemeris are supported and stored in internal Flash memory. The enhanced sensitivity of the SE873Q5 is rated at -147dBm for acquisition, -161dBm for navigation and -167dBm for tracking.

  • Russians seek answers to GPS anomaly in Moscow

    Mobile phone apps that use GPS are malfunctioning in Moscow, Russia, according to the Associated Press.

    According to research by programmer Grigory Bakunov, who works for Russian internet firm Yandex, a system for blocking GPS was located inside the Kremlin.

    GPS users in central Moscow have been complaining on social media that when they are near the Kremlin, their GPS apps stop working or show them to be in Moscow’s Vnukovo airport, which is 18 miles away, which suggests the purpose of the disruption may be to prevent drones flying over the Kremlin.

    Runners in September’s Moscow marathon also complained that their jogging apps lost track of how far they had run when they passed the Kremlin.

    Putin’s spokesman Dmitry Peskov said Thursday he did not know why the malfunction was occurring.

  • u-blox brings ARM mbed OS 5 to low-energy IoT modules

    u-blox brings ARM mbed OS 5 to low-energy IoT modules

    u-blox has announced ARM mbed OS 5 support for the ODIN-W2 wireless Internet of Things (IoT) gateway module and the NINA-B1 Bluetooth low energy module.

    The ARM mbed OS 5 incorporates a real-time operating system (RTOS) in the core of the operating system. This open environment provides all the features needed to rapidly deploy and develop connected IoT products based on an ARM Cortex-M microcontroller, including security, connectivity and drivers for sensors and I/O devices. On existing u-blox ODIN-W2 and NINA-B1 hardware, customers can now rapidly develop wireless IoT applications making it easier to cut costs, development time and footprint, the company.

    ublox-iot-ub066At ARM TechCon, held Oct. 26-27 in Santa Clara, California, u-blox will showcase the NINA-B1 and ODIN-W2 in a powerful and responsive IoT sensor-to-gateway-to-cloud scenario. In the live demonstration, the ODIN-W2 will read sensor data via Bluetooth low energy technology from NINA-B1-equipped sensors, and simultaneously stream it via Wi-Fi to an IBM cloud server, allowing for real-time data visualization and analysis.

    “The unique combination of u-blox’s ODIN-W2 and NINA-B1 with ARM mbed OS 5 creates the best performing complete IoT architecture solution for applications from sensor data collection to flexible intelligent gateway processing and cloud connectivity, with full support for customer code — all based on the most proven embedded application platform,” said Hakan Svegerud, head of Product Strategy, Short Range Radio, at u-blox.

    The already certified ODIN-W2, NINA-B1 and wireless stacks are designed for typical embedded applications, which require a small footprint implementation with responsive, high-performance hardware and an open CPU architecture. Usage scenarios include IoT, and medical and industrial applications, that use multiple sensors and/or smart gateways linked by Wi-Fi and Bluetooth. ODIN-W2’s 1-MB of flash memory offers ample space for all the customer software required to create a powerful gateway.

    The ARM mbed development environment, with its extensive tools and libraries, gives embedded application designers and developers full access to each u-blox module’s ARM Cortex-M4 based microcontroller. This allows them to avoid the need for an external host CPU — not only saving bill of materials costs and development resources, but also keeping the board footprint to an absolute minimum.

    In addition to providing a consistent development process across multiple products, this environment allows an extremely broad range of sensor and actuator-based IoT designs to be implemented on a single, compact, certified wireless module. Moreover, the u-blox open standard approach ensures ease of IoT connectivity, while still accelerating type approval certification by controlling access to the wireless stacks. All of these advantages combine to reduce time to market to a minimum.

  • Esri partners with Waze on open data-sharing for governments

    Esri partners with Waze on open data-sharing for governments

    Global mapping company Esri is partnering with Waze to make it easier for governments to begin building intelligent transportation systems in their communities.

    Waze enables users to share and harness the power of anonymous, aggregated data to promote greater transportation efficiency, deeper insight into travel conditions, and safer roads.

    Governments already using the Esri ArcGIS platform can quickly and easily exchange data through the Waze Connected Citizens Program, a free two-way data share of publicly available traffic information.

    Governments that have not already subscribed to Esri technology or joined the Waze Connected Citizens Program can sign up online to start sharing road closure alerts and other information with their citizens right away.

    Waze Esri Traffic Alerts.
    Waze Esri Traffic Alerts.

    “Municipalities can now leverage real-time reports without having to invest in sensor networks or an Internet of Things infrastructure,” said Andrew Stauffer, manager of civic technology at Esri. “Waze allows local governments to share open data with a purpose — in an application that is already popular with constituents, commuters, and tourists.”

    The data feeds allow local governments to merge information into existing enterprise systems, such as emergency dispatch and street maintenance systems, to make their communities operate smarter and safer.

    The partnership also enables communities to extend the reach of the data they map and manage by sharing it with Waze, which has more than 65 million monthly active users worldwide. The public-private partnership allows greater government transparency and collaboration with citizens to help people better navigate their streets and highways.

    “The Waze Connected Citizens Program empowers municipalities to harness real-time driver insight to improve congestion and make better informed planning decisions,” said Paige Fitzgerald, head of new business development and data acquisition for Waze. “With 100 partners worldwide, Waze provides each partner with the same set of free, data-driven tools and resources to foster collaboration and communication between all partners. Working with Esri allows Waze to further scale the program and creates additional opportunities for our partners to collaborate, helping each other incorporate the power of crowdsourced data into their traffic management strategies.”

    In 2014, Waze pioneered data standards for road closure and incident reporting, which are embedded within customized data feeds provided to each partner. Established as a two-way data share, Waze provides partners with real-time, anonymous, Waze-generated incident and slowdown information directly from the source: drivers themselves. In exchange, partners provide real-time, government-reported construction, crash, and road closure data to Waze to return one of the most thorough records of current road conditions.

    For more information on how to get started, visit go.esri.com/pr-waze.

  • MediaTek offers hardware development kit for wearables

    MediaTek offers hardware development kit for wearables

    MediaTek has announced the availability of the LinkIt 2523 Hardware Development Kit (HDK) for developers looking to create advanced wearable products. Based on the MediaTek MT2523G chipset, the HDK — produced by Silicon Application Corp (SAC) — offers dual-mode Bluetooth, comprehensive GNSS standards support and industry leading performance in terms of time-to-first-fix, accuracy and power consumption.

    The HDK is designed for developers looking to create sophisticated wearables such as smartwatches, fitness trackers, health monitors, emergency locators and more.

    mt2523-hdk
    MediaTek LinkIt 2523 HDK.

    This new HDK — the second for the LinkIt Development Platform for RTOS — takes advantage of the platform’s common tool chain and set of APIs. It offers developers the ability to create a range of IoT devices using a common software development kit, the MediaTek LinkIt SDK v4. With the HDK launch, the SDK has also been updated to include a new smaller, more efficient Bluetooth stack and various improvements to support the MT2523 chipset variants.

    The goal of the LinkIt Development Platform for RTOS is to make it easy for developers to create code that powers a range of wearables and IoT devices. The new HDK puts MediaTek leading technology in the hands of professional developers to create exciting and compelling wearable products.

    The LinkIt 2523 HDK is developed by SAC, one of MediaTek’s value-added resellers of chipsets and modules, using a hardware board reference design from MediaTek. The HDK provides an easy-to-use IoT development board for the design, prototyping, evaluation and implementation of commercial wearables projects.

    The board offers developers a range of essential features to ensure devices are market ready. Key features of the board include:

    • Robust connectivity with support for dual-mode Bluetooth 2.1 + EDR and Bluetooth 4.2 Low Energy with integral antenna.
    • Fast, low-power, high-precision positioning with integral HDK antenna for GPS and SMA connector for GLONASS, Galileo and BeiDou.
    • Integrated display and versatile peripheral options, including I2C, master and slave SPI, master and slave I2S, PCM, UART, 12-bit ADC and PWM which can be used for a broad range of wearables, including smartwatches, fitness trackers and wristbands incorporating various sensors.
    • Low power consumption achieved by highly integrated SoC using 55nm ultra-low power (ULP) technology from TSMC with Power Management Integrated Circuit (PMIC), and multiple frequency and voltage modes.
    • Flexible power options for external battery and USB (5V) charger.
    • Additional onboard memory (eMMC) or the option to use a microSD card.

    Key features of the LinkIt Development Platform for RTOS include:

    • Based on the popular FreeRTOS with additional open-source modules (source code available).
    • Supports chipsets based on the ARM Cortex-M4 architecture, offering high performance, low power connectivity.
    • Supports multiple chipsets/hardware, including the MT7687F Wi-Fi SoC and the MT2523 Bluetooth/GNSS chipset family.
    • Development and debugging in ARM Keil μVision, IAR Embedded Workbench and GCC.
  • Nikken Lease uses u-blox positioning for trackable pallet

    Nikken Lease uses u-blox positioning for trackable pallet

    Japan-based Nikken Lease has tapped into positioning and cellular technologies from u-blox for its new trackable pallet, Transeeker.

    transeeker-pallet-u-bloxTranseeker is a pallet equipped with u-blox’s cellular and positioning technologies for accurate tracking: u-blox cellular UMTS/HSPA(+) module LISA-U200-62S and u-blox 7 standalone GNSS module, EVA-7M.

    For the first time, Nikken Lease is offering its GNSS-enabled pallets as a rental service. Made of plastic, the pallets are reusable, making them a good ecological alternative to their wooden counterpart.

    “When we designed Transeeker, we wanted to ensure that we could locate a pallet precisely and retrieve it nationwide at low costs for our customers,” said Tsumura, planning director for Nikken Lease. “u-blox is just the right partner with excellent product performance and unique features such as CellLocate.”

    Embedded in Transeeker, the LISA-U200-62S of the LISA-U2 series is equipped with CellLocate, u-blox’s proprietary hybrid positioning technology enabling stand-alone location estimation based on surrounding GSM/UMTS cell information in conjunction with GPS positioning data.

    The module also offers worldwide W CDMA(UMTS) and GPRS/EDGE coverage, and an easy migration to u-blox GSM/GPRS, CDMA and LTE modules. Also found in Transeeker, the EVA-7M single GNSS module features the reliable performance of the u-blox 7 positioning engine (receiving GPS, GLONASS, QZSS and SBAS signals) and delivers high sensitivity and minimal acquisition times in the ultra-compact EVA form factor.

    “This collaboration with Nikken Lease should help us strengthen our position in the Japanese market as a key player for IoT applications,” explains Tesshu Naka, Country Manager of u-blox Japan. “We are looking forward to more collaboration with Nikken Lease.”

  • Precision GNSS in phones, drones and cars forecast by 2021

    UAV-opening-O

    Low-cost, precision GNSS receivers will become a reality in the driverless car, drone and even smartphone markets by 2021, finds ABI Research. The automotive industry will be the main driver behind precision GNSS receiver adoption, in which centimeter-level accuracy is essential to complete driver safety systems with the redundancy necessary for autonomous vehicles.

    “There is a variety of competing technologies currently under investigation by the automotive industry, but ABI Research forecasts it will move to a hybridized approach, combining LIDAR, HD maps, sensor fusion, machine vision and precision GNSS,” says Patrick Connolly, principal analyst. “As the receivers’ average selling price drops below $50, we expect to see a more immediate market for location technology services, such as AR Heads Up Displays (HUDs), in high-end vehicles. Vehicle-to-Vehicle, or V2V, communication might constitute another use case for high-precision GNSS.”

    In addition to autonomous vehicles, the report also identifies opportunities for low-cost, precision GNSS receivers in autonomous unmanned vehicles (AUVs), as well as commercial and consumer devices. Though the average selling prices of such GNSS receivers is $1,000 and higher, ABI Research finds the cost to be one of the most addressable inhibitors to market growth today.

    “Precision GNSS achieves sub-meter accuracy through a variety of methods, including a network of reference stations,” Connolly says. “The biggest question mark today is not cost-related, but instead how to achieve reliable, worldwide satellite navigation coverage to support correction techniques, such as real time kinematic, or RTK, and precise point positioning, or PPP. This is an extremely expensive undertaking, with currently no guarantee of a return on investment.”

    Competition in the location technologies market ranges from crowdfunded startups to Internet giants, reflecting the scale of the opportunity. Traditional precision GNSS receiver vendors like NovAtel have the intellectual property, engineering experience and ownership of correction networks.

    In the consumer GNSS receiver market, u-Blox and Skytraq lead the way, according to the report. Each developed low-cost single frequency PPP and RTK receivers, with a clear roadmap toward dual-frequency. Other consumer GNSS providers, like ST Microelectronics, Broadcom and Qualcomm, also appear active in this space.

    Start-ups like North Surveying, NVS Technologies, REACH, and Swift Navigation continue to disrupt the industry, bringing low-cost precision receivers to market, said ABI Research.  Their goal is to hit an ASP below $100 in the near future. And Radiosense is a startup that received a lot of attention for its previous work concerning precision GNSS on smartphones. It is now working on automotive solutions in a pilot in Austin, Texas.

    Locata has the potential to be the wildcard in the deck, working on a powerful synchronization and location technology that may find its way into consumer technologies by 2021.

    “Most interesting in the location technology competitive landscape is the involvement of Internet giants Google and Alibaba,” concludes Connolly. “Google recently announced it will make GPS pseudoranges available to developers, which, although extremely nascent, could open up the door for a lot of innovation. And in China, Alibaba is a major partner in the roll-out of Continuous Operating Reference Stations, or CORS, networks in the region.”

    These findings are from ABI Research’s Precision GNSS in Automotive and GNSS IC Design Trends: Modules, Standalone, Combo, and Embedded reports.

  • Launchpad: TraceME TM-178 is upgraded with LoRa technology

    Tracking with options

    The TraceME module TM-178, targeted for tracing and controlling vehicles and other powered equipment, is now upgraded with optional LoRa, Wi-Fi, Bluetooth Smart (BLE), ANT/ANT+ and proprietary RF. The upgrades enable integration with existing wireless networks and specific custom mobile apps on smartphones and tablets.

    productheader-r9hc-kcsbv-wThe KCS BV LoRa technology offers a communication range up to 60 kilometers, line of sight. The module offers an advanced indoor and outdoor location-based positioning solution, which covers a variety of Internet of Things (IoT) applications and enables stolen object or vehicle recovery.

    The TM-178 is equipped with external power and battery backup connection, basic I/O-connectivity and multiple on-board sensors. The unit contains multiple integrated antennas for GPS/GLONASS, GSM (2G/3G) and RF functionality. The functionality of the module can be remotely programmed to fit any job. From basic/general functionality to advanced/low-level application specific detailed functionality.

    With a compact size of 91 x 40 millimeters and weighing 30 grams, along with a battery lifespan of more than 10 years, the module offers endless OEM integration possibilities. Optionally, the module can be ordered in a robust IP67 housing.

    TM-178 Features

    • GPS
    • GSM/GPRS/EDGE coverage
    • Basic I/O-connectivity
    • Long-range RF coverage

    Optional Features

    • GPS + GLONASS
    • UMTS/HSPA+
    • LoRa
    • Bluetooth Smart (BLE), ANT/ANT+, iBeacon
    • Wi-Fi
    • Robust IP67 housing
    • External RF antennas
    • Internal battery, no need for external power supply

    KCS BV, www.trace.me

  • HellaPHY wireless positioning better than 50 meters for IoT

    Acorn Technologies Inc., a semiconductor and wireless technology company focused on the Internet of Things (IoT), has developed and  demonstrated new wireless long-term evolution (LTE) positioning technology for the location of things. The LTE location-based technology meets the new Enhanced 911 (E911) mandate performance requirements and performs well in very low bandwidth conditions. HellaPHY technology provides better than 50-meter accuracy for next generation location of things in the machine-type communications (MTC) and IoT markets.

    Location of devices acts as an organizing principle for anything connected to the internet, helping organize the billions of internet-connected devices based on the sensors and other location-centric elements in them. The installed base of IoT endpoints will grow to more than 25 billion in 2019, hitting 30 billion in 2020, according to a recent IoT forecast.

    “We are achieving accuracy in low bandwidth scenarios,” says Steven Caliguri, VP of wireless products at Acorn Technologies. “We believe that our advanced LTE positioning solution is the lowest complexity, lowest cost and lowest power solution available today for LTE based applications from high-end smartphones to loT.”

    Acorn has demonstrated better than 50-meter accuracy in live network testing of their user equipment (UE)-based positioning algorithms for low bandwidth CAT-M devices. (Cat-M refers to Category M, the second generation of LTE chipsets meant for IoT applications.) The network tests were conducted on a network that has not been fully optimized for LTE-based positioning.  Further gains are expected when optimizations begin to rollout.

    Acorn’s network testing has demonstrated the ability to exceed the 2021 E911 mandated performance requirements even in low-bandwidth scenarios.

    The technology has been developed from the core hellaPHY Channel Estimation algorithm that employs machine-learning techniques. The positioning algorithms are  suited for IoT applications due to their extremely low complexity, and require less then 10 kilobytes of memory and only a fraction of a low-end DSP during the maximum processing interval. It has further proven to exceed the performance of super resolution algorithms at a fraction of the complexity.

    HellaPHY RSTD is an advanced signal processing algorithm that was developed to improve LTE wireless network indoor and outdoor location accuracy. It is designed to be a drop-in replacement for existing Reference Signal Time Difference (RSTD) algorithms in UE chipsets and can be customized for any unique DSP or interface requirements. The hellaPHY RSTD IP core is designed to support advanced LTE features contemplated by operators as well as for LTE Release 14 including Positioning Reference Signals (PRS) muting, Cell-Specific Reference Signal (CRS) plus PRS transmit diversity, and fractional Ts reporting. The hellaPHY RSTD IP core is scalable and can support CAT-M through CAT-15.

    (” … the ubiquitous parameter Ts. This nameless parameter is the most basic unit of time in the LTE air interface and pretty much everything in the LTE frame structure is based on multiples of this basic time unit, capital “T”, sub small “s”. Ts is defined exactly as: Ts = 1/(15000 x 2048) seconds, a little more than 32 nano-seconds.”

    — from LTEuniversity.com)

    Acorn Technologies is a provider of performance scaling semiconductor and wireless intellectual property for the Internet of Things. With nearly 200 patents issued and pending, Acorn’s IP addresses the fundamental building blocks with algorithms for wireless and IoT. The company’s semiconductor IP portfolio includes buried silicon stressors and metal insulator silicon  technologies to significantly boost semiconductor transistor performance.