The eclipse across America on Aug. 21 was not only a magnificent visual event, it was also observed indirectly by the impact that it had on the propagation of radio signals — including those of global navigation satellite systems.
There was a decrease in the number of free electrons in the part of the Earth’s ionosphere along the eclipse path where sunlight was temporarily blocked by the moon. While not as significant as the daily variation as day turns to night, the effect was clearly seen in the signals received on the ground from GPS satellites.
GPS signals are routinely used to monitor the behavior of the ionosphere. The density of electrons in the ionosphere affects the speed of propagation of radio signals and this effect is slightly different at different frequencies.
By combining measurements made on the L1 and L2 legacy signals transmitted by all GPS satellites using high-grade receivers, scientists and engineers can measure the total electron content (TEC), which is the number of electrons in a column with a cross-sectional area of one meter squared along the path of the signal from satellite to receiver.
This value can then be projected to the vertical direction using a simple equation. Given the large number of electrons in the column, we measure the TEC in TEC units (TECU), where 1 TECU = 1016 electrons per square meter.
TEC time series from two continuously operating GPS monitoring stations near the path of totality, BREW at Brewster, Washington, and NISA at Boulder, Colorado, show a small dip of about 2 TECU or so around 18:00 UTC on Aug. 21, coincident with the timing of the eclipse. These time series are illustrated in FIGURES 1 and 2. Also shown in the figures are the time series for the day before, Aug. 20, which just show the normal diurnal ionospheric variation.
Figure 1. Time series of vertical total electron content observed using all GPS satellites observed at Brewster, Washington, on Aug. 21, 2017, the day of the eclipse (in blue) and the time series from the previous day, Aug. 20., 2017, for comparison (in red).Figure 2. Time series of vertical total electron content observed using all GPS satellites observed at Boulder, Colorado, on Aug. 21, 2017, the day of the eclipse (in blue) and the time series from the previous day, Aug. 20., 2017, for comparison (in red).
The effect of the eclipse was also be seen in the real-time correction data transmitted by the U.S. Wide-Area Augmentation System (WAAS) using geostationary satellites.
WAAS provides enhanced accuracy, integrity and availability for GPS single-frequency users using a network of dual-frequency GPS receivers all across North America. Corrections include a grid of ionospheric propagation delay values, updated every 5 minutes, which are used to account for the delay in receiver measurements.
FIGURE 3 shows part of the grid transmitted by WAAS and the path of totality across the U.S. Three of the grid points are close to the path and the time series of delay values of these points are shown in FIGURE 4.
Figure 3. Map showing the locations of a subset of the grid points used for the WAAS ionospheric delay corrections highlighting the three grid points close to the eclipse path of totality used to examine the effect of the eclipse along with one grid point far removed from the path for comparison.Figure 4. Time series of ionospheric vertical delay values of three WAAS ionospheric grid points along the eclipse path of totality on Aug. 21, 2017, along with the values from a grid point far removed from the path.
We see clear dips in values of up to about 50 centimeters. This is equivalent to what we see in the TEC time series from the BREW and NISA monitor stations since 1 TECU equates to 16 centimeters of propagation delay at the GPS L1 frequency.
Furthermore, the times of the dips correspond to the times of totality as the eclipse quickly moved across the country from west to east. Also shown for comparison in Figure 4 are the delay values for a grid point far removed from the path of totality, which show only the normal diurnal variation.
Not only does a total eclipse mesmerize the general public, it excites many scientists and engineers, too. A number of university research groups organized special eclipse observing campaigns to collect data from GPS receivers as well as other ionospheric monitoring tools to better understand exactly how the ionosphere reacts to a total eclipse of the sun.
And although we expect future analysis of the data will show features of great interest to science, the immediate results from the total eclipse of Aug. 21 show no significant impacts on the position, navigation and timing service GPS provides.
GPS “weathered” the eclipse with flying colors.
(Attila Komjathy, Siddharth Krishnamoorthy, Anthony J. Mannucci, Lawrence C. Sparks, Lawrence E. Young and Giorgio Savastano from the NASA Jet Propulsion Laboratory operated by the California Institute of Technology; Gerald W. Bawden from NASA HQ Earth Science Division; and Hyun-Ho Rho and Richard B. Langley from the University of New Brunswick, Fredericton, Canada, contributed to this article.)
This month we bring you a guest column by Steffen Thoelert, André Hauschild, Peter Steigenberger and Oliver Montenbruck of the German Aerospace Center (DLR) and Richard B. Langley of the University of New Brunswick.
UPDATE:Since Sept. 10, continuously operating DLR receivers in Sydney, Australia, and Chofu, Japan, have been reporting measurements from QZSS satellite J07, which, according to the QZSS Interface Control Document, is the geostationary satellite QZS-3.
The second satellite of Japan’s Quasi-Zenith Satellite System (QZSS) has started transmitting navigation signals. QZS-2, or Michibiki-2, was launched on June 1, 2017, and joins its predecessor QZS-1 (Michibiki-1), which has been in orbit since September 2010.
Both satellites have been placed into inclined geosynchronous, elliptical orbits, which enable extended satellite visibility periods over Japan and are characteristic features for this regional navigation system.
The third satellite, QZS-3, was launched on Aug. 19, 2017, into a geostationary orbit. If all goes according to plan, a fourth satellite in an eccentric orbit will follow by the end of this year and complete the constellation.
QZS-2 Signal Tracking
It is not straightforward to tell when QZS-2 started signal transmission exactly. About four weeks after launch, on June 27 between 10:17 and 12:37 UTC, several Septentrio PolaRx GNSS receivers in the Asia-Pacific region recorded continuous L5 observations. About one week later, on July 4 shortly after 03:02 UTC, Javad and Trimble receivers picked up L1 C/A and L5 signals from QZS-2 for a few seconds. Then again, between 23:03 UTC on July 6, and 01:36 UTC on July 7, several receivers intermittently tracked the L1 C/A, L2C and L5 signals. Finally, on July 10, starting at approximately 01:03 UTC, these three signals were continuously tracked until approximately 04:00 UTC on July 12. Up until Aug. 1, signal tracking had remained intermittent, but has been stable since. This was presumably the result of interruptions in the signal transmission due to test activities.
Figure 1. QZS-2 signals tracked by GNSS receivers in Chofu, Japan, (top plot) and Sydney, Australia, (bottom plot). The plots depict the measured C/N0 for L1 C/A (black), L2C (red) and L5 (green) together with the observed pseudorange (grey). The frequent discontinuities in the pseudorange are due to the receiver clock adjustments. Both receivers exhibited a short tracking outage at approximately 06:00 UTC. The interruption in tracking at Chofu around 08:00 UTC is due to the low elevation angle of the satellite.
The plots in FIGURE 1 show QZS-2 signals as tracked by GNSS receivers in Japan and Australia on July 10. The two first sets of broadcast messages were transmitted on July 16 at 6:00 and 7:00 UTC. Regular transmission of broadcast ephemerides started on July 27 at 22:00 UTC, but deviations from the hourly update rate still occur from time to time.
Identical or Fraternal Twins?
At first glance, QZS-2 seems like a look-alike of QZS-1, but there are many differences between the two spacecraft. Most apparent is the presence of an additional auxiliary antenna. Like QZS-1, QZS-2 transmits its navigation signals on the L1, L2, L5 and L-band Experiment (LEX) frequencies through the main antenna, while the augmentation signal L1S (formally known as Submeter-class Augmentation with Integrity Function or SAIF) is transmitted from a separate antenna. However, the new L5S signal, which is introduced with QZS-2, is transmitted with yet another antenna.
The new satellite also has a shorter “wingspan” of only 19 meters, since it is equipped with two solar panel segments on each side, compared to three segments for QZS-1 with a width of 25.3 meters. The second QZSS satellite also follows a different attitude model: Unlike QZS-1, which switches between yaw-steering mode and orbit-normal mode depending on the sun’s elevation angle with respect to the orbit plane, QZS-2 always remains yaw-steering except for short periods of time when orbit maneuvers are performed. Further differences will become apparent in the analysis of the signal spectra in the subsequent sections.
The Cabinet Office of the Government of Japan, which oversees QZSS as a national undertaking, has published QZSS satellite metadata information on its official website. At the time of writing, only one document for QZS-2 is available, which contains information about the satellite’s properties such as mass, dimension, attitude law and reference frame, but also antenna and laser retroreflector positions, antenna phase-center offsets and variations as well as signal group delays.
Additional documents containing metadata for QZS-1, -3 and -4 and further information about QZS-2 are in preparation.
Rubidium Clock
FIGURE 2 illustrates the stability of the QZS-2 rubidium atomic frequency standard (RAFS) by means of the Allan deviation (ADEV). Data from a global network of 150 GNSS stations was processed to estimate GPS and QZSS satellite orbit and clock parameters.
Figure 2. Allan deviation of the rubidium atomic frequency standards of GPS Block IIF satellite G32, QZS-1 (J01) and QZS-2 (J02).
However, whereas about 60 of these stations provide QZS-1 observations, QZS-2 is only tracked by 13 stations. ADEV values for QZS-1, QZS-2 and a GPS Block IIF satellite were computed from a daily solution for Aug. 3 with 30-second clock sampling.
At an integration time of 100 seconds, the QZS RAFS reaches an ADEV of better than 3 × 10-13.
At longer integration times, the QZS-2 clock almost reaches the stability of the GPS Block IIF RAFS.
Based on this preliminary analysis for only one day, the QZS-2 clock seems to perform as expected. The larger ADEV values compared to QZS-1 for integration times up to 1,000 seconds might be attributed to the significantly smaller number of tracking stations contributing to the QZS-2 clock solution. The quality of the clock solution will improve as soon as more stations are able to track QZS-2.
Signals with High-Gain Antenna
Complementary to the receiver measurements and analysis, the German Aerospace Center (DLR) has also recorded raw spectral and in-phase and quadrature (IQ) data of QZS-2 to get further insights into the transmitted signal structure and initial signal quality. FIGURE 3 shows a spectral measurement of the complete GNSS L-band frequency range, which shows the signal transmissions of QZS-2 in the L1, L2, L5 and L6 bands. The signal was captured with DLR’s 30-meter high-gain antenna at Weilheim, southwest of Munich, operated by DLR’s German Space Operations Center.
Figure 3. QSZ-2 L-band normalized power spectra recorded at Weilheim, Germany, on July 18, 2017 at 20:43 UTC.
This first view of the signal transmission shows a good spectral shape, appropriate band filtering and no out-of-band unwanted spurious emissions of the satellite. For further analysis, we looked closer at each signal-band spectrum and performed IQ-sample recording.
Comparing the QZS-2 spectra to that of QZS-1, we see differences in the signal structure for the L1 frequency band.
Figure 4. QZS-1 and QZS-2 L1 spectral flux density.
FIGURE 4 shows the L1 spectra of both satellites. The additional signal component can be seen at an offset of 6 x 1.023 MHz and 18 x 1.023 MHz from the L1 center frequency of 1575.42 MHz. This is the result of the new L1C-pilot modulation, which is based on the time-multiplexed binary offset carrier (TMBOC) modulation technique using a mixture of BOC(1,1) and BOC(6,1). See here for detailed information.
Another difference is present in the L6 band and can be seen within the signal time domain or the IQ domain. The new satellite transmits two components (one each for the I- and Q-channels) while QZS-1 transmits only one I-component. This observation is fully in line with the QZSS Interface Specification. On QSZ-2, an additional L6 signal component (Centimeter-Level Augmentation Message for Experiments, L6E) is implemented. FIGURE 5 shows the IQ constellation plots of QZS-1 and QZS-2 for the L6 band.
Figure 5. L6 IQ constellation plots for QZS-1 (left) and QZS-1(right).
Source: GPS World
Furthermore, the L5 band IQ plot of QZS-2 exhibits significant differences compared to QZS-1. These differences, which are illustrated in the plots of FIGURE 6, are due to an additional L5S signal transmitted by QZS-2.
Figure 6. L5 IQ constellation plots for QZS-1 (left) and QS-2 (right).
Source: GPS World
The QZS-2 L5 IQ diagram is fairly easy to understand as a coherent superposition of two distinct quadrature signals from two antennas. One signal is the GPS-like L5 signal transmitted from the main L-band antenna, while the other (L5S) signal originates from a new L5S antenna. This is illustrated in FIGURE 7.
Figure 7. QZS-2 L5 IQ constellation plot including demarcation of the L5 and L5S signals.
For illustration purposes, the dashed orange square in Figure 7 relates to the 10 MHz L5 signal, while the smaller red squares are the 10 MHz L5S signal.
A code generator has been setup according the QZSS L5 and L5S interface control document (ICD). An analysis of the correlations of possible pseudorandom noise (PRN) codes resulted in the detection of PRN 194 and PRN 196. Based on the information in the ICDs, PRN 194 is used for L5 and PRN 196 is used for L5S.
The performed code correlation analysis also yields the finding that the L5 signal is approximately 3.5 dB stronger than the L5S signal. Note, however, that both signals have a specified minimum receive power of -157 dBW. Due to the limited visibility of QZSS satellites from the Weilheim ground station, it is not possible to verify this value.
Conclusion
With the launch and activation of QZS–2, the deployment of Japan’s regional navigation system is moving forward again. The launch of a geostationary satellite, QZS-3, took place on Aug. 18. A fourth Japanese navigation satellite is scheduled to launch later this year. With this rapid sequence, the target date of 2018 for the completion of an operational constellation with four satellites is quite realistic.
Steffen Thoelert, André Hauschild, Peter Steigenberger and Oliver Montenbruck are from the German Aerospace Center (DLR).
Richard B. Langley is from the University of New Brunswick and authors the monthly Innovation column for GPS World magazine.
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
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.
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.
FIGURE 2. Carrier-to-noise-density ratio for II tracked GPS satellites.
FIGURE 3. Pseudorange rate and Doppler errors of GPS satellite G29 with respect to carrier-phase measurements.
FIGURE 4. Position estimates using pseudorange data only. The plotted estimates are differences with respect to the mean values in each component.
FIGURE 5 Position estimates using pseudorange and Doppler data. The plotted estimates are differences with respect to the mean values in each component.
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.
FIGURE 6. Number of carrier-phase ambiguity resets and number of satellites used in the solution.
FIGURE 7. Position estimates using pseudorange, Doppler and carrier-phase data. The plotted estimates are differences with respect to the mean values in each component.
FIGURE 8. Position estimates as in Figure 7 but with precise external ionospheric information.
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.
“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.
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.
“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.
The development and performance of the VeraPhase GNSS antenna
By Julien Hautcoeur, Ronald H. Johnston and Gyles Panther
INNOVATION INSIGHTS with Richard Langley
ANTENNAS MATTER. Often overlooked by the casual user of a GNSS receiver, its antenna is a critical component of the system. In the case of consumer equipment such as handheld receivers, satellite navigation units and embedded devices inside smartphones, cameras and fitness monitors, the antenna might not even be visible. Nevertheless, a GNSS antenna must be carefully designed and constructed to maximize the transfer of the electromagnetic energy of the weak GNSS signals into an electrical current that can be fed to the receiver. Typically, this means that the antenna has to be designed for reception of the right-hand circularly polarized signals transmitted by the satellites on their particular frequency or frequencies. Some mass-produced embedded devices might use less efficient linearly polarized antennas coupled with a high-sensitivity receiver simply to shave a few cents off the cost of the units or to fit them into a limited volume. But the pros and cons of such antennas is a discussion for another time.
A GNSS antenna must also be omnidirectional, being able to receive signals arriving from any azimuth and elevation angle with acceptable gain in the hemisphere above the antenna while rejecting those signals arriving from below the antenna that, in most cases, are undesirable reflections off the ground and which have a large left-hand circularly polarized component. Reflected signals from the ground or other surfaces combine with the line-of-sight signals from the satellites resulting in multipath interference, which contaminates pseudorange and carrier-phase measurements. The first line of defense against multipath is a multipath-resistant antenna. Signals from non-GNSS transmitters on nearby frequencies should also be rejected so as not to cause interference to the receiver or overload its front end.
An important characteristic for precision GNSS applications is stable electrical phase centers—the locations in three-dimensional space to which GNSS measurements are referenced. Ideally, they would be perfectly fixed with respect to the antenna housing but, in reality, they will vary with the direction of the arriving GNSS signals. The variation, however, should be small, repeatable and calibrated with the calibration values available for data-processing software.
It was about 40 years ago when the first GPS receiving antennas were developed and there have been many significant advances in antenna design and fabrication since then. You might be tempted to think that there is nothing new in the research and development of GNSS antennas. You would be wrong.
In this month’s column, we take a look at a revolutionary design of a multi-frequency multi-GNSS antenna. Our authors discuss how the antenna evolved from a research project in academia to a commercial product about to enter the market. And, like a number of GNSS advances, it’s Canadian, eh?
The use of GNSS technology has permeated many aspects of life today. With each advancement in the technology, new applications become possible as a result of lowered costs, smaller size, greater capabilities, and higher precision and accuracy. In particular, advances in antenna technology can provide greater capabilities to GNSS receiving equipment.
In this article, we report on the research and commercial development of a high-performance GNSS antenna that can cover all of the GNSS frequency bands, that has high purity circularly polarized radiation, high phase-center stability and high radiation efficiency. Early numerical simulations showed that the turnstile/cup antenna was a good starting point for this research. For GNSS applications, this antenna type required much further research to extend the impedance bandwidth, to reduce cross-polarization and to reduce backward radiation. Many thousands of electromagnetic (EM) computer simulations and optimizations of various circular waveguide (or cup) structures led to a high-performance circularly polarized antenna.
This antenna has excellent axial ratios in all theta and phi directions, low backward radiation, excellent phase-center stability and a compact design. Intermediate and final antenna designs were extensively tested in the anechoic chamber of the Schulich School of Engineering at the University of Calgary. Our company subsequently signed a license agreement with the University of Calgary’s University Technologies International Inc. and undertook further development of the antenna for commercial production. In this article, we present measured results for the resulting commercial antenna known as the Tallysman VeraPhase VP6000 antenna.
Early Circularly Polarized Antennas. One of the first circularly polarized antenna designs (1948) can be attributed to Sichak and Milazzo (see Further Reading), who introduced the turnstile or crossed-dipole circular polarization (CP) antenna. The crossed dipoles must have current flows that are 90 degrees out of phase with each other. This phase difference can be achieved feeding the two dipoles 90 degrees out of phase by a phase-shifting signal splitter or by changing the impedance of each of the dipoles. The turnstile antenna produces highly pure CP only in the two directions normal to the two dipoles. If the dipoles are normal to each other and lie in the horizontal plane, they can radiate right-hand circular polarization (RHCP) upwards while left-hand circular polarization (LHCP) is radiated downwards. At the horizon, they will radiate only a linear horizontally polarized wave. For GNSS applications, this is a serious limitation. By 1973, it was known that a horizontal dipole placed near the open face of a “cup” or shorted waveguide would radiate a linear horizontally polarized wave sideways and a vertically polarized wave in its direction of alignment. These properties were utilized by Epis (see Further Reading) to build a broadband CP antenna.
RESEARCH OBJECTIVE
The university research project began with the objective of developing a high-precision GNSS antenna that would cover all of the frequency bands being considered by the various national GNSS satellite systems, whether launched or under development. It was decided at the onset of the research that computer simulation and optimization methods would be an important part of the research endeavor. Many antenna structures were evaluated using EM simulation tools. Various structures were constructed in software and then simulated. Early simulations indicated that the crossed dipole placed in a cup offered the best possibility for producing a high-performance GNSS antenna. To obtain the best RHCP with minimal LHCP, it became necessary to place the dipoles somewhat within the cup. Nevertheless, the impedance bandwidth of this configuration is insufficient to handle the upper and lower GNSS frequency bands at the same time.
Extending the Antenna Bandwidth. The first structure that was used to handle both the L1 and L2 GNSS bands was a second set of dipoles connected in parallel to the first set. This arrangement provided an adequate match to frequencies close to the L1 band (1575 MHz) and the L2 band (1227 MHz) but it gave a rapidly changing reflection coefficient close to and below the L1 band. The two dipole sets were fed by an appropriate surface-mount 90-degree hybrid coupler designed for the required broad frequency band. The dipoles are fed by microstrip via “grounded legs” that are built on printed circuit board (PCB) technology. Good performance was achieved with this structure, but further improvements in the performance were actively sought. The two dipoles connected directly together cause a deep notch in the radiated signal at a frequency close to and below the L1 band. This was considered to be undesirable. It was decided to use a coupled resonant radiating structure tuned to L1 while the main dipoles would be tuned to L2 (see FIGURE 1).
FIGURE 1. An extended bandwidth GNSS antenna. The lower and connected dipoles are tuned to L2 and the upper coupled shorted dipoles are tuned to L1. Current flow in the circular waveguide of the GNSS antenna is shown. Strong circumferential currents flow at the top of the waveguide. Red indicates large currents and the arrows show the directions of the current flow. (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)
It is well known that resonant circuits can be broadbanded by choosing the correct coupling between them. This was tried in software and found to give an excellent wideband response.
Circumferential Current Reduction. Through many EM simulations of the antenna structure, it was found that the LHCP could be suppressed substantially by making the aperture of the cup serrated. The EM wave simulation package allows the user to look at the currents in the structure. The results are shown in FIGURE 2.
FIGURE 2. An antenna with a tapered base and a sawtooth aperture, which reduces circumferential current flow. (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)
The strong circumferential currents (horizontal linear currents) produce radiation with linear horizontal polarization. It is important to reduce the size of these currents to minimize the linearly polarized radiation. The horizontal currents flowing in the top of the waveguide wall are effective in setting up horizontal polarization (HP) radiation in the direction of the horizon. For high-quality CP radiation, the horizontal radiation must be matched by vertical radiation (with a 90-degree phase shift), but the waveguide wall does not permit the required vertical current to flow to produce the vertical polarization (VP) radiation component. Clearly, a serrated waveguide aperture reduces the circumferential current flow. It was also found, through many simulations, that the unwanted polarization components can be reduced by tapering the cup towards the bottom end (see Figure 2).
The sawtooth aperture antenna was chosen for further development. The fed dipoles are constructed using PCB technology and are given shapes that vary from the wire dipole case. The radiating resonator is also constructed using PCB material and is given a different shape from the pure straight-wire case. The software antenna was constructed and tested and found to have good performance with regard to low cross polarization in all directions, low backward radiation and high radiation efficiency.
Further Waveguide Development. It was decided that another way of achieving vertical currents and horizontal currents that would be balanced in magnitude and have a 90-degree phase difference might be obtained by constructing the waveguide walls from a combination of thin conductors connected in a grid. The grid consists of a combination of vertical and horizontal conductors. Simulations with EM software showed the antenna is exceptionally efficient when it uses wires. The wire grid waveguide model of the GNSS antenna was simulated with many, many topological variations. Each variation was optimized for low back (nadir) radiation and high-purity RHCP in all directions. The results were unexpected. The best results were obtained when only one circumferential wire conductor is used and, furthermore, the vertical wire conductors are not connected to the circumferential conductor nor to the base of the antenna. This structure was simulated and optimized many times to derive the best possible topological configuration and component dimensions for a GNSS antenna. A PCB model of the GNSS antenna was then numerically constructed, simulated and optimized as a more practical construction technology for the antenna (see FIGURE 3).
FIGURE 3. The conducting plate waveguide model of the GNSS antenna. The blue plates are conducting sheets and the yellow plates are the dielectric of the PCB. (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)
Note that the vertical strip conductors do not contact the conducting antenna base. Also note the serrated antenna base, as seen on the inside of the antenna. This design feature reduces excessive circumferential current flow in the base of the antenna. The antenna was tested in the University of Calgary anechoic chamber and in the high-quality Simon Fraser University anechoic chamber (a Satimo SG64), and it was found to have well-suppressed LHCP radiation, very low back radiation and very stable phase centers.
The unique topology of this last antenna provides suppression of the expected downward LHCP radiation that most CP antennas exhibit. Radiation tends to “spill over” from the aperture and travel downwards. Downward radiation also emerges from the gap between the antenna base and the vertical conductors. These two sources of downward radiation are largely out of phase and tend to cancel each other out. This reduced downward LHCP radiation largely removes the need for a choke ring to block the reflections from the ground. This in turn means that the antenna can be compact and light.
We undertook the project of converting the research prototype antenna described above into a commercially viable product. The research prototype antenna was modified to achieve optimized gain at lower GNSS frequencies, high mechanical robustness, adaptation for efficient manufacturability and for use of different materials. This antenna is known as the VeraPhase VP6000 antenna and is shown in FIGURE 4.
The topology of the antenna follows that of the research prototype with dimensional adjustments so as to function correctly with the new materials and circuitry being used. It is light and compact with a diameter of 157 millimeters, a height of 137 millimeters and a weight of less than 670 grams.
VeraPhase Measurements. Anechoic chamber tests were conducted at the Satimo facility in Kennesaw, Georgia, to determine the gain pattern, axial ratio, phase-center offset and variation in multipath-free conditions. Data were collected from 1160 MHz to 1610 MHz to cover all the GNSS frequencies.
Antenna Gain, Efficiency and Roll-off. The chamber measurements show that the VP6000 exhibits a gain at zenith from 4.9 dBic at 1164 MHz to 7.05 dBic at 1610 MHz (see FIGURE 5). This high gain in combination with a wideband pre-filtered low-noise amplifier (LNA) with a noise figure of 2 dB provides for high carrier-to-noise density (C/N0) ratios for all GNSS frequencies. Furthermore, the VP6000 exhibits gain at the horizon from –4.4 dBic at 1164 MHz to –6.8 dBic at 1610 MHz (see Figure 5).
FIGURE 5. RHCP gain of the VP6000 at zenith and the horizon at all GNSS frequencies. (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)
Thus, the gain roll-off from zenith to horizon is between 10.1 dB and 13.6 dB, providing for good tracking at low elevation angles. The radiation efficiency of the VP6000 is 70 percent to 80 percent, corresponding to an inherent (“hidden”) loss of just 1 dB to 1.5 dB, which includes all feedline, matching circuit and 90-degree hybrid coupler losses. In contrast, spiral antennas usually exhibit an inherent efficiency loss of close to 4 dB in the lower GNSS frequencies. Thus, with a high performance LNA, high values of gain translate into higher C/N0 ratios.
FIGURE 6. Normalized radiation patterns of the VP6000 on 60 phi cuts of the GPS frequency bands. (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)
Radiation Patterns. The radiation pattern of an idealized antenna would have pure CP and constant high gain from zenith down to the horizon and then roll off rapidly for elevation angles below the horizon. In a realizable antenna, the gain should be close to constant over all azimuths for each elevation angle, with strong cross-polarization rejection over that frequency range. The phase-center offset should be stable with minimal phase-center variation. In the upper hemisphere, the greater the difference between the RHCP and LHCP antenna gain, the greater the resistance of the antenna to cross-polarized signals, usually associated with odd order reflections, and hence improved multipath signal rejection. The measured radiation patterns at GPS frequencies are shown in FIGURE 6.
The radiation patterns are normalized to enable direct comparison of the patterns and show the RHCP and LHCP gains on 60 azimuth cuts three degrees apart. The radiation patterns show excellent suppression of the LHCP signals in the upper hemisphere. Similar results were found for all the other GNSS frequencies. The difference between the RHCP gain and the LHCP gain at zenith ensures an excellent discrimination ranging from 31 dB to 53 dB. Also, for the other elevation angles the LHCP signals usually stay 25 dB below the maximum RHCP gain and even 30 dB from 1200 MHz to 1580 MHz. The antenna shows a constant amplitude response to signals coming at a constant elevation angle regardless of the azimuth or bearing angle. This illustrates the excellent multipath mitigation characteristics of the VP6000 at every elevation angle and every GNSS frequency.
Down-Up Ratio. When a direct satellite signal is reflected from the ground, the reflected signal polarization tends to convert, at least partially, from RHCP to LHCP for most soil types. If the terrain underneath the antenna is homogeneous, then the ground surface acts as a mirror, thus providing a reflected signal coming from below the horizon at the negative of the angle of the direct signal above the horizon. Depending on the angle, in part, the field of the inverted and reflected wave adds to the direct wave, which is undesirable. This is the reason, when characterizing the multipath reflection capabilities of an antenna, it is common to use a down-up ratio between antenna gain for LHCP signals for a given angle below the horizon as that for the RHCP signals at the same angle above the horizon. The down-up ratios at L2 and L1 are –25 dB at zenith and they stay under –20 dB for the upper hemisphere, which is usually not the case for standard GNSS antennas. Similar results have been measured over the whole range of GNSS frequencies and confirm the excellent multipath rejection capabilities of the VP6000.
Axial Ratio. The axial ratio (AR) is a measure of an antenna’s ability to reject the cross-polarized portion of a composite signal with both RHCP and LHCP components. Physically, this is an elliptical wave, typically being the combination of the direct and reflected signals from the satellite. The lower the ratio of the major axis to the minor axis of the polarization ellipse, the better the multipath rejection capability of the antenna. To meet operational standards for a multi-band antenna, the axial ratio should meet these requirements at the following elevation angles:
45–90 degrees: not to exceed 3 dB
15–45 degrees: not to exceed 6 dB
5–15 degrees: not to exceed 8 dB.
The worst AR ratio values of the VP6000 at different elevation angles have been plotted in FIGURE 7. The graph shows an AR of less than 0.5 dB at zenith for all GNSS frequencies, and the ARs stay low at all elevation angles down to the horizon. A maximum value of 1.5 dB has been measured for elevation angles above 30 degrees, increasing to just 2 dB at the horizon (0 degree elevation angle) for the worst case azimuth. This performance contributes to the excellent multipath rejection capability of the VP6000.
FIGURE 7. Worst case of axial ratios of the VP6000 at different elevation angles: 90 degrees (zenith), 30 degrees, 10 degrees and 0 degrees (horizon). (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)
Phase-Center Offset / Phase-Center Variation and Absolute Calibration. For use as a measurement instrument, the antenna must have a precise origin, equivalent to a tape measure zero mark. Thus, it is important that the phase of the waves received by the antenna “appear” to arrive at a single point that is independent of the elevation angle and azimuth of the incoming wave. This point is known as the phase center of the antenna, which should remain fixed for all operational frequencies and for all azimuth and elevation angles of incoming waves, otherwise dimensional measurement is compromised.
In an ideal GNSS antenna, the phase center would correspond exactly with the physical center of the antenna housing. In practice, it varies with the changing azimuth and elevation angle of the satellite signal. The difference between the electrical phase center and an accessible location amenable to measurement on the antenna is described by the phase-center offset (PCO) and phase-center variation (PCV) parameters and their values are determined through antenna calibration.
These corrections are only effective if the predicted phase-center movement is repeatable for all antennas of the same model. The PCO is calculated for each measured elevation angle by considering the signal phase output for all phi (azimuth) values at a specific theta (elevation) angle, and mathematical removal of the normal phase-windup effect in this type of antenna.
A Fourier analysis is then conducted on this resulting data. The fundamental output gives the variation of the horizontal position of the antenna as it is rotated about the z axis. The apparent position normally varies somewhat as the antenna is viewed from various theta angles. The PCV measurement of the VP6000 showed the variation of the phase center in the horizontal plane for elevation angles of 18 to 90 degrees in 3-degree steps at different frequencies. The variations for the different GNSS signals are typically less than 1 millimeter from the x and y axes. Repeatability of the PCO and PCV over several VP6000 antennas has been measured and is also less than 0.5 millimeters.
Five copies of the antenna were sent for absolute calibration by Geo++ in Germany where the VP6000 has been calibrated at GPS L1/L2 and GLONASS G1/G2 signal frequencies. The PCV for the upper hemisphere of the VP6000 at L1 and L2 are plotted in FIGURES 8 and 9. These results confirm a ±1-millimeter PCV at L1 and a ±1-millimeter PCV at L2. Also the standard deviation of the PCV over the five measured antennas stayed under 0.2 millimeters, which represents excellent repeatability. The same results have been observed at G1 and G2.
FIGURE 8. Phase-center variation at L1. The same results have been observed at G1. (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)FIGURE 9. Phase-center variation at L2. The same results have been observed at G2. (Image: Julien Hautcoeur, Ronald H. Johnston and Gyles Panther)
LNA and Optional Circuitry. The best achievable C/N0 for signals with marginal power flux density is limited by the efficiency of each antenna element, the gain and the overall receiver noise figure. This can be quantified by a ratio parameter, usually referred to as G/T, where G is the antenna gain (in a specific direction) and T is the effective noise temperature of the receiver — usually dominated by the noise figure of the input LNA.
In the VP6000 LNA, the received signal is split into the lower GNSS frequencies (from 1160 MHz to 1300 MHz) and the higher GNSS frequencies (from 1525 MHz to 1610 MHz) in a diplexer connected directly to the antenna terminals and then pre-filtered in each band. This is where the high gain and high efficiency of the basic VP6000 antenna element provides a starting advantage, since the losses introduced by the diplexer and filters are offset by the higher antenna gain, thereby preserving the all-important G/T ratio.
That being said, GNSS receivers must accommodate a crowded RF spectrum, and there are a number of high-level, potentially interfering signals that can saturate and desensitize GNSS receivers. These include, for example, the Industrial, Scientific and Medical (ISM) band signals and mobile phone signals, particularly Long-Term Evolution (LTE) signals in the newer 700-MHz band, which are a hazard because of the potential for harmonic generation in the GNSS LNA. Other potentially interfering signals include Globalstar (1610 MHz to 1618.25 MHz) and Iridium (1616 MHz to 1626 MHz) because they are high-power uplink signals and particularly close in frequency to GLONASS signals. The VP6000 LNA is a compromise between ultimate sensitivity and ultimate interference rejection.
A first defensive measure in the VP6000 LNA is the addition of multi-element bandpass filters at the antenna element terminals (ahead of the LNA). These have a typical insertion loss of 1 dB because of their tight passband and steep rejection characteristics. Sadly, there is no free lunch, and the LNA noise figure is increased approximately by the additional filter-insertion loss.
The second defensive measure in the VP6000 LNA is the use of an LNA with high linearity, which is achieved without any significant increase in LNA power consumption, by use of LNA chips that employ negative feedback to provide well-controlled impedance and gain over a very wide bandwidth with considerably improved linearity.
Bear in mind that while an installation might initially be determined to have an uncluttered environment, subsequent introduction of new services may change this, so interference defenses are prudent even in a clean environment. A potentially undesirable side effect of tight pre-filters is the possible dispersion that can result from variable group delay across the filter passband. Thus it is important to include these criteria in selection of suitable pre-filters. The filters in the VP6000 LNA give rise to a maximum variation of 2 nanoseconds in group delay over the lower GNSS frequencies (from 1160 MHz to 1300 MHz) and 2.5 nanoseconds over the higher GNSS frequencies (from 1525 MHz to 1610 MHz). Also, the difference in group delay between the lower GNSS frequencies and the higher GNSS frequencies stays less than 5 nanoseconds.
The VP6000 series antennas are available with either a 35-dB gain LNA or with a 50-dB gain LNA for installations with long coaxial cable runs. The VP6000 is internally regulated to allow a supply voltage from 2.7 volts to 26 volts.
An interesting feature of the VP6000 is that the physical housing includes a secondary shielded PCB that is available for integration of custom circuits or systems within the antenna. This allows the addition of L1/L2 receivers for real-time kinematic operation, for example. A pre-filtered, 15-dB pre-amp version of the LNA is also available to provide RF input for OEM systems embedded within the antenna housing.
The VP6000 is available with a variety of connectors and with a conical radome to shed ice and snow and to deter birds for reference antenna installations. A precise and robust monument mount is also available.
CONCLUSION
In this article, we have described a research program that developed a series of CP antennas, which have increasingly improved performances directed towards GNSS applications. The resulting research CP prototype antenna has a very low cross-polarization, very low back radiation, very high phase-center stability and a compact structure. We have converted the research prototype into a commercially viable GNSS antenna with the superior electrical properties of the research prototype while building into the antenna the required physical ruggedness and manufacturability required of the commercial antenna.
With emerging satellite systems on the horizon, a new high-performance antenna is needed to encompass all GNSS signals. Our new antenna has sufficient bandwidth to receive all existing and currently planned GNSS signals, while providing high performance standards. Testing of the antenna has shown that the new innovative design (crossed driven dipoles associated with a coupled radiating element combined with a high performance LNA) has good performance, especially with respect to axial ratios, cross-polarization discrimination and phase-center variation.
These improvements make the antenna an ideal candidate for low-elevation-angle tracking. The reception of the proposed new signals along with additional low-elevation-angle satellites will bring new levels of positional accuracy to reference networks, and benefits to the end users of the data. With its compact size and light weight, the antenna has been designed and built for durability and will stand the test of time, even in the harshest of environments.
ACKNOWLEDGMENT
This article is based, in part, on the paper “The Evolutionary Development and Performance of the VeraPhase GNSS Antenna” presented at the 2016 International Technical Meeting of The Institute of Navigation held in Monterey, California, Jan. 25–28, 2016.
JULIEN HAUTCOEUR graduated in electronics systems engineering and industrial informatics from the Ecole Polytechnique de l’Université de Nantes, Nantes, France, and received a master’s degree in radio communications systems and electronics in 2007 and a Ph.D. degree in signal processing and telecommunications from the Institute of Electronics and Telecommunications of Université de Rennes 1, Rennes, France, in 2011. From 2011 to 2013, he obtained postdoctoral training with the Université du Québec en Outaouais, Gatineau, Canada. In 2014, he joined Tallysman Wireless Inc. in Ottawa, Canada, as an antenna and RF engineer.
RONALD H. JOHNSTON received a B.Sc. from the University of Alberta, Edmonton, Canada, in 1961 and the Ph.D. and D.I.C. from the University of London and Imperial College (both in London, U.K.) respectively, in 1967. In 1970, he joined the University of Calgary, Canada, and has held assistant to full professor positions and was the head of the Department of Electrical and Computer Engineering from 1997 to 2002. He became professor emeritus in the Schulich School of Engineering in 2006.
GYLES PANTHER is a technology industry veteran with more than 40 years of engineering, corporate management and entrepreneurial experience. He spent the first 20 years of his career in the semiconductor industry, first with Plessey in the U.K., then in Canada with Microsystems International. Panther co-founded and acted as engineering vice president and chief technology officer (CTO) for Siltronics, followed by SilCom and SiGem. In 2002, he founded startup Wi-Sys Communications, acting as president and CTO. He is now president and CTO of Tallysman Wireless, his fourth successful start-up, which was founded in 2009. Panther holds an honours degree in applied physics from City University, London, U.K.
FURTHER READING
Authors’ Conference Paper
“The Evolutionary Development and Performance of the VeraPhase GNSS Antenna” by J. Hautcoeur, R.H. Johnston and G. Panther in Proceedings of ITM 2016, the 2016 International Technical Meeting of The Institute of Navigation, Monterey, California, Jan. 25–28, 2016, pp. 771–783.
“Antennas for Circular Polarizations” by W. Sichak and S. Milazzo in Proceedings of the Institute of Radio Engineers, Vol. 36, No. 8, Aug. 1948, pp. 997–1001, doi: 10.1109/JRPROC.1948.231947.
Antenna Modeling
Electromagnetic Modeling of Composite Metallic and Dielectric Structures by B.M. Kolundzija and A.R. Djordjevi, published by Artech House, Norwood, Massachusetts, 2002.
WIPL-D: Electromagnetic Modeling of Composite Metallic and Dielectric Structures – Software and User’s Manual by B.M. Kolundzija, J.S. Ognjanovic and T.K. Sarkar, published by Artech House, Norwood, Massachusetts, 2000.
Measurement of Phase Center and Other Antenna Characteristics
“Determining the Three-Dimensional Phase Center of an Antenna” by Y. Chen and R.G.Vaughan in Proceedings of the XXXIth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI), Beijing, Aug. 16–23, 2014, doi: 10.1109/URSIGASS.2014.6929023.
Using a smartphone for GNSS ionospheric data collection
By Andrew Kennedy, Ryan Kingsbury, Anthea Coster, Victor Pankratius, Philip. J. Erickson, Paulo Roberto Fagundes, Eurico R. de Paula, Kerri Cahoy and Juha Vierinen
INNOVATION INSIGHTS with Richard Langley
DO YOU REMEMBER YOUR FIRST PERSONAL COMPUTER? I do.
It was a Timex Sinclair 1000. Released in 1982, it used a Zilog Z80A processor running at 3.25 MHz and sported a whopping two kilobytes of memory and a wonky membrane keyboard. You had to hook it up to a tape recorder to record and load programs (in BASIC) and it used a TV tuned to channel 3 or 4 as a display device. We’ve certainly come a long way in the past almost 35 years. Now, I have a computer I can hold in my hand with more than one thousand times the computing power and more than one million times the memory and a built-in interactive display. It’s an Apple iPhone 5S smartphone. I am one of the billions of owners of a smartphone. In 2015 alone, almost 1.5 billion smartphones were sold worldwide.
We use our smartphones for a wide range of tasks. Besides voice phone calls, we use them to text, to wake us up, to listen to our tunes, to watch movies, to take photographs and videos, to surf the Web, to navigate. The list goes on and on. In 2015, there were about 1.5 million applications or apps available for both Apple and Android smartphones.Those with the ability can even program their smartphones to perform tasks specific to their lifestyles, hobbies, or professions.
In this month’s column, we take a look at the use of a smartphone app to collect GNSS ionospheric data. Why would you want to do that?
In the experience of the developers of the app, GNSS receivers are often characterized by a complex, proprietary data interface that differs for each manufacturer. In practice, this leads to significant investments in understanding interfaces and software tools. Human operators must familiarize themselves with the commands used to configure each receiver as well as with proprietary graphical user interfaces and tools specific to each receiver. The authors’ app-centric approach provides a software framework and output format that remain the same for different receivers. Receiver-specific commands are configurable within the app, so users can easily attach new receivers while reusing the existing infrastructures for data collection and processing. And smartphones have more than enough power and connectivity to do the job and can be easily moved from site to site.
The smartphone as a handheld device to help scientists study the ionosphere? Probably not even Clive Sinclair foresaw that.
Continuous and high-resolution dual-frequency GNSS observations are required to capture the ionospheric response to external forcing from events such as the 2011 Tohoku-Oki earthquake and tsunami or the 2003 Halloween geomagnetic storms that severely impacted the U.S. Federal Aviation Administration’s Wide Area Augmentation System. These events, as well as other natural and man-made disasters, have been shown to produce structure of various scales in the ionospheric total electron content (TEC). TEC estimates can be directly derived from dual-frequency GNSS observations and so these observations are a valuable source of information about the ionosphere.
However, with the exception of a few areas such as Japan, where the GNSS Earth Observation Network (GEONET) has an average spacing of 5 kilometers, the density of ground-based GNSS sensors needed to capture displacements of the ionosphere is lacking. This is primarily due to data acquisition costs. Networks on the order of 50-kilometer spacing would provide the density of coverage needed to capture the propagation of medium-scale traveling ionospheric disturbances (TIDs), which have horizontal wavelengths of up to hundreds of kilometers and speeds of hundreds of meters per second. Irregularity structures in the polar regions may require even denser networks to capture the fine-grained auroral structures.
The Mahali project, supported by the U.S. National Science Foundation, aims to improve the ability of the GNSS community to perform large-scale science by facilitating increases in the density of required sensors. The “last mile data transport” problem remains critical, and Mahali explores new ways to efficiently and effectively move data from the many types of GNSS receivers deployed across the world to the cloud, at affordable cost. “Kila Mahali” means “everywhere” in the Swahili language, a term that epitomizes the project’s ambitions for data collection.
A short-term objective of Mahali is to demonstrate the utility of mobile phones as low-cost preprocessors and relays that transport TEC observation data to cloud-computing environments for more advanced processing and storage. We eventually envision an “ecosystem” of open-source software, which includes various smartphone tools that aid researchers in interfacing with GNSS sensors.
In this article, we present one such smartphone software application (hereafter denoted “app”) from the Mahali software ecosystem that researchers can install on their Android smartphones. They can then link the smartphone directly to a dual-frequency GNSS receiver over a USB port. Thus, data can be immediately collected, pre-processed on the smartphone, and sent to cloud storage environments like Dropbox whenever an Internet connection is available. This approach tests out a building block for large-scale data-collection networks, which can grow incrementally by adding more GNSS receivers and smartphones.
Smartphones for science
Modern mobile processors offer ever-increasing computing capabilities. For example, Nvidia offers mobile multicore processors with four central-processing-unit cores and more than 60 graphics-processing-unit cores on a single chip.
While most mobile applications are leveraging this power for multimedia, photography or gaming, these hardware capabilities are now available for scientific data processing. Everyday smartphones like the Samsung Galaxy S5 smartphone have a quad-core processor running at 2.5 GHz, 2 GB of random-access memory, and a variety of sensor and network connectivity options.
The smartphone also features reliable backhaul via Wi-Fi and the world’s ever-growing cellular data network, qualities highly relevant for scientific applications. Even in Africa, there was an average of 60 mobile-cellular subscriptions per 100 inhabitants in 2012 according to the International Telecommunication Union. Smartphones therefore have a significant advantage over other platforms for large-scale, distributed applications.
Today’s smartphones are typically only equipped with single-frequency GNSS receivers, and thus it is not yet possible to entirely replace dual-frequency GNSS receivers by smartphones running a data-collection app. To make the necessary scientific measurements to recover TEC, receivers require dual-frequency tracking capability. In the current work, we focus primarily on using smartphones as data-collection and relay devices. We anticipate, however, that future consumer demands, such as precision navigation, will eventually push dual-frequency capabilities into next-generation mobile devices. In that case, our app would not need to be connected to external receivers, but instead would use the smartphone’s internal receiver.
Mahali GNSS Logger App
This section describes the Mahali GNSS Logger App we developed at the Massachusetts Institute of Technology (MIT) to collect data from a GNSS receiver and relay scientific data to the cloud.
Setup. The app interfaces with a GNSS receiver over a USB-to-serial connector, as shown in FIGURES 1 and 2. It collects observation data output from the receiver, and stores it to files on local storage on the smartphone. The app allows the uplink of data files to a cloud-based storage medium available through the Internet, where further data processing and analysis can be performed. For our evaluation, we demonstrate this uplink by interfacing with Dropbox, a widely used cloud data storage service.
Figure 1. Smartphone and a USB-to-serial adapter.Figure 2. Android smartphone connected to a GNSS receiver over a USB-to-serial adapter.
The GNSS data logger app facilitates the process of collecting GNSS data from a variety of commercially available receivers. The app was developed in the Java/Android programming language for deployment on mobile devices running Google’s Android operating system (OS).
Usage scenarios. Figure 3 illustrates the concept of operations for a scenario involving multiple smartphones and GNSS receivers. Data is initially generated by each GNSS receiver (step 1). A smartphone connected to each receiver runs the app (step 2) and gathers the data on local storage. After establishing a connection to a cloud-based server, the app acts as a relay and transmits the local data to the cloud (step 3).
Figure 3. Concept of operations for the Mahali GNSS Logger App involving data collection from multiple receiver types.
The app is intended for usage scenarios in which a particular smartphone connects to a single GNSS receiver. The app collects the data from the serial output of the receiver and forwards that data to a cloud-based storage location for subsequent analysis. We focused primarily on Dropbox for this purpose, used through Android’s “share” interface. In addition, the app can also configure the GNSS receiver by issuing specific commands on the serial port.
User interface. The app was structured to provide a convenient user interface for the quick commencement of data-collection sessions and upload of data files to the cloud. FIGURE 4 illustrates the typical user interface that a scientist would see when starting the app, and FIGURE 5 shows how scientists can configure commands that the smartphone issues to initialize a GNSS device.
Figure 4. Main screen of the Mahali GNSS Logger App.Figure 5. Command configuration screen for the GNSS receiver initialization.
The “Edit GPS Config” button (item 1 in Figure 4) allows access to a basic text editor screen (shown in Figure 5), which lists a series of ASCII character commands that are sent to the GNSS receiver upon commencement of a data-collection session. This approach lets a user configure the app to work with different types of GNSS receivers.
The “Session control” toggle button (item 2 in Figure 4) allows the user to start and stop a data-collection session. When a session is created, it is assigned a file name using a UTC time tag from the smartphone’s clock and a file extension corresponding to the GNSS receiver type. The file is stored in a dedicated directory in the smartphone’s external bulk memory (such as an SD card). This directory location is defined within the app at a set location.
The real-time status display (item 3 in Figure 4) shows the name of the current session file and the number of bytes that have been collected from the receiver interface and saved to the file.
The scrollable “Previous Sessions” display (item 4 in Figure 4) lists all previous session files found in the external storage directory. The user can tap on any session within the list to upload the file to the cloud. The user can delete session files from a submenu accessible through the three dots at the top of the screen.
File formats. The app currently logs data in a binary format that is dependent on the particular GNSS receiver. An example is the “nvd” format shown in Figure 4. Once the data is in the cloud, a variety of software tools are available to convert these files to other formats, such as the widely used RINEX format.
Currently, our post-processing of “nvd” files stored in the cloud is done in a custom Python script that converts them to the RINEX format in batch mode. To validate the generated RINEX format, we use the “TEQC” tool provided by UNAVCO.
Software architecture. The Android OS implements “threads” as a way to let users run multiple tasks at the same time, to manage multiple user interface updates, or to perform various background actions. “Activity” threads handle a user’s interaction with the main screen and GNSS configuration screens. Other app-specific threads are spawned by the main activity thread in response to user prompts. The spawned threads perform specific actions asynchronously in the background, so the user can continue to interact with the app while uploads are in progress.
In particular, the main activity thread handles the user’s interaction with the main screen. The activity calls the appropriate software functions that respond to button taps. The main thread also updates the user interface with the latest status information and manages the creation of new threads for serial input/output (I/O) as well as uploads to Dropbox.
The GNSS receiver config activity thread presents the user with a light-weight text editor, which captures all necessary GNSS receiver configuration commands. In the current version of the app, these commands are permanently stored in the smartphone internal bulk memory using the Android SharedPreferences module. This can be easily extended in the future, such as to store and download command configuration files to and from the cloud.
When a user toggles the “Session Control” button (in other words, a “Start Session” event), the serial I/O manager thread starts storing all the bytes received from a GNSS receiver to a GNSS session file. The bytes are read from the smartphone’s USB serial data interface and written to a file in binary format. The file and its properties are represented internally by a “GNSS Session” object. The file itself is a raw byte file; it is formatted in exactly the same way that the GNSS receiver outputs data. When interfacing with one modern multi-GNSS receiver specifically designed for scintillation studies and TEC monitoring, every hour of data collected took about 18 MB of storage. We have not yet tested the app’s performance at extremely high data output rates from a GNSS receiver, but we expect that it should be able to support all standard serial data rates.
When a user stops the data collection, the main activity updates the “Previous Sessions” list with a new session. The code ensures that at most one serial I/O manager thread is created, that is, a GNSS receiver data stream can only be logged to one session file at a time.
A DB (Dropbox) upload task is created upon user prompt. The task sends the selected session file to a directory within a Dropbox account specified by the user. The first time a user attempts file upload, the app obtains the necessary account authorization from the user.
Testing in the field
To test the app and the Mahali system concept, field trials were conducted from January to February 2015 in Brazil at the sites shown in TABLE 1. Data were collected from one multi-GNSS scintillation receiver, and two older GPS scintillation receivers, using two types of Android smartphones.
Table 1. Summary of app field test sites.
We chose Brazil for our test because it is in a region of significant interest for space weather studies. Manaus, one of the sites visited, is located at the magnetic dip latitude 5.1° N. São José dos Campos, the other site visited, is located south of the geomagnetic equator at a dip latitude of 18.9° S and is within the equatorial, or Appleton, anomaly region. This anomaly region, consisting of enhanced TEC, forms 10 to 20 degrees north and south of the geomagnetic equator due to the well-known “ionization fountain” effect. During geomagnetic storms, electric fields of magnetospheric origin can penetrate into the equatorial region and directly influence ionospheric density, neutral composition and temperature at low latitudes.
Geomagnetic storms generate large-scale gravity waves that propagate from high to low latitudes. Because of Brazil’s location in the tropics, gravity waves associated with tropospheric convection patterns can also propagate upwards producing a myriad of small- to medium-scale TIDs. Finally, it is suspected that the South Atlantic Anomaly, a region of weakened geomagnetic field that falls over Brazil, exerts considerable influence on the development of space weather phenomena in both hemispheres. Large day-to-night and day-to-day variations in TEC are frequently observed in this region. For all of these reasons, having a dense pattern of GNSS observations from this region is of significant scientific interest.
Our test campaign was primarily motivated by a desire to test the utility of the smartphone-based solution and to demonstrate the feasibility of easily setting up remote field sites for the purpose of filling in gaps in data coverage. During this campaign, we collected data at the three sites listed in Table 1 using three different receivers. About 220 MB of data were collected in total at all of the sites.
Table 1 summarizes the relevant information about the field test sites. The first field site visited was the Universidade Luterana do Brasil (ULBRA) campus in Manaus on Jan. 30, 2015. This site is in close proximity to the geomagnetic equator. Because the receiver at the ULBRA campus is involved in ongoing scientific observations, we were only able to collect data for a short period of time, approximately an hour in total. Nevertheless, we were able to attach the smartphone, configure the GNSS receiver to produce the appropriate data products and start data collection all within about 10 minutes. In total, only 20 minutes of GPS data were collected at this location, but the experience demonstrated how quickly the app-based solution can be installed.
The second and third field sites visited were near São José dos Campos on the campuses of the Instituto Nacional de Pesquisas Espaciais (INPE) and Universidade do Vale do Paraíba (UNIVAP). São José dos Campos is well to the south of the geomagnetic equator and in the Appleton anomaly region. We successfully collected observations from both sites, but were only able to conduct long-duration testing at the UNIVAP site (approximately 9.5 hours in total). This data is shown in FIGURE 6, in total electron content units (TECu = 1016 electrons per square meter) in the bottom plot (with the four-letter site name SAUN).
Figure 6. Total electron content in TECu across South America on Feb. 5, 2015, between 19:00 UTC and 19:15 UTC.
The other data in Figure 6 (site names MCL1, RJCG, ONRJ) were processed from GPS receivers operated by Instituto Brasileiro de Geografia e Estatística (IBGE). These observations show a progression of TEC values as a function of latitude and were collected on Feb. 5, 2015, a day of minor geomagnetic activity (the highest value reached of the Kp index, an indication of global geomagnetic activity, was 3.3) and of moderate solar flux (10.7-centimeter solar flux, an indicator of solar activity, was 142). The data collected at UNIVAP covers the period from 10:00 until 20:00 UTC. Note that for the earlier, more geophysically active period shown in Figure 6 between 0:00 and 5:00 UTC, the smartphone did not collect data due to resource limitations on available battery power.
Figure 7. Total electron content in TECu across South America on Feb. 5, 2015, between 19:00 UTC and 19:15 UTC.
FIGURE 7 illustrates the overall geophysical picture. It shows the locations of the data-collection sites overlaid onto vertical TEC estimates obtained separately from the aforementioned Brazilian GNSS receiver network and receivers owned and operated by the Red Argentina de Monitoreo Satelital Continuo (RAMSAC) continuously operating reference station network of the Instituto Geográfico Nacional de la República Argentina and the Low Latitude Ionospheric Sensor Network. This data was averaged over 15 minutes and binned in 1° by 1° bins. The southern Appleton anomaly region clearly appears as a red band that extends diagonally north of São José dos Campos parallel to the geomagnetic equator that dips in this region. Because this day is geomagnetically quiet, São José dos Campos lies in a region of smaller TEC south of the anomaly region. During more geomagnetically active conditions, it can lie directly under the anomaly.
Figure 8. Differential vertical total electron content in TECu reflecting traveling ionospheric disturbances on Feb. 5, 2015, at 19:15 UTC.
By contrast, FIGURE 8 shows data that is neither binned nor averaged over time. This alternate processing method using the same underlying data set reveals TIDs moving across the region. The low concentration of electrons in the region 40°–55° W longitude and 5°–10° S latitude and the high concentration of electrons in the region 40°–55° W longitude and 15°–20° S latitude suggest the shape of a TID. Relevant to the potentials of distributed Mahali sensor systems, better interpretations could be made in this scientific context with more data points.
Both Figures 7 and 8 clearly show the need for more receiver sites to fill in the gaps in data coverage. This is where the Mahali concept can make a real contribution, as it enables receiver deployment in areas with less developed infrastructure such as the Amazon area.
We have also recently undertaken a campaign in Alaska and have reported on that experience elsewhere (see Further Reading).
Conclusion
This article presents one app in the Mahali software ecosystem, designed to directly connect smartphones to GNSS receivers for scientific data collection. The initial Brazil field tests described here have provided a proof of concept that smartphones can be used as versatile relays of data to cloud storage environments. The results have demonstrated that the Mahali concept can make a new and fundamental contribution to observational science by enabling receiver deployment in areas with less developed infrastructure. Observations from these regions contain crucial geophysical information and are at the forefront of geospace scientific research.
The Mahali project is funded by a National Science Foundation Integrated NSF Support Promoting Interdisciplinary Research (INSPIRE) grant. We would also like to acknowledge our collaborators at Boston College, Virginia Tech, Johns Hopkins University, the University of New Brunswick and Colorado State University, as well as the support of UNAVCO for loans of dual-frequency GNSS receivers for use in this project. We also thank Intel for loans of mobile smartphones.
Travel to Brazil was kindly supported by the MIT International Science and Technology Initiatives program. This article is based on the paper “A Smartphone App for GNSS Ionospheric Data Collection: Initial Field Test Results” presented at ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation held in Tampa, Florida, Sept. 14–18, 2015.
Manufacturers
The GNSS receivers used for our tests in Brazil included a NovAtel GPStation-6 GNSS Ionospheric Scintillation and TEC Monitor (GISTM) receiver and earlier generation NovAtel GSV4004B GISTM receivers. We employed Samsung Galaxy S3 and Motorola Moto G smartphones.
ANDREW KENNEDY is a doctoral candidate in the Space, Telecommunications, Astronomy and Radiation Laboratory at the Massachusetts Institute of Technology (MIT) in Cambridge, Mass.
RYAN KINGSBURY is a recent doctoral graduate from the Space, Telecommunications, Astronomy and Radiation Laboratory at MIT.
ANTHEA COSTER is an assistant director and principal research scientist at MIT Haystack Observatory, Westford, Mass., and a co-principal investigator (co-PI) of the Mahali project.
VICTOR PANKRATIUS is a research scientist at MIT Haystack Observatory where he leads the Astro- & Geo-Informatics Group. He also serves as the principal investigator of the Mahali project.
PHILIP ERIKSON is an assistant director and principal research scientist at MIT Haystack Observatory and a co-PI of the Mahali project.
PAULO ROBERTO FAGUNDES is professor at Universidade do Vale do Paraíba, São José dos Campos, Brazil.
EURICO R. de PAULA is a senior researcher in the Aeronomy Division of the Instituto Nacional de Pesquisas Espaciais, São José dos Campos, Brazil.
KERRI CAHOY is the Boeing Assistant Professor of Aeronautics and Astronautics at MIT.
JUHA VIERINEN is a research scientist at MIT Haystack Observatory.
FURTHER READING
Authors’ Conference Papers
“The Mahali Project: Deployment Experiences from a Field Campaign in Alaska” by A. Coster, V. Pankratius, T. Morin, W. Rogers, F. Lind, P. Erickson, D. Mascharka, D. Hampton and J. Semeter in Proceedings of ITM 2016, the 2016 International Technical Meeting of The Institute of Navigation, Monterey, Calif., Jan. 25–28, 2016, pp. 885–892.
“A Smartphone App for GNSS Ionospheric Data Collection: Initial Field Test Results” by A. Kennedy, R. Kingsbury, A. Coster, V. Pankratius, P.J. Erickson, P. Fagundes, E.R. de Paula, K. Cahoy and J. Vierinen in Proceedings of ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Fla., Sept. 14–18, 2015, pp. 3745–3754.
“The Mahali Space Weather Project: Advancing GNSS Ionospheric Science” by A. Coster, V. Pankratius, F. Lind, P. Erickson and J. Semeter in Proceedings of ION GNSS+ 2014, the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Fla., Sept. 8–12, 2014, pp. 1213–1221.
“Mobile Crowd Sensing in Space Weather Monitoring: The Mahali Project” by V. Pankratius, F. Lind, A. Coster, P. Erickson and J. Semeter in IEEE Communications Magazine, Vo. 52, No. 8, Aug. 2014, pp. 22–28, doi: 10.1109/MCOM.2014.6871665.
“Automated GPS Processing for Global Total Electron Content Data” by W. Rideout and A. Coster in GPS Solutions, Vol. 10, No. 3, July 2006, pp. 219–228, doi: 10.1007/s10291-006-0029-5.
“Medium-scale Traveling Ionospheric Disturbances Observed by GPS Receiver Network in Japan: A Short Review” by T. Tsugawa, N. Kotake, Y. Otsuka and A. Saito in GPS Solutions, Vol. 11, No. 2, March 2007, pp. 139–144, doi: 10.1007/s10291-006- 0045-5.
“Traveling Ionospheric Disturbances as a Diagnostic Tool for Thermospheric Dynamics” by K.C. Yeh in Journal of Geophysics, Vol. 77, No. 4, Feb. 1972, pp. 709–719, doi: 10.1029/JA077i004p00709.
“Detecting Ionospheric TEC Perturbations Caused by Natural Hazards Using a Global Network of GPS Receivers: The Tohoku Case Study by A. Komjathy, D.A. Galvan, P. Stephens, M.D. Butala, V. Akopian, B. Wilson, O. Verkhoglyadova, A.J. Mannucci and M. Hickey in Earth, Planets and Space, Vol. 64, No. 12, Dec. 2012, pp. 1287–1294.
Future automotive GNSS positioning in urban scenarios
By Martin Escher, Mirko Stanisak and Ulf Bestmann
INNOVATION INSIGHTS with Richard Langley
WHERE ARE WE GOING with GNSS positioning? There have been many advances in satellite-based positioning over the past couple of decades and there are more to come.
Probably the most significant advance, affecting the most users, has been the further miniaturization of GNSS chipsets and modules. Virtually every mobile phone now includes a GPS component. Developers have also made these embedded devices more sensitive so that they can work with smaller, less efficient antennas. Furthermore, GPS satellites are now being launched with additional, more capable signals and already high-end receivers are starting to use these signals. Once full constellations transmitting these signals are in place, consumer devices will likely make use of them as well.
Another very important advance in GNSS positioning has been the development of additional GNSS constellations and multi-GNSS receivers capable of using their signals. Actually, it’s been a multi-GNSS world for quite a while now. The Russians began development of GLONASS shortly after work began on fielding GPS and both systems achieved full operational capability in the mid-1990s. Unfortunately, due to financial problems following the break-up of the Soviet Union, the number of operating GLONASS satellites fell to the single digits making the system virtually unusable. However, with renewed government support, GLONASS has once again become a viable GNSS and many consumer and professional receivers can track and use GLONASS signals along with those of GPS.
In the 1990s, we also saw the development of the U.S. Wide Area Augmentation System, transmitting GPS correction and integrity information from geostationary satellites on the GPS L1 (and subsequently L5) frequency. Other compatible satellite-based augmentation systems followed, including the European Geostationary Navigation Overlay Service, Japan’s Multi-Functional Transport Satellite Satellite-based Augmentation System, India’s GPS Aided GEO Augmentation System, and Russia’s System for Differential Correction and Monitoring. Besides enhancing integrity, the data transmitted by the satellites of these systems improves GPS pseudorange-based positioning accuracy, sometimes to below the one-meter level.
Starting about 15 years ago, we have seen the development of additional autonomous GNSSs, joining GPS and GLONASS. The European Galileo system is under construction as is China’s BeiDou system. And although only providing regional coverage, we should also mention Japan’s Quasi-Zenith Satellite System and the Indian Regional Navigation Satellite System. While all of the new systems are still in development and full constellations are still some years away from completion, the signals from the satellites already in orbit can be used to supplement those received from GPS and GLONASS satellites to improve positioning and navigation availability for some difficult navigation scenarios.
One of the most difficult situations requiring a continuous positioning capability is driving in built-up areas where buildings and other objects can block the signals from a number of GPS satellites such that GPS-only positioning becomes impossible. Even if four or more satellites are in view of the satellite navigation receiver’s antenna, those satellites might have unfavorable geometry, resulting in significantly degraded positioning accuracy. However, if the receiver can access the signals of two or more GNSSs, then position fixes might be available where none were possible with GPS alone, and the accuracies of marginal fixes might be improved.
In this month’s column, we take a look at some early work in using multi-GNSS plus additional sensors for navigating in the heart of the city of Braunschweig, Germany (the birth place of Johann Friedrich Carl Gauss, the inventor of least squares and the father of modern geodesy), and how the additional signals can help us to get where we’re going.
In the near future, we will see the introduction of more and more next-generation advanced driver assistance systems (ADASs) targeting the field of automated or autonomous driving. These systems will have to be considered as safety critical. In contrast to conventional localization systems, they will have to guarantee a higher overall accuracy and integrity to their target applications. Of course, the overall performance of any localization system is mostly limited by its behavior during the worst conditions.
Such behavior is a very limiting factor especially for an ADAS that uses a GNSS such as GPS. The accuracy and integrity of GNSS depend on the quality and availability of satellite signals. The more signals that are available, the greater are the accuracy and integrity. However, as GNSS signals can be blocked easily, the worst-time behavior is difficult to characterize, especially in challenging urban scenarios important for an ADAS.
To face these challenges, additional sensors such as inertial measurement units (IMUs) or odometers can be used for positioning as well. These sensors can increase the availability and accuracy for situations where GNSS-based positioning is not available. Additionally, the characteristics of these sensors are complementary to satellite navigation. The combination of these sensors with satellite navigation thus has the potential to achieve a positioning accuracy and integrity superior to that of single-system performance.
As the number of GNSS measurements is crucial for a precise position fix, the parallel use of different GNSS constellations can improve the overall performance significantly.
Four global satellite-positioning systems are now available. The American GPS and the Russian GLONASS have been in operation for years and are already used in a wide variety of applications. Additionally, newer systems like the European Galileo and the Chinese BeiDou systems are under construction. Even though these systems do not have continuous worldwide availability at the moment, their currently available satellites can already be included in multi-constellation GNSS positioning. Once more satellites are in orbit, the benefit of multi-constellation GNSS will increase even further.
In this article, we take a look at the current performance of multi-constellation GNSS positioning in an urban scenario, contrasting it with GPS-only positioning as well as GNSS positioning aided by additional sensors.
Satellites in orbit
To characterize multi-constellation GNSS performance, stationary GNSS data has been collected using different receivers in Braunschweig, Germany. GNSS data from GPS, GLONASS, Galileo and BeiDou was recorded over a 14-hour window on November 20, 2015.
Based on the broadcast ephemeris data and the receiver’s position, the availability of GNSS measurements was calculated for the duration of the campaign. TABLE 1 shows the number of all satellites of the different constellations as well as the minimum and maximum number of available satellites for each system during the recording period down to an elevation angle of 0°.
Table 1. Number of satellites in orbit and in view during a 14-hour window.
FIGURE 1 shows the satellite availability for the measurement campaign. To obtain a position fix using a single GNSS constellation, range measurements to at least four satellites of this constellation must be acquired. Thus, assuming optimal reception of GNSS signals, single-constellation positioning was possible for the full observing window using only GPS, only GLONASS and only BeiDou satellites. On the other hand, Galileo-only position fixes were not possible at any time due to the low number of simultaneously visible satellites.
FIGURE 1. Satellites in view from Braunschweig, Germany.
However, combining measurements from different GNSS constellations in parallel — multi-constellation GNSS — provides the most benefit.
Multi-Constellation GNSS
All major GNSS constellations operate independently and use different reference frames for position and time. To combine measurements of different GNSS constellations, the correct handling of the diverse reference frames needs to be ensured.
On the one hand, the different coordinate systems have to be taken into account. However, the differences between the position frames is usually kept to within a few centimeters and can thus be neglected for most standalone-GNSS applications.
On the other hand, the handling of the different system time scales requires a specific approach. Even though the inter-system biases (that is, the differences between the system time scales) are usually kept within a few nanoseconds, the influence of the inter-system offsets must not be ignored for most applications and have to be taken into account for a combined position solution.
The most common approach is to extend the estimated state vector with a distinct clock error for each used constellation. For a combined position solution incorporating GPS, GLONASS, Galileo and BeiDou, the state vector used for the least-squares estimation could look like this:
. (1)
Each pseudorange measurement only contributes to its respective clock-error component.
Of course, as the values of more unknown variables have to be estimated, the number of necessary GNSS measurements increases, too. To calculate a combined position solution including GPS, GLONASS, Galileo and BeiDou for the above-mentioned example, seven variables must be estimated. This means that at least seven independent GNSS measurements are necessary at each epoch. However, if no satellite of a specific constellation is available, the state vector can also be adapted to not estimate the corresponding clock error. In this way, the availability of a multi-constellation GNSS solution is always higher or, in the worst case, equal to that of the single-constellation GNSS solutions.
By being able to use more than just one GNSS constellation, the geometric distribution of the satellites over the sky is improved, resulting in a lower dilution of precision (DOP). A lower DOP value usually indicates a better mapping of range measurement precision into the position precision. However, as the different GNSS constellations are currently in different states of maturity, the range precision varies significantly. Thus, a multi-constellation position solution is not necessarily more accurate than a single-constellation solution, but will benefit from an improved overall availability and integrity.
Such a capability is particularly important for safe operations in constrained scenarios like urban canyons, which are a common challenge for automotive applications. Compared to currently prevailing GPS-only positioning, multi-constellation GNSS has the potential to enable safety-of-life services, which will require a high level of integrity in the automotive domain.
Tight coupling
To take even greater advantage of multi-GNSS positioning in challenging environments, the combination with additional sensors can improve the overall positioning performance significantly. The Institute of Flight Guidance at the Technische Universität Braunschweig has developed a tightly coupled GPS fusion system, which incorporates measurements of a close-to-market IMU and odometer sensors for reliable urban car positioning.
This system is capable of using raw data from a reference station receiver to generate differential GNSS corrections. These differential corrections must be free from reference-receiver clock error before they can be used by the tightly coupled system (rover-receiver clock-bias update by pseudorange positioning, rover-receiver clock-drift update by Doppler frequency velocity estimation, and clock-bias prediction by clock drift).
. (2)
As shown in Equation 2, the system calculates the residuals for each pseudorange (PSR) received by the reference receiver based on the well-known reference antenna position and the current satellite position as calculated using its broadcast ephemeris . While calculating the residuals, it involves the atmospheric effects ε j computed by the Klobuchar ionosphere delay model and a modified Hopfield tropospheric delay model.
These residuals must be corrected by the satellite clock errors (also calculated using the broadcast ephemeris). The arithmetic average of the corrected residuals is used as an estimate for the reference receiver clock error (see Equation 3). This approach is sufficient for most applications, but it is also possible to use additional algorithms to estimate the clock error more accurately.
. (3)
To generate reference receiver clock error-free pseudorange corrections, the residuals are calculated a second time. Only the estimated clock error of the reference receiver is removed in the second set of residuals:
. (4)
The assumption was made that these residuals correct all satellites, all atmospheric errors and the inter-system time errors.
With this assumption, the tightly coupled system uses the corrected residuals as pseudorange corrections for the ranges measured by the rover receiver. Using the corrected pseudoranges, the tightly coupled system can estimate the rover receiver’s clock error for positioning:
. (5)
In this way, the inter-system offsets are eliminated as well. Corrected multi-constellation GNSS measurements can thus be processed by estimating one receiver clock error only.
Simulation of obstacles
The performance of satellite navigation is affected directly by the distribution of the useable GNSS satellites over the sky. The more GNSS satellites are spread out over the sky, the lower the DOP value and the better the positioning accuracy. For reference, FIGURE 2 shows a sky plot of unconstrained GNSS with perfect reception of all GNSS satellites during the measurement period of 14 hours. Combining the satellites of all four GNSS core constellations (GPS, GLONASS, Galileo and BeiDou), up to 30 satellites are usable at the same time.
FIGURE 2. Sky plot of GNSS satellites (GPS, GLONASS, Galileo and BeiDou) at Braunschweig.
Of course, this is an optimized scenario that can only be achieved using high-quality antennas without any obstacles in the vicinity. Many applications, including urban automotive situations, do not have a comparable reception of GNSS data, and will suffer from blocked satellites and multipath reception.
Therefore, we created a simulation of surrounding obstacles to predict the behavior of GNSS positioning in challenging urban scenarios. In this simulation, all buildings are represented by endless walls with constant height. A satellite is assumed to be invisible if its line of sight crosses the wall.
To get a first impression of the usability of this approach, we took GNSS measurements in front of the Institute of Flight Guidance in Braunschweig.
Using this scenario, the same simulation of optimal visibility using ephemeris data has been conducted again. As shown in FIGURE 3, large portions of the sky are blocked by the simulated obstacles.
FIGURE 3. Sky plot with valid (thick lines) and invalid (thin lines) measurements.
Of course, the blockages also affect the number of visible satellites as shown in FIGURE 4. Instead of 23 to 31 satellites for the unconstrained scenario, only 11 to 18 satellites are now visible.
FIGURE 4. Comparison of satellite visibility with and without simulated obstacles.
In a following step, we validated the theoretical predictions of the visible GNSS satellites against the reception by a GNSS receiver of the available signals at the simulated position.
Validation of simulation
For a validation of the obstacle simulation, data from a high-grade receiver was used for the validation of the simulation. This modern GNSS receiver is able to track signals from all GNSS constellations (GPS, GLONASS, Galileo and BeiDou) on different GNSS frequencies with a data rate of up to 100 Hz. The BeiDou reception, however, was only acquired recently before the recording of the data and unfortunately suffered from bad BeiDou tracking performance.
The receiver was connected to a multi-frequency antenna. This GNSS antenna was installed at the back of the roof of the research car. A sky plot of the tracked signals is shown in FIGURE 5.
FIGURE 5. Tracked signals of the high-end receiver.
A comparison of the simulated (Figure 3) and the actual (Figure 5) sky plots shows a very good agreement between the simulations and the measurements. There are, however, some spots in the sky plot where the real GNSS receiver is able to track satellites that are behind a building. This can be explained by the reception of signals through the windows of the building. Thus, the signal-quality indication based on the receiver’s signal-to-noise measurements of these spots is quite bad in these situations.
As described before, we experienced some problems with the BeiDou reception of the high-grade receiver. Thus, we used an additional single-frequency GNSS receiver. This receiver is capable of providing raw L1 GNSS data of two constellations simultaneously and was configured to track GPS and BeiDou satellites. In this way, an additional sky plot showing GPS and BeiDou reception in the same setup could be generated. The visible BeiDou satellites are shown in light blue in FIGURE 6 and are in accordance with the simulated visibility.
FIGURE 6. Valid signals sky plot of the single-frequency receiver data.
In general, the sky plots identify significant differences compared to the simulated ones as even in regions blocked by buildings some satellites can still be tracked. The contour of the building, however, can still be seen in the signal strength plot in FIGURE 7.
FIGURE 7. Signals strength sky plot of the single-frequency data.
This result is an indication that the single-frequency receiver can track some satellites blocked by the buildings using diffracted or reflected signals, but, of course, resulting in worse positioning accuracy.
It goes without saying that the various receivers we used are designed with contrary goals in mind. High-performance GNSS receivers are optimized to provide accurate position solutions for high-demanding applications. Thus, the receiver attempts to suppress multipath effects as much as possible to obtain precise and accurate position solutions. The single-frequency receiver, on the other hand, is closer to the low-price, high-volume class of receivers for portable devices, and is optimized to provide valid position output even in challenging environmental situations. Thus, the receivers must not be compared directly because they are designed for completely different purposes.
Simulating urban canyons
To assess the overall multi-GNSS performance in urban scenarios, we conducted driving tests in the city center of Braunschweig. Driving through city centers is particularly challenging for any positioning algorithm because of various potential sources of errors. Instead of only using suburban commuter roads, the route we chose represents the most challenging situations for the city center. Most of the roads are surrounded by multi-story buildings (typically up to six floors) very close to the driving lanes. This is – especially for European cities – a common and challenging urban scenario for satellite navigation. An example of such a scenario is shown in FIGURE 8.
FIGURE 8. Dimensions of representative urban scenario.
To quantify the impact of the limited GNSS availability due to buildings and other obstacles, we simulated a scenario with walls on both sides of the road. With the road running in a north-south direction, we simulated buildings within a distance of 14 meters and a height of 15 meters. The simulated effect on a GNSS receiver in the middle of the street due to blocked satellites in this scenario is shown in FIGURE 9. Satellites with an elevation angle of up to 65° can be obstructed by the buildings.
FIGURE 9. Sky plot for obstacle simulation of urban canyon.
In this scenario, more than half of the sky is blocked by buildings, making satellite navigation quite challenging. Additionally, Braunschweig is located at about 52° north latitude and is close to the inclination of most GNSS constellation orbits (GPS 55°, Galileo 56°, BeiDou MEO 55°). Only GLONASS satellites can be seen in the far northern part of the sky from time to time due to their inclination of 65°.
Using GPS satellites only, fewer than four satellites are available for long periods of time. On the other hand, using a combination of all constellations, up to 14 satellites can be used even for this constraining scenario. Most of the time, at least seven satellites are visible, allowing a multi-constellation GNSS position solution.
Downtown positioning
To assess the practical benefit of multi-constellation GNSS in urban scenarios, we conducted a test drive in downtown Braunschweig using our research car. This area is dominated by narrow roads with multi-story buildings on both sides of the road. Using recorded data from different GNSS receivers and other sensors, multiple positioning solutions were obtained by post-processing the recorded data to compare the different positioning performances.
As a baseline for comparison, a GPS-only position solution was calculated. This result represents the current state-of-the-art navigation systems for most production cars. All valid GPS-only position fixes are shown in FIGURE 10. For large portions of the test drive, no GPS-only position solution was possible because of insufficient GPS measurements.
FIGURE 10. GPS-only standalone positioning fixes for test drive in Braunschweig.
To quantify the benefit of multi-constellation GNSS compared to GPS-only, a combined position solution was calculated using the same data as before. There was a significant improvement in the availability compared to the GPS-only position solution.
However, even when using multiple GNSS constellations, some areas with no valid GNSS fixes still exist. The GNSS availability can be improved further by using differential corrections from a GNSS reference receiver. The correction data is available in the research car using 4G mobile telecommunication links to different service providers. Each provider uses a network of GNSS receivers to calculate differential corrections. However, all commercially available services are currently limited to GPS and GLONASS. Thus, another stationary multi-constellation GNSS reference receiver at the Institute of Flight Guidance generated correction data for the test drives. As the baselines are short in this scenario (not longer than 10 kilometers), no significant spatial decorrelation is expected.
As the majority of possible inter-system offsets are already eliminated using the differential corrections of identical receiver types, a multi-constellation solution can be calculated here even with as few as four GNSS satellites in view. This is shown in FIGURE 11. In this way, the achieved availability increased again.
FIGURE 11. Differentially corrected multi-constellation positioning fixes for test drive in Braunschweig.
Finally, using all the information available in the car, a hybrid position solution based on differentially corrected GNSS, inertial navigation and the test vehicle’s odometer has been calculated.
In sections without any GNSS positioning aiding (marked red in FIGURE 12), the inertial navigation system was used in dead-reckoning mode. As these outages lasted only for short periods of time, the accuracy of the combined position remained usable for the duration of the test. In this way, an accurate position solution could be calculated for the whole test drive using this tightly coupled positioning algorithm.
FIGURE 12. Tightly coupled positioning trajectory for test drive in Braunschweig.
With increasing positioning complexity, the computational burden increased as well. For a tightly coupled system integrating the measurements of the different sensors, significantly more calculations must be performed in real time than for current GPS-only standalone positioning. However, even today these computations can be easily made using embedded devices.
Conclusions and outlook
For this article, the achievable positioning performance of multi-constellation GNSS has be analyzed with a special emphasis on urban automotive applications. Simulations of constrained environments have been compared with real data and show good agreement. Multi-constellation GNSS outperforms GPS-only positioning, especially in situations where large portions of the sky are blocked by obstacles, because significantly more satellites remain usable. Multi-constellation GNSS has thus the potential to be an important part of future safety-of-life positioning and navigation applications.
However, a few challenges still exist. Some GNSS constellations have not reached their full operational capabilities as not all satellites are in orbit yet (Galileo and BeiDou). Additionally, the ranging errors of these systems are expected to decrease with improved navigation message accuracy and receiver performance.
The availability of numerous GNSS constellations results in new requirements for the receivers as well. Even though most manufacturers of GNSS equipment already support the additional systems with some products, the majority of currently used GNSS receivers is limited to one or two constellations, especially in mass-market applications. In addition, the reception quality of the newer systems is not always on the same level as GPS or GLONASS because of the limited experience that manufacturers have with Galileo and BeiDou. This, we hope, will change in the near future.
Acknowledgments
This article is based on the paper “Future Automotive GNSS Positioning in Urban Scenarios” presented at The Institute of Navigation 2016 International Technical Meeting, held in Monterey, Calif., Jan. 25–28.
Manufacturers
The high-grade receiver used in our tests was a Septentrio AsteRx3. The receiver was connected to a NovAtel GPS-703-GGG antenna. The single-frequency receiver we used was a u-blox LEA-M8T GNSS receiver with firmware version 2.3. Additionally, we used a NovAtel OEM6 multi-GNSS receiver and an Analog Devices ADIS16375BMLZ IMU.
MARTIN ESCHER holds a Dipl.-Ing. in electrical engineering from the Technische Universität (TU) Braunschweig in Braunschweig, Germany, and has been employed as a research engineer at the Institute of Flight Guidance (IFF) since 2010.
MIRKO STANISAK is a research assistant and Ph.D. candidate at the IFF of TU Braunschweig. He received his Dipl.-Ing. in mechanical engineering in 2009 and since then has worked on various GNSS-related topics.
ULF BESTMANN received his Dr.-Ing. in mechanical engineering from the TU Braunschweig in 2010. He is employed at the IFF of TU Braunschweig, where he is head of the navigation department.
Further Reading
Authors’ Conference Paper
“Future Automotive GNSS Positioning in Urban Scenarios” by M. Escher, M. Stanisak and U. Bestmann in Proceedings of ITM 2016, the 2016 International Technical Meeting of The Institute of Navigation, Monterey, Calif., Jan. 25–28, 2016, pp. 836–845.
Multi-Constellation GNSS Measurements
“Precise Point Positioning with Galileo Observables” by R.M. White and R.B. Langley in Proceedings of the 5th International Colloquium on Scientific and Fundamental Aspects of the Galileo Programme, Braunschweig, Germany, Oct. 27–29, 2015.
“Accuracy and Reliability of Multi-GNSS Real-Time Precise Positioning: GPS, GLONASS, BeiDou, and Galileo” by X. Li, M. Ge, X. Dai, X. Ren, M. Fritsche, J. Wickert and H. Schuh in Journal of Geodesy, Vol. 89, 2015, pp. 607–635, doi: 10.1007/s00190-015-0802-8.
“Performance Evaluation of Integrated GPS/GIOVE Precise Point Positioning” by W. Cao, A. Hauschild, P. Steigenberger, R.B. Langley, L. Urquhart, M. Santos and O. Montenbruck in Proceedings of ITM 2010, the 2010 International Technical Meeting of The Institute of Navigation, San Diego, Calif., Jan. 25–27, 2010, pp. 540–552.
“A GPS/Galileo Tightly-Coupled Localization System for Safety-Relevant Automotive Assistance Systems” by H.-G. Büsing, M. Escher, T. Scheide and P. Hecker in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Ore., Sept. 19–23, 2011, pp. 356–362.
Geometry Effects on GNSS Positioning
“Dilution of Precision” by R.B. Langley in GPS World, Vol. 10, No. 5, May 1999, pp. 52–59.
When siting the antenna of a GNSS receiver or designing a GNSS-based navigation system, electromagnetic compatibility is an important concern. This is particularly true for airborne platforms. In this month’s cover story, we take a look at how radio-frequency interference can impact GNSS equipment on unmanned aircraft systems and how robustly the equipment can navigate those systems.
INNOVATION INSIGHTS with Richard Langley
WHAT’S THE WEAKEST THING ABOUT GNSS?Literally, it’s the signals. The strength of GNSS signals is notoriously low as anyone who has tried to operate a consumer-level device inside a steel and concrete building can readily attest. Unlike mobile phone signals, GNSS signals are too weak to survive the attenuation of walls, floors, and ceilings and so typically cannot provide a dependable signal indoors for most receivers.
Even outdoors, the signals can be significantly attenuated by dense wet foliage and completely blocked by buildings and other objects. The GPS C/A-code signal generated by the transmitter in a satellite is approximately 27 watts. If such a transmitter were operated on Earth it would provide a decent signal even inside a nearby building. First responders, for example, can communicate with each other using portable transceivers with even lower-powered transmitters.
However, GPS satellites are about 20,000 kilometers away at their closest and the signals they transmit spread out as they travel to the Earth and even with the directivity of the satellite transmitting antenna, by the time the signals reach the surface of the Earth, their power density is only on the order of 10-13 watts per square meter. And that’s outdoors.
This signal is so weak that it is buried in the receiver’s background noise, which is similar to what you hear when you tune an AM radio between stations. So how can GPS possibly work with such a weak signal? The received signal is actually spread out over several megahertz of radio-frequency spectrum by the pseudorandom noise ranging code. It is this known noise-like code that allows receivers to determine the biased-ranges to satellites and from those ranges determine their positions. Knowing the code, the receiver de-spreads the weak received signal, concentrating it and lifting it above an acceptably low background noise.
All is fine and well as long as the received signal density doesn’t drop much below the 10-13 watts per square meter level but also the background noise level mustn’t rise much above the acceptable level for which the receiver is designed. Both of these criteria are reflected in the carrier-to-noise-density ratio, or C/N0, of the signal. Why might the noise level change? The noise comes from the receiver itself as well as from naturally produced electromagnetic radiation from the sky, the ground, and objects in the receiving antenna’s vicinity. The sky noise includes so-called cosmic noise from the sun, Milky Way galaxy, other discrete cosmic objects and radiation left over from the Big Bang as well as radiation from our atmosphere. For the most part, the noise from these sources is small but occasionally the sun can have a radio outburst that can significantly increase the noise level at GNSS frequencies and actually overpower the GNSS signals as happened with GPS in December 2006.
But the noise level can also be impacted by human-made electrical devices in the vicinity of a GNSS receiver’s antenna. This radio-frequency interference, or RFI, can come from devices such as radio transmitters, microwave ovens, motors, relays, ignition systems, switching power supplies and light dimmers. So, when siting the antenna of a GNSS receiver or designing a GNSS-based navigation system, electromagnetic compatibility is an important concern. This is particularly true for airborne platforms. In this month’s column we take a look at how RFI can impact GNSS equipment on unmanned aircraft systems and how robustly can the equipment navigate those systems.
As the number of unmanned aircraft systems (UAS; also called unmanned aerial vehicles and drones) in use is increasing across many sectors, there is an interest in understanding the robustness of the GNSS engine used on UAS. With UAS being integrated into the National Airspace System (NAS), questions arise about what kind of navigation system should be used on UAS, and to what degree it should be standardized. Conventional aircraft typically use a certified GNSS receiver for navigational purposes, and as UAS will share the sky with conventional aircraft in the future, it is not unreasonable that UAS will use similar receivers.
The first part of this article provides background on the status of GNSS standards for UAS. In the second part, we discuss why radio-frequency interference (RFI) can be expected on some UAS, together with what issues the RFI could cause for the GNSS engine. A simple experiment to determine the presence of RFI in the GPS L1 band due to proximity of a GPS antenna to electronics is presented in this section as well. The third part of the article discusses real-time kinematic (RTK) positioning for UAS purposes. In terms of accuracy, RTK positioning often provides the best results. The robustness of RTK measurements is questionable, though, because the technique relies on carrier-phase measurements. We present a case study, which shows some of the issues of using RTK positioning for UAS, in this part of the article, too.
GNSS standards for UAS
GNSS, and especially GPS, have been used in aviation for quite some time. The GPS receivers used for aviation have to guarantee a certain level of performance to be used, and are certified by the manufacturer to deliver said performance.
The Federal Aviation Administration (FAA) is working on integrating UAS into the NAS. The development of UAS has been quick and has led to a lack of standardization for UAS, something that does exist for traditional manned aircraft. This has led to operators in most cases having to file for exemptions from the existing rules in order to use UAS. It is the ambition of the FAA to transition from issuing exemptions to issuing certifications of UAS once an agreement on regulations has been reached. There are still a number of challenges associated with a full integration of UAS into the NAS, including regulatory, procedural and technical challenges.
The Wide Area Augmentation System (WAAS) was the first operational space-based augmentation system, intended to increase the robustness and reliability of GPS for aviation purposes. The WAAS Minimum Operational Performance Standards (MOPS) document (see Further Reading) specifies what kind of performance GPS plus WAAS provides to aviation users.
The MOPS requirements have been carefully examined and extended. The maximum in-band interference levels for aviation have been theoretically analyzed. As long as signal and interference levels are within the specified ranges, the required performance should be expected.
These levels, combined with the WAAS MOPS, provide the aviation community with the standardization required for manned aircraft operations where lives can be at stake if something were to go wrong with a navigation system. A Volpe National Transportation Systems Center report (see Further Reading) recommends the use of certified GPS receivers for applications where GPS is a critical system. This is not yet a requirement for UAS, and the question remains unanswered as to whether this will be a requirement for UAS in the future.
Traditional aviation uses required navigation performance (RNP), a performance-based navigation approach, to assess what type of navigation systems can be used for different phases of flight. For example, while an aircraft is en route, an RNP of 2 nautical miles is required, meaning the actual position of the aircraft cannot deviate more than 2 nautical miles from a reported position. It should be noted that RNP takes the entire system into consideration, from the space-segment to the receiver to the capabilities of the aircraft.
GNSS receivers used on manned aircraft have to be certified to deliver the RNP for each phase of flight for which they are used. Receiver autonomous integrity monitoring (RAIM) is used to ensure that faulty measurements do not affect the position and navigation solution. Due to the nature of RAIM, more satellites are required than the traditional minimum of four. If GNSS supplements other systems on board the aircraft, RAIM may be used to only monitor the quality of the system, and it will report when performance is below the required minimum. This form of RAIM requires a minimum of five satellites.
However, if the aircraft depends on GNSS for navigation, RAIM must be able to determine if a particular satellite is providing incorrect or subpar data. This requires one additional satellite, bringing the minimum number of satellites that have to be in view of the receiver’s antenna up to six (two more than non-RAIM GNSS operation).
However, using RAIM requires additional computational power, which one might not be able to provide on board a UAS due to size, weight and power limitations. It has been suggested that a GNSS system coupled with an inertial navigation system (INS) could be used for UAS navigation. A micro-electro-mechanical system (MEMS) INS would be very small, would not require a lot of power, and could improve the performance of a UAS navigation system. A GNSS plus MEMS INS approach may well be able to provide the robustness needed for UAS. However, the analysis of such a system is outside the scope of this article.
Some basic considerations should be taken into account for a UAS GNSS positioning system. Integrity should be prioritized over accuracy if the system is used for navigational purposes. Low-altitude operations could bring on problems of sky blockage. The proposed solution to this is to use a receiver capable of using multiple constellations to ensure that as many satellites as possible are in view.
Radio frequency interference
Radio frequency interference, or RFI, is the interference caused by electromagnetic waves interacting with a system they were not intended to interact with. A familiar case of RFI can be experienced when a cellular phone is placed in close proximity to an AM radio. A distinctive sound can sometimes be heard, which is the sound of RFI interacting with the radio.
Many forms of RFI exist. The interference can be in-band, that is, originating on frequencies transmitted within the band occupied by a desired signal, or out-of-band where the center-frequency of the interfering signal lies outside the band used by the desired signal but it can have a nonlinear impact on the components in the front end of the GNSS receiver. In some cases. the bandwidth of the interference is very small (narrowband), and in some cases the bandwidth is quite large (broadband). Depending on the type of interference, the affected systems will react differently.
RFI can, for obvious reasons, be expected from intentional radiators, such as equipment broadcasting signals near the GNSS signal frequencies, or other equipment that emits harmonics that lie close to the GNSS frequencies. These sources are documented, and the effects of them can be mitigated through proper planning and analysis.
However, electrical equipment can produce RFI that is not intended to be emitted — a so-called unintentional radiator. The Federal Communications Commission (FCC) Part 15 regulations define an unintentional radiator as “a device that intentionally generates radio frequency energy for use within the device, or that sends radio frequency signals by conduction to associated equipment via connecting wiring, but which is not intended to emit RF energy by radiation or induction.” Such devices are allowed to emit signal levels up to 300 or 500 microvolts per meter (depending on the class of the device) in the GNSS bands, as measured three meters away from the device.
Although most GNSS frequencies are protected, the risk for intentional or unintentional RFI exists. Some elements of the GPS system have been designed to mitigate interference effects, and GPS remains a relatively robust system. However, there are still sources that could interfere with the GPS signals, such as out-of-band transmissions, harmonics of airborne or ground-based transmitter equipment, radar transmitters or even local oscillators in nearby equipment.
In 1996, under a presidential decision directive, a commission to investigate a broad range of infrastructure vulnerabilities, including vulnerabilities to GPS, was set up. The commission found that GPS is in fact vulnerable to unintentional disruptions, from both human-made and naturally occurring sources. The commission recommended using certified GPS receivers for critical applications. The commission further recommended monitoring, reporting and locating unintentional RFI sources.
One of the potential issues with RFI in a GNSS engine is that it can cause false local correlation peaks, which could cause the code-tracking loop and the carrier-tracking loop to diverge from the main correlation peak.
RFI in the UAS GNSS Engine. On smaller UAS, space restrictions could lead to electronic components being placed in close proximity to each other. As stated earlier, some of these components could be producing RFI in the GNSS bands. If the RFI is strong enough to significantly raise the noise floor, the GPS signals could effectively be drowned out by noise. UAS that rely primarily on GNSS for navigation will risk losing navigational capabilities during such occurrences.
With no external interference present, the noise floor should be at the receiver’s thermal noise floor. The presence of interference could be indicated by the raising of the noise floor above the level of the thermal noise.
FIGURE 1 shows a simple setup for testing the hypothesis that electronics found on a common UAS could produce harmful RFI in the GPS engine. Some of the onboard equipment was a flight-controller, a 915-MHz communication link and a 2.4-GHz communication link.
FIGURE 1. Setup to test for GPS RFI.
A GPS antenna was placed outside and inside the UAS at common antenna locations. The antenna was connected to a high-performance GPS single-frequency-receiver evaluation kit and a spectrum analyzer. To enhance the effects and signals, a 40-dB inline amplifier was connected before the signal was split.
Three tests were carried out in this case study:
In a reference test, the antenna was placed on the outside of the airframe and the UAS was not powered on.
With the UAS power remaining off, the antenna was placed inside the airframe to see how much the signal was attenuated (see FIGURE 2).
With the antenna still inside the airframe, the UAS was powered on and all systems on the UAS were running.
FIGURE 2. Inside the UAS (including the GPS antenna).
The results from the receiver can be seen in FIGURES 3 and 4. Figure 3 shows that the number of satellites being tracked by the GPS receiver did not change between tests.
FIGURE 3. Satellites tracked by the evaluation-kit receiver.FIGURE 4. C/N0 values for different antenna and power configurations.
However, Figure 4 shows C/N0 for each test, and a clear difference can be seen (up to 10-dB difference from the case where the antenna was in the same location but with the UAS on and off). While this difference did not affect the receiver’s ability to provide a position solution, the accuracy was likely degraded due to the RFI. In a real-world scenario, this could lead to the user not noticing the presence of RFI, since the receiver is still able to output a position.
TABLE 1 shows some metrics calculated from the GPS receiver data. The table clearly shows a drop in C/N0 values when the UAS is powered on.
Table 1. Calculated values.
The results from the spectrum analyzer further show the effects of turning the UAS and its equipment on. FIGURE 5 shows the frequency spectrum using an average of 50 sweeps centered at 1575.42 MHz (GPS L1) with a bandwidth of 30 MHz for the case when the antenna was inside the airframe and the UAS was switched off. Due to improper initial calibration, the absolute values of the measurements are incorrect, and should be increased by 9 dBm. However, the relative measurements are still valid. FIGURE 6 shows the same setup for the spectrum analyzer but with all the UAS equipment on with the same caveat about the absolute values.
By comparing Figures 5 and 6, it is clear that the noise floor rises significantly when the UAS and its equipment is switched on. The GPS “bump” that was visible in the center of Figure 5 is no longer visible when the UAS is switched on in Figure 6.
FIGURE 5. RF spectrum when the antenna is inside the airframe, UAS switched off. See text concerning y-axis scale.FIGURE 6. RF spectrum when the antenna is inside the airframe, UAS switched on (all systems running). See text concerning y-axis scale.
RTK Positioning
RTK positioning is a high-accuracy GNSS positioning method that involves a base station and one or more rovers. The receivers operate in two distinct modes, fix or float. When a receiver is in float mode, the number of integer wavelengths in the carrier-phase measurements has not been resolved yet. In fixed mode, these have been resolved. This is also known as ambiguity resolution. The accuracy is greatly improved if ambiguities are resolved to their correct integer values. During dynamic cases (and even sometimes during static cases), the receiver may change between the two modes repeatedly.
RTK for UAS. RTK positioning can be very useful for UAS, as it can provide a better accuracy in a lot of cases compared to traditional positioning. It can be used for navigational purposes, or for positioning of scientific payloads carried on board a UAS.
RTK use on UAS is currently limited, in part due to the number of challenges associated with it. These include the size and weight issue for smaller UAS. Space is limited on board smaller UAS, and the available payload is also limited. RTK systems require more equipment than a regular GNSS system and therefore require more space and weight.
There is also the issue of cost for smaller UAS. To get quick, high-precision RTK positioning, a dual-frequency receiver is desirable, but such a system is often expensive and could deny a wide sector of the market access to such receivers. Researchers have performed some experiments with an L1-only RTK receiver and show that it could be possible to use such a system for UAS.
The experiments to be discussed in this article assume that the receivers being tested are candidates for possible UAS use. The high-performance GPS single-frequency-receiver evaluation kit used in the RFI tests is considered the prime candidate, as it is a common receiver found on UAS and is relatively cheap and lightweight.
As shown in the previous RFI section, it is possible for RFI to be present and for it to lower the C/N0 without affecting the number of satellites tracked. This could lead to the user being initially unaware of the RFI, and could potentially be a problem for RTK positioning as carrier-phase measurements are more easily disrupted.
Dynamic RTK Experiment. We performed an experiment to evaluate the performance of RTK in a real-world scenario that could be comparable to the use of RTK on a UAS. A comparison between RTK positioning and standard pseudorange-based positioning, essentially the GPS Standard Positioning Service (SPS), was also carried out for one of the receivers. RFI effects were not measured during the experiment.
Almost all post-processing (and some data capturing) was done using RTKLIB, a free and open source GNSS software suite. RTKLIB is modular and can be used at any stage in GNSS applications. The software is available at rtklib.com.
Three receivers were compared: the previously discussed high-performance GPS single-frequency-receiver evaluation kit; a low-cost, high-performance GPS receiver with RTK functionality; and a professional-grade multi-GNSS multi-frequency RTK survey receiver. As the low-cost receiver is marketed for UAS use, it was of interest to see how the receiver compared to the others in a dynamic case. The evaluation-kit receiver was of interest due to similar receivers often being used on UAS today. The professional-grade receiver was of interest since it is a high-end receiver capable of receiving multiple constellations and frequencies. The experiment was performed to simulate some of the conditions that might be experienced on UAS. The most approximate test vehicle that was available at the time was a car.
The receivers were set up to capture GPS signals only. The low-cost and evaluation-kit receivers are only capable of receiving the L1 signal, and were set up accordingly. The professional-grade receiver was set up to capture the L1, L2 and L5 signals. A truth reference for the test vehicle was needed for comparison, and for this we used a multi-frequency receiver with an inertial measurement unit (IMU). The benefit of the IMU is that it contains gyros and accelerometers that can capture very precise movements at times when GNSS signals might not be available (during periods of sky blockage for example).
However, due to the gyros drifting, the IMU needs to be updated with GNSS data every few minutes to give an accurate solution. The receiver was configured to capture GPS L1+L2+L5, GLONASS L1+L2 and WAAS. The GNSS data was then post-processed in precise point positioning (PPP) mode with data from several nearby stations. The GNSS PPP data was then smoothed and combined with the IMU data to form a GNSS PPP plus IMU solution. It was assumed that the GNSS receiver and IMU gave a correct solution at all times. A diagram of the setup can be seen in FIGURE 7.
FIGURE 7. Diagram of the setup of dynamic RTK experiment.
The car with the equipment was driven around the town and campus at the University of Colorado in Boulder. The path included a parking lot (a wide open area), parts of a highway (an open area), major roads (open area with parts covered by trees), residential areas (with many trees covering the sky) and a parking garage (with complete sky blockage). The parking garage was entered towards the end of the experiment.
The receiver data was post-processed using an RTKLIB setup to process the data as if it was received in real time. A multi-frequency multi-GNSS receiver was set up with a roof-mounted antenna at the University of Colorado to collect data for the duration of the experiment, and this data was later used as base-station data for the RTK calculations.
The low-cost receiver had a hard time regaining a position solution, while the evaluation-kit receiver did slightly better. The professional-grade receiver only lost a clear position for about 10 seconds. This behavior agrees with expectations: the low-cost receiver is new and is being updated regularly with new software, and the evaluation-kit receiver is known for being able to perform well under poor conditions. The professional-grade receiver has the support of additional GPS signals, which could explain why it was the first to regain a good position solution.
TABLE 2 shows some of the values calculated from the experiment, which further confirms that the evaluation-kit receiver is able to calculate a position more often than the professional-grade receiver, but a more inaccurate position. In the table, “availability” is defined as how many data points the receiver was able to capture, divided by how many would have been captured if the receiver could capture data at all times. “RTK solution” is how often the captured data was sufficient to calculate an RTK solution. “Fix solution” is defined as how often the ambiguities could be resolved out of the available RTK data points, and “float solution” is how often the ambiguities could not be resolved out the available RTK data points. The comparison of the results using SPS versus the RTK technique for the evaluation-kit receiver is interesting. Using RTK increases the accuracy only slightly, but not as much as anticipated before the test was performed.
Table 2. Tabulated results from the dynamic RTK experiment (N/A = not applicable).
Conclusions
GNSS is viable for UAS navigation, but it remains to be seen how policymakers will decide to regulate its use for this application. Many existing and emerging technologies could prove useful in increasing not only the reliability, but also the accuracy, of the GNSS engine on board a UAS.
Although UAS share many similarities with traditional manned aircraft, by their nature they are unmanned and would not pose the same immediate risk for significant loss of life if an accident were to occur. This, coupled with the fact that UAS can vary greatly in size and operational requirements, leaves the possibility open to using different certification requirements of GNSS navigation for different UAS.
RFI. The RFI experiment showed a considerable impact on C/N0 from the evaluation-kit receiver. While the number of satellites tracked remained constant between tests, it is possible that during slightly different operating conditions (different UAS and/or receivers, other onboard equipment and so on), the impact could have been more severe.
RTK for UAS. RTK systems are complex, but they have some clear advantages to traditional pseudorange-based standalone GNSS, with regard to accuracy. From the results of using the evaluation-kit receiver during the dynamic RTK experiment, it seems as though it would be only advantageous if RTK could be used on a UAS. The only visible difference between the SPS and RTK operation in the experiment was a slight increase in accuracy. The availability of the measurements (that is, how much data was available) was the same for when the receiver used SPS versus RTK. However, the slight increase in accuracy might not be sufficient to compel operators to use the RTK technique for UAS navigation, as additional equipment and setup will be required.
However, when using a receiver with more frequencies, such as the professional-grade receiver, we saw a great increase in accuracy. This receiver was quite large and heavy, and is most likely outside the budget considerations for many smaller UAS setups. It is also likely that using a dual-frequency receiver that is similar to the evaluation-kit receiver in size and weight could improve accuracy, and this should be tested in the future.
Further investigations should be performed to determine if the RTK technique could be used successfully for UAS navigation. A natural next step would be to place an RTK setup on an actual UAS and to test how RFI affects the RTK measurements.
Acknowledgments
This article is based on the paper “GNSS/GPS Robustness for UAS” presented at The Institute of Navigation 2016 International Technical Meeting. The research was carried out in cooperation with the Research and Engineering Center for Unmanned Vehicles in the Department of Aerospace Engineering Sciences at the University of Colorado, Boulder.
JOSHUA STUBBS has an M.Sc. in space engineering, with a focus on aerospace, from Luleå University of Technology in Sweden. In 2015, he did his master’s thesis work at the University of Colorado, Boulder, where he focused on GNSS applications for UAS.
DENNIS M. AKOS completed his Ph.D. degree in electrical engineering at Ohio University, Athens, Ohio, within the Avionics Engineering Center. He is a faculty member with the Aerospace Engineering Sciences Department at the University of Colorado and maintains visiting appointments at Stanford University and Luleå University of Technology.
Further Reading
Authors’ Conference Paper
“GNSS/GPS Robustness for UAS” by J. Stubbs and D. Akos in Proceedings of ITM 2016, the 2016 International Technical Meeting of The Institute of Navigation, Monterey, Calif., Jan. 25–28, 2016, pp. 485–493.
“Radio Frequency Devices” in Code of Federal Regulations, Title 47 (Telecommunication), Chapter I (Federal Communications Commission), Subchapter A (General), Part 15, U.S. National Archives and Records Administration, Washington, DC, 2016.
“Interference, Multipath, and Scintillation” by P.W. Ward, J.W. Betz and C.J. Hegarty, Chapter 6 in Understanding GPS: Principles and Applications, 2nd ed., E.D. Kaplan and C.J. Hegarty, Eds., Artech House, Boston and London, 2006.
“Analytical Derivation of Maximum Tolerable In-Band Interference Levels for Aviation Applications of GNSS” by C.J. Hegarty in Navigation, Vol. 44, No. 1, Spring 1997, pp. 25–34, doi: 10.1002/j.2161-4296.1997.tb01936.x.
“GPS Integrity and Potential Impact on Aviation Safety” by W.Y. Ochieng, K. Sauer, D. Walsh, G. Brodin, S. Griffin and M. Denney in the Journal of Navigation, Vol. 56, No. 1, Jan. 2003, pp. 51–65, doi: 10.1017/S0373463302002096.
Real-Time Kinematic Positioning for Unmanned Aircraft Systems
“A Precise, Low-Cost RTK GNSS System for UAV Applications” by W. Stempfhuber and M. Buchholz in the Proceedings of UAV-g 2011, the 2011 Conference on Unmanned Aerial Vehicles in Geomatics, Zurich, Switzerland, Sept. 14–16, 2011, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII 1/C22, pp. 289–293, 2011.
Examining the interoperability of precise point positioning products
By Garrett Seepersad and Sunil Bisnath
INNOVATION INSIGHTS with Richard Langley
CARRIER PHASE. We’ve all heard the term and recognize it as a more precise observable for GNSS positioning, navigation and timing than code phase, more commonly called the pseudorange. The carrier-phase measurement is the phase of the received continuous radio-frequency sinusoidal waveform that “carries” the pseudorandom noise ranging codes and the navigation messages. The underlying carrier of a satellite signal can be recovered and its phase measured at regular intervals by the receiver once it locks onto the signal.
As long as there is no interruption in the carrier tracking, the receiver can generate a continuous series of measurements of the cumulative phase or cycle count including fractional cycles. The initial value at signal lock-on is arbitrary. Ideally, it would equal the exact number of cycles (and fractional cycle) of the waveform between the antenna of the satellite and the antenna of the receiver.
If that was the case, then we could simply multiply that cycle count by the wavelength of the carrier in meters, say, and we would have the initial geometric distance (or range) to the satellite. Then we could update this value as time progresses with the receiver’s measurements and have a continuous sequence of range values, which, when corrected for satellite and receiver clock errors and other effects, would allow the receiver’s position to be accurately determined. But because we don’t know the true initial cycle count, the carrier-phase measurements are ambiguous by a constant integer amount (when measured in cycles). This characteristic of the observable is referred to as the integer ambiguity.
It was realized early in the development of GPS, that if the integer ambiguity of carrier-phase measurements could be resolved, we would have a very precise observable for positioning, navigation and timing, some two orders of magnitude more precise than the code-based pseudorange. Instead of measurement precisions of tens of centimeters, we could have precisions of tenths of millimeters.
In the early 1980s using the few test GPS satellites in orbit at the time, surveyors and geodesists developed a series of clever techniques that allowed them to make use of carrier-phase measurements to determine the baseline between pairs of receivers by estimating combinations of the ambiguities as unknowns along with the receiver relative coordinates or, for short baseline work, use a calibration procedure before starting a survey.
Now jump forward a few decades. While it is still common practice to double difference carrier-phase measurements between pairs of satellites and pairs of receivers to determine relative receiver coordinates, the technique of precise point positioning or PPP, which uses carrier-phase (and pseudorange) measurements from a single user receiver, is growing in popularity. But, the integer ambiguity problem is still with us and has to be addressed by the analysis software. The ambiguities are often estimated as real- rather than integer-valued quantities, in part because of the contribution of satellite hardware biases to the carrier-phase measurements.
However, it is possible to resolve the ambiguities to integer values by using PPP ambiguity resolution products distributed by several research organizations. In this month’s column, we take a look at the interoperability of these products for increasing the reliability and precision of position solutions and reducing the time required for a solution to converge to a required level of accuracy.
Ambiguity resolution in precise point positioning (hereafter, PPP-AR) requires that hardware delays within the GPS measurements be mitigated, which will then allow for resolution of the integer ambiguities within the carrier-phase measurements. Resolution of these ambiguities converts the carrier-phases into precise “range” measurements, with measurement noise at the centimeter-to-millimeter level compared to the meter-to-decimeter level of the C/A- and P(Y)-code pseudoranges. If the ambiguities could be isolated and estimated as integers, then that information could be exploited to accelerate PPP convergence to provide, for example, few-centimeter horizontal positioning accuracy within tens of minutes or even minutes from a cold start.
Integer ambiguity resolution of measurements from a single receiver can be implemented by applying additional satellite products, where the fractional component — representing the satellite hardware delay — has been separated from the integer ambiguities in a network solution. One method of deriving such products is to estimate the satellite hardware delay by averaging the fractional parts of steady-state real-valued or floating-point (float) ambiguity estimates, and the other is to estimate the receiver clock offset in the pseudorange and carrier-phase measurements independently by fixing the undifferenced ambiguities to integers in advance.
Similar positioning performances have been demonstrated among three approaches of different groups or agencies using the two methods: FCB (Fractional Cycle Bias), IRC (Integer Recovery Clock) and DC (Decoupled Clock). For the PPP user, the mathematical model is similar. The different PPP-AR products contain the same information and, as a result, should allow for one-to-one transformations, allowing interoperability of the PPP-AR products. The advantage of interoperability of the various products is to allow the PPP user to transform independently generated products to obtain multiple fixed solutions of comparable precision and accuracy, with no changes to the core PPP user software. An overview of the different providers and their products is presented in FIGURE 1.
FIGURE 1. Public providers of PPP-AR products. (Source: Richard Langley)
The ability to use different products would increase the reliability of a positioning solution in real-time processing, for example. If there was an outage in the generation of a particular PPP-AR product, a user could instantly switch streams to a different provider. The research presented in this article examines the PPP-AR products generated from the FCB and IRC models that have been transformed into the DC format and applied within a PPP user solution. The novelty of the research is the solution analysis using the transformed product. We examine the convergence time (time-to-first-fix and time to a pre-defined performance level), position precision (repeatability), position accuracy and solution outliers. The temporal and spatial behavior of these estimated terms is examined for the different products applied to understand the unmodeled effects responsible for incorrect solution fixes.
The Role of PPP-AR Products
The standard GPS pseudorange ( and ) and carrier-phase () observation equations are given by
(1)
where i denotes the frequency-dependent GPS measurements for frequencies L1 or L2, s represents the tracked satellite, r represents the receiver, is the geometric range between the satellite s and the user position, T is the tropospheric delay, is the first order slant ionospheric delay, γi is the frequency dependent coefficient, dts is the satellite clock and is the pseudorange hardware delay. is the ambiguity term and is the carrier-phase hardware delay, both of which are expressed in cycles and scaled by the wavelength λi . The error sources can be grouped into two main components, the geometric parameters and the timing parameters. Included in the timing parameters are the clock offsets and the hardware delay terms. Understanding the role of the hardware delays is critical in isolating the integer ambiguities.
The following equations illustrate the effects of not mitigating the hardware delay. The set of equations was simplified by combining the clock and hardware delay parameters. Processing the carrier-phase measurements with the pseudoranges (code measurements) ensures that the pseudoranges provide a reference for the carrier-phase measurements and for the clock parameters. An implication of this is the manifestation of the hardware delay present in both the estimated clock parameters and the ambiguities.
(2)
By not mitigating the hardware delay terms ( and), they are absorbed within the estimated ambiguity terms, rendering the integer nature of the ambiguity term inaccessible. The user observation equations do not contain sufficient information to solve for an integer-ambiguity-resolved user position. Ambiguity resolution would only become possible if information about the satellite hardware delays were provided to the user. The receiver hardware delay can be removed by single differencing (between satellites).
In the following section, we present an overview of the different public providers of products that enable PPP-AR, their products and how they are applied to the PPP user equations.
Public PPP-AR Products
Currently, there are three main public providers of products that enable PPP-AR. These are Scripps Institution of Oceanography, which provides regional real-time FCB products; Natural Resources Canada (NRCan), which provides post-processed and real-time DC products; and Centre National d’Etudes Spatiales (CNES), which provides post-processed and real-time IRC products.
FCB Model. The initial application of ambiguity resolution to PPP was the Uncalibrated Phase Delay (UPD) model, now called the Fractional Cycle Bias (FCB) model. The FCB method estimates the hardware delay by averaging the fractional parts of the steady-state float ambiguity estimates to be removed from common satellite clock estimates. The FCB products consist of , and , where WN indicates the Melbourne-Wübbena (widelane ambiguity) combination and IF indicates the ionosphere-free linear combination.
DC Model. The underlying concept of the decoupled clock model is that the carrier-phase and pseudorange (code) measurements are not synchronized with each other at an equivalent level of precision. The timing of the different observables must be considered separately if they are to be processed together rigorously. The decoupled clock model is a reformulation of the ionosphere-free carrier-phase and pseudorange observation equations. When combined with the narrowlane pseudorange and the widelane phase, ambiguity resolution is possible. The DC products transmitted to the user are , and .
IRC Model. The integer recovery clocks estimate constant daily widelane pseudorange/carrier-phase hardware delays by averaging arc-dependent estimates. Using float-solution estimates of the range parameters, narrowlane ambiguity resolution is performed and the ionosphere-free satellite carrier-phase clocks are estimated. In 2014, the format of the IRC products was changed from , and to a state-space uncombined representation, such that the satellite hardware delay is provided for each observable (, ) and satellite pseudorange clock ().
Summary. The three publicly provided products to enable real-time PPP-AR are listed in TABLE 1 along with their primary characteristics. The table summarizes the various measurements used, different products transmitted and the varying data rate of the transmitted products.
TABLE 1. Comparison of different publicly provided real-time products to enable PPP-AR.
Product Transformation
While the different strategies (FCB, FC, IRC) make different assumptions, there are fundamental similarities among them. The mathematical models for the PPP user are similar, as the different products contain the same information and as a result would allow for a one-to-one transformation. The following sections examine the transformation matrix used to transform the IRC and FCB products to the DC format (see Figure 2.)
FIGURE 2. Transformation of FCB and IRC products to DC input format.
FCB. The FCB products consist of , and , which are estimated in the network solution using International GNSS Service (IGS) ultra-rapid orbit and clock products. The fundamental difference between the FCB and DC products is that is not determined in the DC method, but assimilated within the clock estimates. Also, is assumed constant over a 48-hour time period, whereas in the DC method the is neither constrained nor smoothed. Here is the transformation matrix used to transform from FCB to the DC model:
(3)
where z1 is the single-differenced L1 ambiguity and zw is the single-differenced widelane ambiguity.
IRC. The original IRC products used a decoupled-like approach, where independent clocks ( and ) were transmitted for the pseudorange and carrier-phase measurements and widelane satellite hardware delays () were estimated. A redefined model was presented in 2014, where a state-space approach was adopted such that one phase bias per phase observable ( and ) was identified and broadcast. The primary benefit of such an approach is interoperability, allowing the network and user side to implement different ambiguity resolution methods. Here is the transformation matrix used to transform from IRC to the DC model: (4)
where d12 represents .
Analysis of Transformed Products. In FIGURES 3 to 5, we present the FCB and IRC products transformed to the DC format. The presented format was selected because it represents the nature of the transmitted real-time DC products. The philosophy of the DC model refers to the satellite hardware delay as an unmodeled timing error and, as such, the satellite carrier-phase clocks in Figure 3 are in units of seconds and in Figures 4 and 5 are in units of nanoseconds. Nanoseconds were selected because of the magnitude of the relative satellite pseudorange and widelane clock error, as well as being more bandwidth efficient.
FIGURE 3. Transformed FCB and IRC satellite carrier-phase clock correction on day-of-year 28 of 2015 for PRN 10. DC was included for comparison.
Figure 3 illustrates the FCB and IRC products transformed to the DC satellite carrier-phase clock. The satellite-clock corrections presented were not differenced with respect to a reference satellite, to illustrate their differences in an absolute nature. If the clocks are differenced, in a relative nature, they are equivalent. The data gaps in the FCB products are expected because of the regional nature of the products. Unlike the DC and IRC products, the FCB pseudorange clocks illustrate different trends such as those between hours 3 and 4. The noise illustrated in the IRC clock can be removed either by filtering or by differencing with respect to another satellite clock.
In Figure 4, we present the relative satellite clock error ( − ) for the transformed FCB (upper subplot) and IRC (lower subplot) products. For the original DC product (middle subplot), a simple moving average filter was applied with a bin size of five minutes to reduce the noise and illustrate the underlying equipment delay. The relative satellite clock error represents the difference between the pseudorange and carrier-phase clocks. The distinct differences of the products are easily visible, such as the filtering present within FCB and IRC products in contrast to the DC. The underlying relative satellite clock error is also significantly different in contrast to the DC product, such that FCB and IRC have an average relative satellite clock error of -0.041 ± 0.101 nanoseconds and -0.645 ± 0.005 nanoseconds, respectively, whereas the DC has an average of 8.465 ± 1.546 nanoseconds.
FIGURE 4. Transformed FCB and IRC products to code-phase relative clock correction on day-of-year 28 of 2015 for PRN 10. DC was included for comparison. Linear trend has been removed.
Figure 5 shows the relative satellite widelane clock error for the transformed FCB (upper subplot) and IRC (lower subplot) products. For the original DC product (middle subplot), a simple moving average filter was applied with a bin size of five minutes, to reduce the noise and illustrate the underlying equipment delay. The relative satellite clock error represents the difference between the widelane clocks and phase clocks. Similar to the relative satellite clock error, the differences in the transformed relative satellite widelane clock error are noticeable. As expected, the transformed FCB has a constant widelane estimate of -0.24 nanoseconds, whereas the transformed IRC and DC have an average widelane estimate of 0.0589 ± 0.002 and 3.6704 ± 0.34 nanoseconds, respectively.
FIGURE 5. Transformed FCB and IRC products to code-phase relative widelane clock correction on day-of-year 28 of 2015 for PRN 10. DC was included for comparison. Linear trend has been removed.
Performance of Transformed Products
One of the metrics we can use to examine the performance of the transformed products is the quality of the solution in the position domain. The solutions were examined with respect to the time for convergence to a pre-defined threshold and position stability. We used five stations from the Scripps Orbit and Permanent Array Center (SOPAC) network for days 23 to 30 of 2015. These five stations were selected because of the regional nature of FCB products provided by SOPAC. We show the results for site Brand Basin (BRAN) on day-of-year 30 of 2015 as it reflects the performance of the whole dataset processed.
In FIGURES 6 to 8, we show the varying convergence periods at the site BRAN on day-of-year 30 for the “float” and “fixed” solutions using the different PPP-AR products, where fixed means the ambiguity-resolved solution and float the unresolved solution. Figure 6 uses the decoupled clock products, and the fixed solution performs as expected. After a few minutes, the solution attains the correct ambiguity candidate, and a fixed state is maintained.
FIGURE 6. Position errors for site BRAN located in Burbank, Calif., on day-of-year 30 of 2015 illustrating the difference between the float and fixed solutions using the DC products.FIGURE 7. Position errors for site BRAN located in Burbank, Calif., for day-of-year 30 of 2015 illustrating the difference between the float and fixed solutions using the IRC products.FIGURE 8. Position errors for site BRAN located in Burbank, Calif., for day-of-year 30 of 2015 illustrating the difference between the float and fixed solutions using the FCB products.
The performance of the fixed solution using the IRC products is depicted in Figure 7. Initial convergence is similar to the DC products in the northing and easting components where a fixed state is attained after a few epochs. In the up component, the solution quality deteriorates after 30 minutes. What is also easily visible is the solution sensitivity to changes in the satellite geometry. As the number of satellites changes, the fixed ambiguities change, causing datum shifts in the user solution.
Similar trends were also observed when the transformed FCB products were used, with the results presented in Figure 8. The solution deterioration is most evident in the easting component, as the incorrect integer candidate is selected.
Challenges of Interoperability
Interoperability of the various PPP-AR products is a challenging task because of the different qualities of the publicly available products, limited literature documenting the conventions adopted within the network solution of the providers, and unclear definitions of the corrections.
In TABLE 2, we summarize the various qualities of the products we used in the study, showing why it was challenging to perform a consistent comparison. IRC products were generated from a network of reference stations globally distributed and in real time. Similar to the IRC products, the DC products were generated from a global network of solutions, but post-processed, and the FCB products were based on a regional network of reference stations, but were available in real time. Post-processed orbits and clocks have an accuracy of ~2.5 centimeters and ~75 picoseconds, respectively, whereas the predicted half of ultra-rapid orbits and clocks have an accuracy of ~5 centimeters and ~3 nanoseconds, respectively. While it is evident in the existing literature that PPP-AR is possible in real time, the solution is rather sensitive to changes experienced by the PPP user solution, such as varying local conditions and satellite geometry. The sensitivity is illustrated in Figures 7 and 8 with solution jumps typically occurring when there is a change in the number of satellites.
TABLE 2. Summary of the different quality of products provided by public providers to enable PPP-AR.
The general assumption when PPP-AR products are estimated within a network is that the PPP user would follow similar conventions when using the products. Consequences of different conventions adopted may result in incorrect ambiguities being resolved. For example, if inconsistent satellite antenna conventions were adopted between the network and user, then when phase wind-up corrections are applied, fractional cycles would be introduced. The introduced fractional cycles would result in incorrect ambiguities being resolved. FIGURE 9 shows the orientation of the spacecraft body frame for GPS Block IIR/IIR-M satellites adopted in the IGS axis convention (subplot (a)) and those provided in the manufacturer specifications (subplot (b)). The difference between the manufacturer specifications and IGS axis convention is the orientation of the x- and y-axes.
FIGURE 9. Orientation of the spacecraft body frame for GPS Block IIR/IIR-M satellites as (a) adopted within the International GNSS Service axis convention, and (b) those provided in the manufacturer specifications.
Conclusions
The mathematical model for the PPP user is similar for all PPP-AR products, as the different products contain the same information and, as a result, would allow for one-to-one transformations, allowing interoperability of the PPP-AR products. Interoperability of the various PPP-AR products would allow the PPP user to transform independently generated PPP-AR products to obtain multiple fixed solutions of comparable precision and accuracy. The ability to provide multiple solutions would increase the reliability of the solution such as in real-time processing: if there was an outage in the generation of the PPP-AR products, the user can instantly switch streams to a different provider.
We looked at the PPP-AR products provided by three organizations and examined position solutions for a set of stations in the SOPAC network with respect to convergence time to the pre-defined threshold and position stability.
Using the decoupled clock products, we found that the fixed solutions performed as expected. After a few minutes, a solution attains the correct ambiguity candidate and a fixed state is maintained. Unlike the fixed solutions using the decoupled clock products, instantaneous convergence was not attained in the horizontal and vertical components when the transformed IRC and FCB products were used. The ambiguity-resolved solutions were sensitive to changes in the satellite geometry. As the number of satellites change, the fixed ambiguities change, causing datum shifts in the user solution.
The unstable solutions from both transformed products are attributed to the magnitude of the relative satellite code and widelane clock errors. Additional refinement of the transformation model is required as the satellite hardware delay has not been completely mitigated. Mismodeling of the hardware delay was absorbed by the ambiguity terms, causing incorrect fixed solutions.
Future Research
Future prospective research includes refinement of the proposed transformation models to include the mismodeled effects, thus providing the user with a more reliable solution. The functional model needs to be further examined to ensure that the corrections were applied consistently. Further analysis of the instability of the user solution is required, as solution jumps typically occur when there are changes in the number of satellites tracked. Also to be analyzed are the post-fit residuals, to examine the effects of mismodeling. The temporal and spatial behavior of the estimated terms will be examined for the different products used to understand the unmodeled effects that introduce incorrect solution fixes. We would also consider increasing the number of reference stations to further test the reliability of the transformed products under varying user conditions.
Acknowledgments
We acknowledge Paul Collins, Jianghui Geng and Denis Laurichesse for our valuable discussions and their suggestions. The research was funded by the Natural Sciences and Engineering Research Council of Canada. The results we have presented were derived from data and products provided by Natural Resources Canada, Scripps Institution of Oceanography, Centre National d’Etudes Spatiales and the International GNSS Service.
This article is based on the paper “Examining the Interoperability of PPP-AR Products” presented at ION GNSS+ 2015, the 28th International Technical Meeting of The Satellite Division of the Institute of Navigation held in Tampa, Fla., Sept. 14–18, 2015.
GARRETT SEEPERSAD is a Ph.D. candidate at York University, Toronto, Canada, in the Department of Earth and Space Science and Engineering. He completed his B.Sc. in geomatics at the University of the West Indies and his M.Sc. in geomatics engineering at York University. His area of research currently focuses on the development and testing of PPP functional, stochastic and error-mitigation models.
SUNIL BISNATH is an associate professor in the Department of Earth and Space Science and Engineering at York University. His research interests include geodesy and precise GNSS positioning and navigation.
Further Reading
• PPP Ambiguity Resolution Techniques
“Review and Principles of PPP-RTK Methods” by P.J.G. Teunissen and A. Khodabandeh in Journal of Geodesy, Vol. 89, No. 3, 2014, pp. 217–240, doi: 10.1007/s00190-014-0771-3.
“A Novel Un-differenced PPP-RTK Concept” by B. Zhang, P.J.G. Teunissen and D. Odijk in Journal of Navigation, Vol. 64, Supplement S1, 2011, pp. S180–S191, doi: 10.1017/S0373463311000361.
“Isolating and Estimating Undifferenced GPS Integer Ambiguities” by P. Collins in Proceedings of the 2008 National Technical Meeting of The Institute of Navigation, San Diego, Calif., January 28–30, 2008, pp. 720–732.
“Resolution of GPS Carrier-Phase Ambiguities in Precise Point Positioning (PPP) with Daily Observations” by M. Ge, G. Gendt, M. Rothacher, C. Shi and J. Liu in Journal of Geodesy, Vol. 82, No. 7, 2008, pp. 389–399, doi: 10.1007/s00190-007-0187-4.
“Integer Ambiguity Resolution on Undifferenced GPS Phase Measurements and Its Application to PPP” by D. Laurichesse and F. Mercier in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, Sept. 25–28, 2007, pp. 839–848.
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. Recipient of The Institute of Navigation 2014 Bradford W. Parkinson Award.
Proposed GNSS Navigation Messages for Improved Performance
By Wentao Zhang and Yang Gao
INNOVATION INSIGHTS by Richard Langley
TIME-TO-FIRST-FIX, commonly known by the initialism TTFF, is the elapsed time between the powering on or starting up of a GNSS receiver and when it successfully computes either a two-dimensional (height constrained) or three-dimensional position fix and sets its clock to the correct time. A three-dimensional fix requires simultaneous receiver measurements on the signals from a minimum of four satellites along with the satellites’ positions (ephemerides) and the offsets between the individual satellite clocks and the GNSS system time.
TTFF depends crucially on the availability and timeliness of the satellite ephemerides and clock information when a receiver starts up and, accordingly, there are three types of start-up with correspondingly different TTFF.
A cold start (sometimes also called a factory start) occurs when the receiver has no knowledge of its current position, time or the positions of the satellites and their clock offsets. The receiver must do a blind search of the sky trying different possible Doppler frequency shifts and pseudorange delays for all the satellites in the constellation. Once satellites are found and tracked, the ephemerides and clock information must be collected. This is repeated in each satellite’s navigation message every 30 seconds. In addition, the information on the offset between GNSS system time and UTC must be collected along with the ionospheric delay correction parameters and the almanac (an approximate ephemeris for all active satellites in the constellation) to be used for faster subsequent signal acquisition. This data is only transmitted once in the 12.5-minute-long navigation message. Therefore, the TTFF for a cold start can take up to 12.5 minutes and even longer especially if the GNSS signals are hard to acquire such as in obstructed environments.
A warm start, or what we might call normal operation, occurs when the receiver has some a priori information on its position, the time and the approximate locations of the satellites. Typically, this means knowing the receiver position to within a few hundred kilometers, time to within 10 minutes or so, and a reasonably fresh almanac. Armed with that information, a receiver knows which satellites should be visible to it and can quickly acquire and track satellite signals and obtain the satellite ephemeris and clock information. Since that information is repeated every 30 seconds, TTFF for a warm start can be 30 seconds or less.
A hot start occurs when a receiver is powered on after being off and stationary for a short interval and it therefore has a very good estimate of its position and the current time and valid satellite ephemeris and clock data. TTFF for a hot start, therefore, is typically only a few seconds. This mode of receiver operation would also apply to scenarios where all signals are temporarily lost in road or rail tunnels or where a number of signals are momentarily blocked by obstructions causing a break in position fixing.
Fast first fixes were traditionally only possible when a receiver had a clear view of the sky and could readily acquire the navigation messages. Pseudorange measurements can be made, however, even if satellite signals are somewhat attenuated in strength to the point that navigation messages cannot be acquired. Position fixing in this case would be possible if the receiver could obtain the navigation information from elsewhere. Over the past decade or so, assisted GNSS techniques have been developed to provide frequently refreshed navigation information over cellular telephone networks, for example. But would there be a way to achieve fast first fixes autonomously without reliance on these assisted techniques? Not with the signals presently being transmitted by either the mature or nascent constellations, it seems, but in this month’s column, we look at proposed changes to the way navigation messages are formulated that could result in a future satellite navigation system providing faster fixes effectively giving receivers higher sensitivity and stronger performance.
“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. Email him at lang @ unb.ca.
Despite some differences in their structures, different GNSS broadcast navigation (NAV) messages usually consist of two parts: immediate (primarily ephemeris) and non-immediate (primarily almanac) data. The immediate data is repeated at a much shorter interval than the non-immediate data, and expires much sooner than the non-immediate data. Taking GPS as an example, the civilian navigation (CNAV) messages consist of five subframes with each lasting six seconds, as depicted in FIGURE 1. The first three subframes provide the ephemeris, with the content repeated every 30 seconds and updated every two hours, while the last two subframes provide the almanac for each satellite in 25 pages, with the content updated nominally every six days (according to the GPS Interface Specifications document), but updates are actually daily.
FIGURE 1. Frame structure of GPS CNAV messages.
Depending on the accuracy of receiver time and the availability of previously collected ephemerides (the immediate data) when powered on, GNSS user equipment (UE) might experience cold, warm or hot starts, among which the warm start is the most common case. In the widely accepted definition for warm start, no valid ephemeris is available, but the receiver time is roughly known at startup.
As depicted in FIGURE 2, the position fix sequence by a standalone GNSS UE normally consists of signal acquisition, tracking, bit synchronization, frame synchronization, ephemeris downloading, measurements taking and position computation. In performing a regular warm start, signal acquisition usually takes only a few hundred milliseconds for a GPS device in open-sky environments. However, under weak signal conditions, signal acquisition might take much longer (say a few tens of seconds). Once the signal is acquired, the tracking loop is activated, and immediately after the signal is pulled in the process of data-bit synchronization is started. This process takes a few hundred milliseconds to several seconds depending on signal strength and algorithm efficiency. In a stable tracking status, the navigation bits are collected sequentially one by one. Collecting a complete copy of a GPS ephemeris takes about 18 seconds in open-sky environments but may take minutes or even forever in weak signal environments due to an increased bit error rate (BER). As soon as the ephemeris downloading from three to four satellites is completed and the measurements are made, the user position fix usually can be obtained immediately. Therefore, in weak signal environments, the obstacles to fast time-to-first-fix (TTFF) are primarily signal acquisition and ephemeris downloading, and in open-sky environments the obstacle mainly lies in the time needed for ephemeris downloading.
FIGURE 2. Typical sequence of position fix process in standalone GPS user equipment (Msr=measurements).
For a GNSS UE in an open-sky environment on the Earth’s surface, the minimum received signal level for GPS L1 is around -130 dBm according to the interface specifications. For other GNSS signals, the nominal received signal levels are approximately the same.
However, in some extreme cases, such as urban canyon, foliage and indoor environments, the signals finally arriving at a receiver’s antenna could be heavily attenuated by 30 dB or even more. Working under such conditions requires GNSS UE to have high-sensitivity capability.
When the GNSS signal strength drops to a certain level, it causes immediate difficulties in the GNSS receiver tracking loop and for ephemeris downloading. Firstly, the parameters of the tracking loop, designed for normal signal strengths, are no longer optimum for either obtaining enough gain for signal detection or for maintaining signal tracking. Secondly, BER increases with decreasing signal strength. When the signal carrier-to-noise-density ratio drops below 27 dB-Hz, even if signal tracking is maintained, the increased BER would make it difficult for successful decoding of NAV messages.
Sensitivity improvements for a GNSS receiver can involve contributions from the antenna, the RF front end and baseband signal processing. In the signal processing, to obtain adequate processing gain in signal-to-noise ratio for signal detection, combined coherent and non-coherent integrations are needed. An approximate relationship for calculating such processing gain is given in Equation (1). Considering that non-coherent integration is subject to squaring loss, for a fixed total integration period (TI), increasing the coherent period (Tc) is more efficient for achieving higher processing gain. However, without knowing the navigation bits, the coherent integration is limited within a 1-bit period or 20 milliseconds for GPS signals.
(1)
To improve sensitivity to -160 dBm, coherent integration over multiple bits is desired. Therefore, valid navigation bits as well as the bit boundaries are needed for data wipe-off. For this purpose, previously collected navigation bits can be directly used if they are still valid or fresh navigation messages from different sources, including ephemeris and almanac, can be used to recover the navigation bits.
GNSS Assistance Technologies
The existing efforts for improving TTFF and sensitivity for GNSS UE include developing assistance systems and implementing new algorithms for UE. The concept of AGPS goes back to the late 1990s when lots of patents were filed and then granted in early 2000s. Seeing the challenges of TTFF and sensitivity for standalone GPS devices, the general idea from the patents is to provide assistance information to GNSS UE, such as time, rough location, a list of visible satellites, the Doppler shift of each satellite, ephemerides and so on, in a way to speed up each stage in the process of a position fix (Figure 2). With a series of AGPS specifications embodied in the 3GPP and Open Mobile Alliance standards since 2001, AGPS-enabled products have become quite popular in the GNSS marketplace.
The assistance data definitely brings enhanced performance in TTFF and sensitivity for GNSS UE, but it is a challenge when network connectivity is not available. A technology often referred to as ephemeris extension (EE) was introduced by Global Locate and SiRF, which enables fast TTFF and high sensitivity for GNSS UE even without network connectivity. According to the descriptions of the long-term orbit used by Broadcom and InstantFix used by CSR, both are based on orbit determination theory and provide alternative ephemerides with a validity period extending to a few days, rather than two hours for the regular GPS ephemerides. As of today, a variety of EE products are available from many companies and research institutes, and EE has become a standard feature for GNSS products in the market place.
Limitations of Existing GNSS Assistance Technologies
In spite of the benefits to TTFF and improved sensitivity, the assisted GNSS (AGNSS) and EE technologies have obvious limitations, as detailed in TABLE 1. Building and maintaining the AGNSS infrastructure require significant efforts and continuous cost. Any AGNSS-capable UE, unlike standalone GNSS UE, are tied to good signals from the subscriber cellular phone networks to get assistance data on time, which substantially limit their areas of operation. The EE technologies consist of server-based and client-based modes. Client-based EE is good for standalone UE, but the accuracy is subject to the validity of the embedded Earth orientation parameters (EOPs), and the quantity and quality of the local data collection. Server-based EE is able to provide better accuracy, but it also needs support from the global infrastructure for data collection and is subject to network connectivity. Table 1 clearly indicates that AGNSS and EE can only be beneficial under certain prerequisite conditions, such as with network connectivity and data availability. In other words, even with the above-described technologies, fast TTFF and high sensitivity may still not be obtainable when those prerequisite conditions are not met, which is not uncommon in practical use.
TABLE 1. Comparison of assisted GNSS (AGNSS) and extended ephemeris in improving time-to-fist-fix (TTFF) and sensitivity.
Suggested New GNSS NAV Messages
The fundamental cause of the problem related to TTFF and sensitivity, in our view, lies in the congenital weakness of the design of the existing GNSS NAV messages. Taking GPS as an example, the contents of GPS subframes 1–3 are updated every two hours, although the ephemeris is valid for up to four hours. It is challenging for standalone GPS UE working in weak signal environments to catch up with such frequent ephemeris updates. Working properly during the past two hours does not mean that the UE can work properly in the next two hours if ephemerides are not downloaded in time. The NAV messages received two hours ago cannot be used for data aiding in the subsequent two hours to improve tracking sensitivity. For startups under normal signal conditions, the UE, if missing the start of subframe 1, have to wait 30 seconds to get to the next subframe 1 to download a complete copy of the ephemeris. Successful startups four hours ago also do not help much to reduce the TTFF in the subsequent startups, as time is needed again for ephemeris downloading.
For other GNSSs, some specifications of their NAV messages are listed in TABLE 3. According to these specification, the downloading of Galileo ephemerides takes at least 30 seconds, and if the start of the first ephemeris page is missed, it will take at least 50 seconds to get a complete copy. So, from this perspective, the Galileo TTFF for standalone devices is expected to be longer than that for GPS. As to BeiDou, given the high degree of similarity between BeiDou D1 and GPS CNAV messages, it is expected that for standalone BeiDou UE, TTFF is also similar to standalone GPS UE. For GLONASS, the downloading takes just about10 seconds, and it will take about 30 seconds to get a complete copy of the ephemeris if the start of the first ephemeris string is missed. Therefore, in this regard, the GLONASS TTFF for standalone devices is expected to be the fastest among the GNSSs. It is worth noting that the GLONASS ephemeris, unlike that of other GNSSs, comprises Cartesian coordinates, velocity components and solar/lunar gravitational accelerations at the reference time, with the content valid over about 0.5 hours. Upon receiving the ephemeris, the UE is to calculate the satellite orbit by numerically integrating the motion equations that include the second zonal geopotential coefficients through a fourth-order Runge-Kutta method. Since the designed NAV messages for GPS, GLONASS, BeiDou and Galileo are all valid for only short periods (see Table 3), they are all subject to the aforementioned limitations.
TABLE 3. Comparison of the NAV messages for GPS/GLO/BD(D1)/GAL(F/NAV)/New GNSS.
The common weaknesses in the NAV messages of GPS, GLONASS, BeiDou and Galileo described above can be overcome and fast TTFF and high sensitivity can be facilitated through the design of new NAV messages, when the following guidelines are followed:
Update interval, as short as possible
Repeat interval, as high as possible
Length of ephemeris content, as short as possible
Ephemeris life expectancy, as long as possible
Let’s take a closer look at the GPS CNAV messages in terms of the above four guidelines. In the GPS CNAV messages, the primary content includes:
Satellite clock
Satellite ephemeris
Ionosphere information
UTC parameters
Almanac
Two types of atomic clocks, rubidium and cesium, with stabilities of 10-12 to 10-13 are used on the GPS satellites. Given such stabilities, it is possible to have the clock parameters updated at an interval much longer than two hours, without introducing significant errors in the pseudorange observations. For the Keplerian parameters in the GPS ephemerides, they are derived from the fitting of four-hour orbit curves. The orbit, represented by the Keplerian parameters plus perturbation corrections, gives the overall best fitting of the whole orbit segment. If fitting over a longer orbit curve, it would be harder for the fitted orbit to agree well with each small portion of the original orbit. A set of Keplerian orbital parameters can be a good approximation of a short orbit segment (say four hours), but can hardly be the case over a long period (say 24 hours). Frequent updating of the ephemeris content is therefore indispensable in order to guarantee the orbit accuracy using this approach. As a result, there is not much room for extending the ephemeris update interval or equivalently to lower the update frequency.
GPS CNAV messages include ionosphere information using the Klobuchar model, UTC parameters for relating GPS Time to UTC, and the almanac providing the rough orbits for all GPS satellites in service. According to the GPS Interface Specifications, all these messages will be updated at least once every six days, but they are typically updated on a daily basis.
Based on the above analysis, it can be concluded that, in GPS CNAV messages, the only part that changes frequently is the ephemeris (primarily the Keplerian parameters). To facilitate fast TTFF and high sensitivity, we should reduce the update frequency of the GPS CNAV message. For that, the key is to find a way to minimize the update frequency of the ephemerides.
Taking a close look at the satellite orbit may help us find a hint. For a satellite in space, given the initial conditions (position, r, velocity, , and so on) in Equation (2) at time t0 , the succeeding orbit, r(t), can be obtained by integrating the accelerations, , in Equation (3), as illustrated in Equation (4).
(2)
(3)
(4)
To ensure the accuracy of the derived orbit, r(t), the forces exerted on the satellites that result in the acceleration, (t), should be well modeled. The forces are both gravitational and non-gravitational.
Standard gravitational force models embedded in UE can be independently used for years without introducing significant accuracy loss. As to the force of solar radiation, it is related to the reflectivity and attitude of the solar panels of the satellite, which can also be well modeled by some slow-varying and satellite-dependent parameters. If a set of such solar radiation parameter(s) along with some satellite initial conditions (position and velocity) can be provided with a certain period (say one day), the satellite orbit can be derived in the UE through some embedded force models.
By now, we have found what we are looking for — namely, the solar radiation parameter(s) together with the satellite initial condition at a reference time, which can be the ideal content for our new ephemeris that can deliver a long orbit even if updated at a low frequency.
Consider that, at any epoch, the satellite position and velocity expressed in Cartesian form (r, ) can also be identically expressed in Keplerian form through the set of standard elements as is currently done with GPS.
The initial condition expressed in Keplerian form may give a better idea of what the orbit looks like and may have advantages for message encoding and sanity checks when it is adopted as the ephemeris content.
The above fundamental analysis leads us to propose the new GNSS NAV messages provided in TABLE 2, which comply with the previously mentioned guidelines and therefore should be able to inherently facilitate fast TTFF and provide UE with high sensitivity.
Note that the EOP data in the above table, used for relating coordinates in an Earth-centered Earth-fixed (ECEF) frame and those in an Earth-centered inertial (ECI) frame, are slowly varying parameters. The update interval for each part of the new NAV messages in Table 2 is one day, but for the almanac part, the update interval can be possibly extended to a few days similar to that currently used for GPS. In the ephemeris part, the proposed messages contain the six basic Keplerian elements and one solar radiation parameter for a selected reference time (t0). Once the ephemeris is downloaded, the six Keplerian elements can be immediately transformed to Cartesian position, r(t0), and velocity, (t0), in the ECEF frame, and further converted to the initial condition in the ECI frame to derive the entire orbit through Equation (4).
TABLE 2. Proposed content of new GNSS navigation messages.
Compared to the current GPS ephemeris, Table 2 contains many fewer parameters, so it is possible to have the new GNSS ephemeris and clock data packed in only two subframes, assuming that the data rate, word structure and subframe length are the same as for GPS CNAV messages. For the remaining parts listed in Table 2, they can be packed into multiple pages of 2 subframes in a similar way as the pages of subframes 4 and 5 in GPS CNAV messages. Therefore, we have the frame structure of the proposed new GNSS NAV messages as depicted in FIGURE 3. Considering that the contents of the first two subframes play a primary role in TTFF, the pages of subframes 3 and 4 are not further discussed here.
FIGURE 3. Frame structure of the new GNSS NAV messages.
Advantages of the New NAV Messages
The content of the new NAV messages have been proposed in the last section, but the detailed format design is beyond the scope of this article. In TABLE 3, a comparison of the new NAV messages to the current GPS, GLONASS (GLO), BeiDou (BD) and Galileo (GAL) messages is presented. For the convenience of comparisons, the same data rate (50 bits per second [bps]) and the same length of subframe (6 seconds) as for the GPS CNAV messages have been used for the new GNSS NAV messages.
Compared to other GNSS NAV messages, the new NAV messages have a smaller size, but the contained ephemeris has a longer life and, as a whole, the new NAV messages just need to be updated once every 24 hours. To help understand the advantages of the new NAV messages, we have made several comparisons.
Standalone UE, New GNSS vs. GPS. For any new GNSS that deploys the new NAV messages, the UE just need to download the ephemeris from the satellites once in a whole day, whereas current GPS UE need to do it 12 times. In each downloading, it takes about 18 seconds for current GPS UE compared to about 12 seconds for the new GNSS UE. So there is no doubt that, from the TTFF perspective, the new NAV messages have incomparable advantages over the current GPS ones. Once a complete copy of the new NAV messages is downloaded, it can be used for data aiding in tracking loops for the rest of the whole day, even without network connections in weak signal environments. However, for current standalone GPS UE, they have to be in a strong signal environment to acquire fresh NAV messages every two hours. Otherwise there could be no position fix available in the next two hours due to the stale NAV bits and expired ephemerides. So, from a sensitivity point of view, a GNSS with the new NAV messages (referred to as new GNSS below) will also have incomparable advantages over GPS.
Assisted UE, New GNSS vs. GPS. There are three purposes for assistance information for mobile devices: 1) to expedite signal acquisition; 2) to save time in ephemeris downloading; and 3) to have navigation bits for data aiding in the tracking loops. For assisted GPS UE and assisted GNSS UE with the new NAV messages, there is not much difference in the first aspect, as the assistance data, such as a satellite vehicle list, Doppler frequency, code phase, location and time, are common to both. For the second and third purposes, the assistance data sent from the assisting network to the UE are only needed once per day using the new NAV messages because they are updated only once per day. For assisted GPS UE, the assistance data are needed once every 2 hours, which means that GPS UE need frequent network connectivity and more network bandwidth for data transportation. In addition, as the size of a GPS frame is larger than the frame of the proposed new NAV messages, the time delay in transporting the assistance data will be longer in a GPS assistance network.
New GNSS, Standalone vs. Assisted. When the new GNSS NAV messages are deployed, as the messages are only needed to be downloaded once a day, the assisted UE mostly show advantage in sensitivity and the required time for signal acquisition. Since signal acquisition is difficult only when the signal becomes weaker than a certain level, the performance of standalone and assisted new GNSS UE is expected to be comparable under normal signal conditions. Under weak signal conditions, as long as the NAV messages are received once a day, the performance in tracking sensitivities for both standalone and assisted UE is also expected to be comparable.
Feasibility Considerations
Since the proposed update interval for the new NAV messages is 24 hours, a period much longer than that currently used by all constellations, some immediate concerns may arise, such as:
Is the orbit/clock derived from the ephemeris good enough for 24 hours?
Is the calculation load for deriving satellite orbits affordable for a UE?
The advancement in orbit determination and EE technologies can help relieve the worry on the first concern. For the JPL predicted orbit and clock states, it is claimed that the user range error (URE) of around one meter for one day and URE of less than 10 meters for seven-day predictions can be obtained.
For a future GNSS that deploys the proposed new NAV messages, an orbital determination center (ODC) on the ground should be able to provide orbit predictions better than or at least comparable to those already obtained. Every 24 hours, as the intermediate results of the orbit predictions are obtained in the ODC, the new ephemeris data can be extracted and packed as one part of the new NAV messages. Once uploaded to the satellites and broadcast to GNSS UE on the ground, they can be used in deriving satellite orbits. The accuracies of the orbits/clock finally derived by GNSS UE will be subject to the accuracy of ephemeris, clock coefficients, EOPs and force models embedded in UE.
The EOP data, describing the irregularities of the Earth’s rotation, are needed for coordinate transformations between ECEF and ECI, so the up-to-date EOP data carried in the new NAV messages ensures no accuracy loss in such transformations. For the force models embedded in GNSS UE, accuracy is not a problem as long as they are the same as that used by the ODC.
As to the satellite clock, it is desired that, even if the clock coefficients are updated once per day, the accuracy of the predicted clock is still sufficient for navigation. For the current spaceborne clocks on GPS satellites, they are primarily rubidium atomic clocks with stability not better than about 10-13. The advancement of atomic clock technologies is fast, especially in recent years, and the era of rubidium, cesium and hydrogen maser clocks is evolving to ytterbium and even optical atomic clocks. As of today, atomic clocks as stable as 10-18 have been operated in laboratory settings. A project called the Space Optical Clock aims to put a lattice optical clock with a stability of 10-16 on the International Space Station by 2020. So it is foreseeable that new GNSSs should be able to deploy atomic clocks with stability several orders better than those currently deployed. At the stability of 10-16, the clock will only introduce millimeter-level errors in ranging in a 24-hour period. With such a stable satellite clock, there should be no accuracy concerns with clock data being updated once per day.
Once the broadcast ephemeris is received by a UE, numerical integration can be started to derive the satellite orbit. During the numerical integration, the calculation load is primarily dependent on the following factors: 1) the length of numerical integration; 2) the numerical integration step size; 3) the order of the integrator; and 4) the complexity of local force models. Regarding the run-time necessary for orbital numerical integration on an embedded system, some published results indicate that a three-day prediction (numerical integration) takes only around 0.6 seconds on a 600-MHz processor with floating point unit. So a 12-hour integration would take only about 0.1 seconds on the same platform. As of 2014, for the popular high-end smartphones on the market, the speed of embedded processors ranges from 1.2 to 2.5 GHz with dual- or quad-cores. Considering the drastically growing computation power of mobile processors and the potential of further algorithm optimizations in orbital integration, the calculation load of numerical integration for a 12-hour interval is not at all an issue on a mobile device today, much less in the future.
The GPS system designers four decades ago might not have realized that GPS would become so popular in the 21st century. Fast TTFF and high sensitivity have become standard requirements. The growing power of the application processors has also been beyond the imagination of people 40 years ago. So in their design, fast TTFF and high sensitivity might not have been given too much attention. The GPS modernization program is an attempt to meet the growing expectation on the system performance in the applications for today and the near future. In view of this, there is no reason not to give special considerations to inherently support fast TTFF and high-sensitivity applications when investigating and designing a new GNSS. Certainly, such efforts can be found both in recently launched GPS (Block IIF) and Galileo satellites, such as the pilot channels, but navigation under weak signal conditions for future standalone GPS and Galileo devices is still susceptible to the frequent change of NAV messages (see Table 3).
Conclusions
In this article, we have analyzed the benefits and limitations of the existing technologies (AGNSS and EE) widely adopted to improve TTFF and sensitivity performance, and pointed out the weakness in current GNSSs. Instead of seeking solutions in the user terminal, this article proposes to deploy new NAV messages on future GNSSs, with the contents updated once a day, to inherently facilitate fast TTFF and high sensitivity in the standalone GNSS UE. A future GNSS that uses such new NAV messages will have significant advantages for both standalone and assisted UE.
Acknowledgment
This article is based, in part, on the paper “New GNSS Navigation Messages for Inherent Fast TTFF and High Sensitivity” presented at the 2015 Pacific PNT Meeting of The Institute of Navigation, held in Honolulu, Hawaii, April 20–23, 2015.
WENTAO ZHANG is a Ph.D. student in the Department of Geomatics Engineering at the University of Calgary. His research interest lies in different location technologies, and he is focusing his research on potential new GNSS navigation messages in an attempt to inherently improve time-to-first-fix and receiver sensitivity.
YANG GAO is a professor in the Department of Geomatics Engineering at the University of Calgary. His research expertise includes both theoretical aspects and practical applications of satellite-based positioning and navigation systems. His research focuses on high-precision GNSS positioning and multi-sensor integrated navigation systems.
FURTHER READING
Authors’ Conference Paper
“New GNSS Navigation Messages for Inherent Fast TTFF and High Sensitivity” by W. Zhang and Y. Gao in Proceedings of The Institute of Navigation 2015 Pacific PNT Meeting, Honolulu, Hawaii, April 20–23, 2015, pp. 131–141.
Assisted GNSS
A-GPS: Assisted GPS, GNSS, and SBAS by F. van Diggelen, published by Artech House, Boston and London, 2009.
“Assistance When There’s No Assistance — Long-Term Orbit Technology for Cell Phones, PDAs” by D. Lundgren and F. van Diggelen in GPS World, Vol. 16, No. 10, Oct. 2005, pp. 32–36.
“SiRF InstantFix II Technology” by W. Zhang, V. Venkatasubramanian, H. Liu, M. Phatak and S. Han in Proceedings of ION GNSS 2008, the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Ga., Sept. 16–19, 2008, pp. 1840–1847.
The Soyuz launcher is transferred to the launch pad. (Credit: Arianespace)
I had the honour of the first question at today’s Galileo press conference hosted by the European Space Agency (ESA), and it was about the status of the satellites launched last March. The answer to that question and others are below.
The satellites being launched this evening are destined for Plane A and will be its first occupants. They will occupy slots 5 and 8 in the plane. They will undergo a 76-day-long in-orbit test procedure before being made available to users.
The satellites launched in March, Galileo satellites 7 and 8 (a.k.a. FOC-FM3 or GSAT0203 and FOC-FM4 or GSAT0204 using PRNs 26 and 22, respectively), have essentially completed in-orbit testing and should be available to users sometime this month.
The ground segment is to be modified to enable the production of navigation messages for satellites 5 and 6 (a.k.a. FOC-FM1 or GSAT0201 and FOC-FM2 or GSAT0202 using PRNs 18 and 14, respectively) launched in August 2014 into wrong orbits (a “kind of Plane D” according to one of the ESA officials at the press conference). This will occur by the beginning of 2016 when these satellites will then be available for testing in navigation and positioning applications. They will not be included in the broadcast almanac as the orbits are too far from nominal to be represented by the standard almanac format. But the signals should be fully usable by those receivers and chipsets that can acquire and track Galileo satellites without an almanac. Testing will be carried out to see if the satellites can become part of the operational constellation.
IOV-4 (a.k.a. FM4 or GSAT0104 using PRN 20), the in-orbit validation satellite that suffered a power failure in May 2014 and is only broadcasting on the E1 frequency, may become operational for single-frequency use if suitable ground segment modifications can be made.
The next Galileo launch after this evening’s will be in December on a Soyuz launcher when another two satellites will be placed into orbit.
In 2016, there will be one launch but using, for the first time, the Ariane 5 launcher, to place four satellites into orbit.
In 2017, there will be two launches: a Soyuz launch orbiting two satellites, and an Ariane 5 launch, orbiting four satellites.
A 30-satellite constellation will be in place by 2020, following ESA’s slogan “30 satellites by 2020,” with 10 satellites per plane with each plane having two spare satellites. This should be feasible as two satellites are now being manufactured every three months. Twenty-four satellites is the minimum for Galileo operational capability.
“Time waits for no one,” Mick Jagger lamented in song when he turned 30. But tonight, on the evening of June 30, our clocks will stand still for a moment, waiting for the passage of a “leap second.”
The International Earth Rotation and Reference Frames Service (the world’s time monitor) has decreed that the last day of June will contain an extra second. Rather than the usual 86,400 seconds in a day, June 30 will have precisely 86,401 seconds.
National time-keeping centres around the globe, such as the National Research Council in Ottawa, will insert this extra second or leap second into their master clocks so that they remain synchronized with an international time standard. All other clocks that get their time from a master clock will be updated similarly. This includes all of the so-called time servers on the Internet, which keep our computer clocks in sync.
This global time standard is called UTC or Coordinated Universal Time. The standard was established in the 1960s once it was demonstrated that the newly developed atomic clocks could keep time with unprecedented precision and that clocks, even if they were on different continents, could be synchronized with each other to a fraction of a microsecond.
UTC is the time system kept in most countries straddling or bordering the prime meridian at zero degrees of longitude. The civil time systems in regions to the east and west of the prime meridian are typically offset by an integral number of hours from UTC. Atlantic Time, for example, is currently three hours behind UTC, so the leap second will occur here just before 9 p.m.
UTC (and the various zone or regional time scales related to it such as Atlantic Time) has replaced the previously used time scale based on the Earth’s rotation with respect to the sun for most civil time-keeping purposes.
Although the Earth appears to rotate uniformly with night following day since time immemorial, the Earth actually does not spin at a constant rate. It fluctuates slightly due to a variety of causes including variations in winds and ocean currents, the motions of the Earth’s fluid core, and the friction of tidal currents flowing along the bottom of the oceans.
Tidal friction and the other effects has resulted in a long-term or secular decrease in the Earth’s rate of rotation resulting in an increase in the length of the solar day of a little over 1 millisecond per day per century. Currently, the length of the day is roughly 2 milliseconds longer than it was in the early 1800s when it was exactly 86,400 seconds. This means that over a period of 1,000 days, a clock keeping time based on the rotation of the Earth, a time scale known as UT1, would lose about 2 seconds compared to UTC, which is based on the atomic second and referenced to the period of the Earth’s rotation around 1820.
To keep UTC to within 0.9 second of UT1, leap seconds are periodically added to UTC. While tidal friction is the primary reason for adding these leap seconds, the other factors responsible for the variation in the Earth’s spin contribute as well. In fact, negative leap seconds are theoretically possible, although all leap seconds to date have been positive.
The last leap second occurred on June 30, 2012. There have been 25 leap seconds added to UTC since the current system began in 1972. Leap seconds are applied either on December 31st or June 30. Two thirds of them have occurred on New Years Eves with the rest taking place at the end of June like the one coming up.
The world runs on UTC. Everything from financial transactions to air traffic control depends on UTC and so these systems will have to properly accommodate the leap second when it happens. This includes satellite navigation systems. The Global Positioning System itself is unaffected by the introduction of a leap second because it has its own time system, GPS (System) Time, which is not adjusted for leap seconds. GPS Time was set equal to UTC back in 1980 and is currently 16 seconds ahead of it. On July 1st, this offset will increase to 17 seconds. GPS does provide UTC to its users by transmitting the necessary adjustment data in the satellite signals, permitting a receiver to compute UTC from GPS Time.
The upcoming leap second might be the last. The International Telecommunication Union is considering a proposal that leap seconds be abolished. The justification for the proposal is that leap seconds are cumbersome and their incorrect use could lead to problems with time-dependent infrastructure including safety-of-life navigation systems.
At an ITU meeting in Geneva in January 2012, national delegates debated a motion to eliminate the use of leap seconds in the UTC time scale. However, there was no agreement with countries evenly split in favour of, against, and undecided about abolishing leap seconds. Many of the undecided delegates said they were not sufficiently informed about the proposal to make a decision. The ITU will next consider the proposal in November 2015.
Editor’s Note: See the report from the European Space Agency here.
An official with the European Space Agency has confirmed that the sequence of maneuvers to adjust the orbit of the second of two Galileo satellites launched into a wrong orbit in August 2014 has been completed.
The orbit of the first satellite, known variously as GSAT0201, Galileo FOC-FM1 or Galileo 5 (with COSPAR ID 2014-050A and NORAD ID 40128) was raised during operations carried out in November, and the satellite began transmitting L-band signals on Nov. 29.
Maneuvering of the second satellite (GSAT0202, Galileo FOC-FM2 or Galileo 6, with COSPAR ID 2014-050B and NORAD ID 40129) began around Jan. 15. The procedure took somewhat longer than that for the first satellite as it also involved changing the mean anomaly of the satellite to be about 180° away from that of the first satellite.
The locations of the satellites in the Galileo constellation are shown in the accompanying figure. Satellites in green are transmitting a full complement of L-band signals. Galileo 4 (GSAT0104), one of the in-orbit validation satellites, suffered a power anomaly and only transmits on the E1 frequency. Galileo 5 is transmitting L-band signals but its orbit cannot be properly represented in the Galileo broadcast almanac. Galileo 6 has not started transmitting valid L-band signals yet.
Officially, all Galileo signals are currently declared unavailable during an extended period of testing following ground segment upgrades. However, signals continue to be monitored by stations participating in the International GNSS Service Multi-GNSS Experiment.
. While calculating the residuals, it involves the atmospheric effects ε j computed by the Klobuchar ionosphere delay model and a modified Hopfield tropospheric delay model.
