GPS World

Blog

  • Innovation: Record, Replay, Rewind

    Innovation: Record, Replay, Rewind

    Testing GNSS Receivers with Record and Playback Techniques

    By David A. Hall

    Is there a way to perform repeatable tests on GNSS receivers using real signals? This month’s column looks at how to use an RF vector signal analyzer to digitize and record live signals, and then play them back to a GNSS receiver with an RF vector signal generator.

    INNOVATION INSIGHTS by Richard Langley
    INNOVATION INSIGHTS by Richard Langley

    AS A PROFESSOR, I’m quite familiar with testing — of students, that is. It’s how we check their performance — how well they have mastered the course material. Outside academia, testing is also quite common. We have to pass a driving test before we can get a license. We might have to pass a physical fitness test before starting a job. And manufacturers have to test or stress their products to make sure they are fit for purpose. As David Ogilvy, the father of advertising once quipped, “Never stop testing, and your advertising will never stop improving.” But it’s not just manufacturers who should test products. Consumers, or their representatives, should test products on offer — not only to corroborate (or dispute) manufacturers’ claims but also to compare one manufacturer’s product against another. There’s a whole slew of magazines, television programs, and web resources devoted to testing and comparing everything from laundry detergent to automobiles. And GNSS receivers are no exception.

    When we conduct tests, we are usually trying to get answers to certain questions — just like those posed to students on their exams. In testing GNSS receivers, what are some appropriate questions? When a receiver is turned on, how long does it take until the position of the receiver is determined? When a weak signal area is encountered, can the receiver still determine its position? If the signal is interrupted and then restored, how long does it take for the receiver to recover and resume calculating its position? And what is the position accuracy under different situations?

    While we can certainly hook up an antenna to a receiver to get answers to these questions in a certain environment on a certain day at a certain time with certain signals, the scenario cannot be repeated — not exactly. If we tweak a receiver operating parameter, for example, we don’t know for certain whether any observed change is due to the tweaking or a change in the scenario. We could use a radio-frequency (RF) simulator — a device for mimicking the radio signals generated by the satellites. This would allow us to define scenarios, including receiver trajectories, and to replay them as many times as necessary while varying the operating parameters of the receiver. Or we could modify the scenario from run to run. Such test scenarios could include those difficult to carry out with live signals such as determining how a receiver would perform in low Earth orbit. While extremely useful, these are tests with simulated signals.

    Is there a way to perform repeatable tests on GNSS receivers using real signals? In this month’s column, we learn how to use an RF vector signal analyzer to digitize and record live signals, and then play them back to a GNSS receiver with an RF vector signal generator — a procedure we can repeat as often as we like.

    While GNSS simulators have long provided the de facto technique for testing GPS receivers, radio frequency (RF) record and playback has emerged as an innovative method to introduce real-world impairments to GNSS receivers. In this article, we will provide a hands-on tutorial on how to test a navigation device using the record and playback technique.

    The premise of RF record and playback is to capture GNSS signals off the air with a vector signal analyzer (VSA) and then replay them to a receiver with an RF vector signal generator (VSG). With recorded GNSS signals, one is able to introduce a signal that contains natural impairments — instead of an ideal signal — to the GNSS receiver. As a result, one can observe how a receiver will behave in a real-world environment where interference, multipath fading, and other impairments are present.

    A VSA combines traditional superheterodyne radio receiver technology with high-speed analog-to-digital converters and digital signal processors to perform a variety of measurements on complex modulated signals. It is widely used in the telecommunications industry as a test instrument. Digitized signals can be recorded for future analysis. A VSG reverses the process, taking a digital representation of a complex waveform and, using digital-to-analog converters, generating an appropriately modulated RF signal.

    Recording GPS or GLONASS signals off the air can be done in a fairly straightforward manner. An RF recording system combines appropriate antennas, amplifiers, and an RF signal recorder (usually a VSA) to capture many hours of continuous RF signal. In such a system, the basic components include the RF front end, the RF signal-acquisition device, and high-volume storage media. A block diagram of a typical recording system is shown in Figure 1.

    Figure 1. GPS receivers implement cascaded low-noise amplifiers. The RF signal acquisition block includes analog-to- digital conversion (ADC) and digital down conversion (DDC) to select the data of interest.
    Figure 1. GPS receivers implement cascaded low-noise amplifiers. The RF signal acquisition block includes analog-to- digital conversion (ADC) and digital down conversion (DDC) to select the data of interest.

    In the figure, the RF front end is designed to condition the GNSS signal in such a way that it can be captured — with maximum dynamic range — by the recording device. The recording device digitizes a given signal bandwidth, and then stores in-phase and quadrature (IQ) waveforms to disk.

    In general, RF recording devices are designed to tune to a broad range of frequencies and can thereby record many different types of signals. Thus, selecting the signal to record is as simple as setting the center frequency and bandwidth of the recording device. For example, to record the GPS C/A-code L1 signal, the center frequency should be set to 1575.42 MHz. Because each satellite generates the same carrier frequency, one can capture C/A-code signals from all satellites simply by capturing all signals within a 2.046 MHz (twice the code chipping rate) band around the carrier frequency.

    By contrast, recording GLONASS signals requires slightly different settings. Because the GLONASS constellation uses frequency division multiplexing, every satellite generates the same code, but each pair of antipodal satellites transmits at a unique center frequency. Thus, recording L1 signal information for the entire GLONASS constellation requires a recorder to capture signals that range from 1598.0625 MHz (channel -7) to 1605.375 MHz (channel 6). In order to capture the entire bandwidth of each satellite, a recorder is actually required to capture 1.022 MHz of signal for each carrier (again, twice the code chipping rate). Therefore, the total recording bandwidth is actually 1597.5515 MHz to 1605.886 MHz, a span of 10.3345 MHz. On the RF signal analyzer, one can record GLONASS signals simply by setting the center frequency to 1601.71875 MHz, and the bandwidth to ≥ 10.3345 MHz.

    Modern RF signal recorders are capable of recording both GPS and GLONASS C/A-code signals on a single wideband recording channel. For example, one of our RF signal analyzers is capable of recording up to 50 MHz of signal bandwidth. With this instrument, one can simultaneously record both GPS and GLONASS by setting the center frequency to 1590.1415 MHz and the bandwidth to ≥ 31.489 MHz. However, while RF recording systems can be used to capture a wide range of GNSS signals including GPS L1/L2/L5, GLONASS L1/L2, Galileo, and others, this article focuses primarily on the GPS C/A-code signal.

    Setting up the RF Front End

    The trickiest aspect of recording GPS signals is the selection and configuration of the appropriate antenna and low noise amplifier (LNA). When connecting a typical off-the-shelf GPS passive patch antenna to a signal analyzer, the peak power in the GPS L1 band ranges from -120 to -110 dBm. Because the power level of GPS signals is small, significant amplification is required to ensure that the VSA can capture the full dynamic range of the signal.

    The simplest method to amplify an off-the-air GPS signal so that it can be captured by an RF signal recorder is the combination of an active GPS antenna and one or more external LNAs. Note that many professional GPS antennas offer the best performance because they combine high element gain with an LNA and even pre-selection filtering, which improves the dynamic range of the RF recorder.

    With the RF front end appropriately configured, one can verify system performance using a simple spectrum analyzer demonstration panel. The demo panel allows one to visualize the RF spectrum in the GPS L1 band. If all is set up correctly, the C/A-code GPS signal should be visually present on the display. Figure 2 illustrates a screenshot of the spectrum on a virtual spectrum analyzer display.

    Note that visualizing the GPS signal in the frequency domain with an RF signal recorder (or spectrum analyzer) requires careful attention to settings such as resolution bandwidth and averaging. Because the signal-to-noise ratio (SNR) of the GPS signal is so small, the settings shown in Figure 2 require a narrow resolution bandwidth (10 Hz) and significant averaging (20 averages per measurement record, so a 20-second interval for 1 Hz data). With these settings applied, one can easily visualize a modulated signal above the noise floor with approximately 1 MHz of bandwidth and centered at 1575.42 MHz. This signal is the GPS C/A-code. In Figure 2, the reference level of the signal analyzer was set to -50 dBm to reduce the noise floor of the instrument to the lowest possible level. Note that setting the signal analyzer’s reference level provides a simple mechanism to adjust the front-end attenuation or amplification. In general, RF signal analyzers provide the greatest dynamic range when the reference level of the instrument matches closely with the average power of the signal connected to the front end. In this case, setting the reference level of our signal analyzer to -50 dBm removes all front-end attenuation, giving the analyzer a more optimal noise figure for signal recording.

    Figure 2. GPS is visible in the spectrum only if a narrow resolution bandwidth is used. This spectrum was obtained with a center frequency of 1575.42 MHz, a frequency span of 4 MHz, a resolution bandwidth of 10 Hz, root-mean-square averaging with 20 averages, and a reference level of 250 dBm.
    Figure 2. GPS is visible in the spectrum only if a narrow resolution bandwidth is used. This spectrum was obtained with a center frequency of 1575.42 MHz, a frequency span of 4 MHz, a resolution bandwidth of 10 Hz, root-mean-square averaging with 20 averages, and a reference level of 250 dBm.

    Hardware Connections

    With the reference level appropriately set, it is important to properly configure the RF front end of the recording device. As previously mentioned, one can achieve the best RF recording results by using an active GPS antenna. The active antenna used in our experiment utilized a built-in LNA to provide up to 30 dB of gain with a 1.5 dB noise figure. (Recall that the noise figure is the difference in dB between the noise output of a device and the noise output of an “ideal” device with the same gain and bandwidth when it is connected to sources at the standard noise temperature — usually 290 K.) However, the LNA must be powered by supplying a DC bias to the RF connection. While there are several methods to supply the DC bias, we will look at two of the easiest methods.

    Method 1: Active Antenna Powered by GPS Receiver. The first method to power an active antenna is with a bias tee or DC power injector. Using this three-port component, a DC voltage (3.3 V in this case) is fed to its DC port, which applies the appropriate DC offset to the active antenna connected to the RF-in port while blocking it on the RF-out port. The device gets its name from the fact that the three ports are often arranged in the shape of a “T.” Note that the precise DC voltage one should apply depends on the DC power requirements of the active antenna. A diagram illustrating the connections is shown in Figure 3.

    Observe in Figure 3 that one can use off-the-shelf hardware such as a programmable DC power supply to supply the DC bias signal. Also, one can use a generic off-the-shelf bias tee as long as it has bandwidth up to 1.58 GHz.

    Figure 3. This set-up shows the use of a DC bias tee to power an active GPS antenna.
    Figure 3. This set-up shows the use of a DC bias tee to power an active GPS antenna.

    Method 2: Active GPS Antenna Powered by Receiver. A second method of powering the active GPS antenna is with the receiver itself. Most off-the-shelf GPS receivers use a single port to power and receive signals from an active GPS antenna, and this port is already biased with an appropriate DC voltage. Combining an active GPS receiver, a power splitter, and a DC blocker, one can power an active LNA and simply record essentially the same signal as that observed by the GPS receiver. A diagram of the appropriate connections is shown in Figure 4. Some splitters incorporate a DC block on all but one of the output ports.

    As Figure 4 illustrates, the DC bias from the GPS receiver is used to power the LNA. This method is particularly useful for drive tests because one can observe the receiver’s characteristics, such as velocity and dilution of precision, while recording.

    Figure 4. With a DC blocker, one can record and analyze the same GPS signals being tracked by a GPS receiver.
    Figure 4. With a DC blocker, one can record and analyze the same GPS signals being tracked by a GPS receiver.

    Selecting the Right LNA

    Recording GPS signals with generic RF signal recorders is possible largely because external LNAs can be used to reduce the effective noise floor of the receiver. Today, one can find off-the-shelf spectrum analyzers with noise figures ranging from 15 dB to 20 dB. One of our analyzers, for example, has a 15 dB noise figure while applying up to 60 dB of gain. By applying external amplification to the front of an RF signal analyzer, however, one can substantially reduce the noise figure of the RF recording system.

    To calculate the total noise that will be added to the recorded GPS signal, one must calculate the noise figure for the entire RF front end. As a matter of principle, the noise figure of the entire system is always dominated by the first amplifier in the system. Thus, careful selection of the first and second stage LNAs is crucial for a successful signal recording.

    We can calculate the noise figure of the RF recording system by using the Friis formula for noise figure, named for engineer Harald Friis, a Danish-American radio engineer who worked at Bell Telephone Laboratories. To use this formula, first convert the gain and noise figure of each component to its linear equivalent; the latter is called the “noise factor.” For cascaded systems such as our RF recording system, the Friis formula provides us with the noise factor of the entire system:

    In-E1        (1)

    Note that both noise factor (nf) and gain (g) are shown in lowercase to distinguish them as linear measures rather than logarithmic measures. The conversion from linear to logarithmic gain and noise figure (and vice v
    ersa) is shown in the following equations:

    IN-E2-5

    An active GPS antenna using a built-in LNA typically provides 30 dB of gain while introducing a noise figure that is typically on the order of 1.5 dB. The second part of the recording instrumentation provides 30 dB of additional gain as well. Though its noise figure is higher (5 dB), the second amplifier actually introduces very little noise into the system. As an academic exercise, one can use the Friis formula to calculate the noise factor for the entire RF front end of the recording instrumentation. Gain and noise figure values are shown in Table 1.

    Table 1. Noise figures and factors of the first two components of the RF front end.
    Table 1. Noise figures and factors of the first two components of the RF front end.

    According to the calculations above, one can determine the overall noise factor for the receiver:

    IN-E6  (6)

    To convert noise factor into a noise figure (in dB), apply Equation 2, which yields the following results:

    IN-E7     (7)

    As Equation 7 illustrates, the noise figure of the first LNA (1.5 dB) dominates the noise figure of the entire RF recording system. Thus, with the VSA configured such that the noise floor of the instrument is less than that of the input stimulus, one’s recording introduces only 1.507 dB of noise to the off-the-air signal.

    Saving Data to Disk

    Each GNSS produces slightly varying requirements for an RF recorder’s signal bandwidth and center frequency. For the GPS C/A-codes, the essential requirement is to record 2.046 MHz of RF bandwidth at a center frequency of 1575.42 MHz.

    In the tests described here, we set the IQ sample rate of our RF recorder at 5 megasamples per second (Ms/s). Since each 16-bit I and Q sample is 32 bits (or 4 bytes each), the actual recording data rate is 20 megabytes per second (MB/s) to ensure the entire bandwidth was captured. Capturing more than 4 MHz of bandwidth is sufficient to record the 2.046 MHz C/A-code signals.

    Because one can achieve data rates of 20 MB/s or more with standard PXI controller hard drives (PXI is the open, PC-based platform for test, measurement, and control), one does not need to use an external redundant array of independent disks (RAID) volume to stream GPS signals to disk when using a PXI recording system. In general, data rates exceeding 20 MB/s require the use of an external RAID volume. External RAID systems are capable of storing more than 600 MB/s of data and can be used to support wide bandwidth channels or even multi-channel recording applications. For example, the recording system shown in Figure 5 uses an external RAID volume for high-speed signal recording. This system combines PXI RF signal generators and analyzers with external amplifiers and filter banks for a ready-to-use GNSS record and playback solution.

    Figure 5. Two-channel record and playback system from Averna.
    Figure 5. Two-channel record and playback system from Averna.

    In our tests, we decided to use a 320 GB USB drive for better portability. With a disk speed of 5400 revolutions per minute, we were able to benchmark it ahead of time and observed that we were able to achieve read and write speeds exceeding 25 MB/s. Thus, we were easily able to use this disk drive and still record IQ samples at 5 MS/s (20 MB/s) when recording off-the-air signals. With the existing hard-drive setup, we could record more than 4 hours of continuous IQ signal. Note that capturing longer recordings simply requires a larger hard disk. By using a 2 terabyte RAID volume (the largest addressable disk size in the Windows XP operating system), we can extend our recording time to 25 hours. With this setup, we could also reduce the IQ sample rate to 2.5 MS/s (still sufficient to capture the GPS C/A-code signals) and extend the recording time to 50 hours.

    Receiver Performance

    Once the off-the-air signal of a GNSS band is recorded to disk, it can be re-generated and fed to a receiver using an RF signal generator. With an RF signal generator that is able to reproduce the real-world GNSS signal, engineers are able to test a wide range of receiver characteristics. Because recorded signals contain a rich set of channel impairments such as ionosphere distortion and interference from other transmitters, design engineers often use recorded signals to prototype the baseband processing algorithms on a GNSS receiver.

    In our case, we used a VSG directly connected to a GPS evaluation board. In the experiments described below, the receiver’s latitude, longitude, and velocity were tracked over time. Data was read from the receiver using a serial port, which read NMEA 0183 sentences at a rate of one per second. NMEA 0183 is a standard protocol developed by the National Marine Electronics Association for communications between marine electronic devices. NMEA 0183 has been adopted by virtually all GPS receiver manufacturers. In our LabVIEW graphical development environment, one can parse all sentences to return satellite and position-fix information.

    For practical testing purposes, GPS dilution of precision and active satellites (GSA), GPS satellites in view (GSV), course over ground and ground speed (VTG), and GPS fix data (GGA) sentences are the most useful. More specifically, one can use information from the GSA sentence to determine whether the receiver has achieved a position fix and is used in time-to-first-fix measurements. When performing sensitivity measurements in this example, the GSV sentence was used to return carrier-to-noise-density ratios (C/N0) for each satellite being tracked. In addition, the VTG sentence allows us to observe the velocity of the receiver. Finally, the GGA sentence provides the receiver’s precise position by returning latitude and longitude information. See the references in Further Reading for in-depth information on the NMEA 0183 protocol.

    Using the receiver’s reported latitude and longitude information, we are able to test its ability to report a repeatable position when the recorded signal is played back to the receiver. In this experiment, we tracked the receiver position over 10 minutes. For the best results, the command interface of the receiver should be tightly synchronized with the start trigger of the RF signal generator. The results in Figure 6 show that the RF vector signal generator in this experiment was synchronized with the GPS receiver by using the data line of the serial communications (COM) port (RxD, pin 2) as a start trigger. Using this synchronization method, the vector signal generator and GPS receiver were synchronized to within one clock cycle of the VSG’s arbitrary waveform generator (100 MS/s). Thus, the maximum skew should be limited to 10 microseconds. Given our receiver’s maximum velocity of 15 meters per second (our maximum speed on the drive test), we can determine that the maximum error induced by clock offset of the signal generator is 10 microseconds x 15 meters per second, or 0.15 millimeters.

    Using the configuration described above, one is able to report the receiver’s latitude and longitude over time, as shown in Figure 6.

    IN-Fig6a
    Figure 6A. Receiver latitude over a four-minute span.
    Figure 6B. Receiver longitude over a four-minute span.
    Figure 6B. Receiver longitude over a four-minute span.

    As the data from Figure 6 illustrate, a recorded test-drive signal reports static, position, and velocity information. In addition, one can observe that this information is relatively repeatable from one trial to the next, as evidenced by the difficulty in graphically observing each individual trace. To better characterize the deviation between each trace, one can also compute the standard deviation between each sample in the waveforms. Figure 7 illustrates the standard deviation between each of the 10 trials, calculated for every one-second interval, versus time.

    FIGURE 7 Standard deviation of both latitude and longitude over time
    Figure 7. Standard deviation of both latitude and longitude over time.

    When observing the horizontal standard deviation, it is interesting to note that the standard deviation appears to rapidly increase at time = 120 seconds. To investigate this phenomenon further, we can plot the total horizontal standard deviation against the receiver’s velocity and a proxy for C/N0. In this case, we simply averaged the C/N0 values for the four highest satellites reported by the receiver. Since four satellites are required to achieve a three-dimensional position fix, our assumption was that position accuracy would closely correlate with the signal strength of these important satellite signals.

    One simple method to evaluate the horizontal repeatability of the receiver position versus time is to calculate the standard deviation on a per-sample basis of each recorded latitude and longitude (in degrees). Once the standard deviation is measured in degrees, we can roughly convert this to meters with the following equation:

    IN-E8

    Note that Equation 8 represents a highly simplified error calculation method, which assumes that the Earth is a perfect sphere. For a more precise calculation of repeatability, the geodesic formula (which presumes that the Earth is ellipsoidal) should be used. In our simple experiment, the goal is merely to correlate repeatability with other factors that we can measure from the receiver. Figure 8 illustrates the standard deviation of horizontal position repeatability over 10 trials and at one-second time intervals.

    Figure 8. Correlation of position accuracy and C/N0.
    Figure 8. Correlation of position accuracy and C/N0.

    As one can observe in Figure 8, the peak horizontal error (measured by standard deviation) occurring at time = 120 seconds is directly correlated with satellite C/N0 and not correlated with receiver velocity. At this sample, the standard deviation is nearly 2 meters while it is less than 1 meter during most other times. Concurrently, the top four C/N0 averages drop from nearly 45 dB-Hz to 41 dB-Hz.

    The exercise above illustrates not only the effect of C/N0 on position accuracy but also the types of analysis that one can conduct using recorded GPS data. For this experiment, the drive recording of the GPS signal was conducted in Huizhou, China (a city north of Shenzhen), but the actual receiver was tested at a later date in Austin, Texas.

    Conclusion

    In this article, we’ve illustrated how to use commercially available off-the-shelf products to record GPS signals with an RF recorder, and then play the signal back to a receiver. As the results illustrate, recorded GPS signals can be used to measure a wide range of receiver characteristics. Not only can receiver designers use these test techniques to better prototype a receiver baseband processor, but also to measure system-level performance such as position repeatability.

    Manufacturers

    The tests discussed in this article used a National Instruments PXIe-5663E, 6.6 GHz, RF signal analyzer; a National Instruments PXI-5690, 100 kHz to 3 GHz, two-channel programmable amplifier and attenuator; a National Instruments PXIe-5672, 2.7 GHz, RF vector signal generator with quadrature digital upconversion; a 320 GB USB Passport hard drive from Western Digital Corp.; a National Instruments PXI-4110 programmable, triple-output, precision DC power supply; and a ZX85-12G-S+ bias tee manufactured by Mini-Circuits. The article also mentioned the RP-3200 2-channel record and playback system manufactured by Averna, which incorporates National Instruments modules.

    David Hall is an RF product manager for National Instruments. He holds a bachelor’s of science with honors in computer engineering from Pennsylvania State University.

    FURTHER READING

    More on GNSS Receiver Record and Playback Testing

    GPS Receiver Testing, tutorial published by National Instruments, Austin, Texas.

    Friis Formula and Receiver Performance

    RF System Design of Transceivers for Wireless Communications by Q. Gu, published by Springer, New York, 2005.

    Global Positioning System: Signals, Measurements, and Performance, 2nd edition, by P. Misra and P. Enge, published by Ganga-Jamuna Press, Lincoln, Massachusetts, 2006.

    “Measuring GPS Receiver Performance: A New Approach” by S. Gourevitch in GPS World, Vol. 7, No. 10, October 1997, pp. 56-62.

    “GPS Receiver System Noise” by R.B. Langley in GPS World, Vol. 8, No. 6, June 1997, pp. 40–45.

    Global Positioning System: Theory and Applications, Vol. I, edited by B.W. Parkinson and J.J. Spliker Jr., published by the American Institute of Aeronautics and Astronautics, Inc., Washington, D.C., 1996.

    GNSS Receiver Testing Using Simulators

    “Testing Multi-GNSS Equipment: Systems, Simulators, and the Production Pyramid” by I. Petrovski, B. Townsend, and T. Ebinuma in Inside GNSS, Vol. 5, No. 5, July/August 2010, pp. 52–61.

    “GPS Simulation” by M.B. May in GPS World, Vol. 5, No. 10, October 1994, pp. 51–56.

    GNSS Receiver Testing Using Software

    “GPS MATLAB Toolbox Review” by A.K. Tetewsky and A. Soltz in GPS World, Vol. 9, No. 10, October 1998, pp. 50–56.

    GNSS L1 Signal Descriptions

    Navstar GPS Space Segment / Navigation User Interfaces, Interface Specification, IS-GPS-200 Revision E, prepared by Science Applications International Corporation, El Segundo, California, for Global Positioning System Wing, June 2010.

    Global Navigation Satellite System GLONASS, Interface Control Document, Navigational Radio Signal in Bands L1, L2 (Edition 5.1), prepared by Russian Institute of Space Device Engineering, Moscow, 2008.

    NMEA 0183

    NMEA 0183, The Standard for Interfacing Marine Electronic Devices, Ver. 4.00, published by the National Marine Electronics Association, Severna Park, Maryland, November 2008.

    “NMEA 0183: A GPS Receiver Interface Standard” by R.B. Langley in GPS World, Vol. 6, No. 7, July 1995, pp. 54–57.

    Unofficial online NMEA 0183 descriptions: NMEA data; NMEA Revealed by E.S. Raymond, Ver. 2.3, March 2010.

  • Letter to the Editor: History Articles Set Record Straight

    I was relieved to see that the facts related to the conception of GPS were clearly laid out in the two-part article “GPS Heroes” (May and June issues). During the past few years, erroneous information about the early years of GPS development has circulated in some military, engineering, and scientific circles.These stories centered on some version of the idea that GPS’ design originated with the Naval Research Laboratory (NRL) and within the patent submitted for Timation by the NRL’s Roger Easton; the U.S. Air Force and The Aerospace Corporation were conspicuously missing from the various scenarios that credited Roger Easton with “inventing” GPS.

    I have had the privilege to record and publish oral history interviews with several GPS pioneers, including Drs. Getting and Parkinson and Ed Lassiter. I also had 
opportunities to speak to many more early GPS participants off the record, including retired Air Force personnel and several non-Aerospace employees, when conducting background research for an article 
dealing with the beginnings and 
subsequent implementation of GPS.

    My research included a review of many of the primary documents relating to GPS’ origins, including the Woodford/Nakamura study completed for 621B in 1966, and several subsequent studies. I can state emphatically that during the course of my research, I never encountered any evidence indicating that NRL’s/Easton’s Timation system was the progenitor of GPS. In fact, as the authors point out, Timation was considered and rejected by 621B personnel when planning the original system.

    Not a single person I spoke to has ever provided me with any version of GPS’ genealogy other than the one related by Parkinson and Powers. The majority of the interviewees, on or off the record, gave NRL and Mr. Easton ample praise for their significant contribution to satellite navigation through the development of the Timation system; no one even remotely carried this acknowledgement and 
appreciation of Timation as an antecedent to GPS any further, historically speaking. After discussing Timation with several 
interview subjects familiar with the 
system, it became clear there was a general consensus that Timation simply did not have the necessary capabilities to meet the requirements for the GPS design that was ultimately selected.

    With the publication of Parkinson’s and Powers’ article, GPS World has provided an excellent public forum for the presentation of the facts, not the folklore, regarding the historical origins of GPS, clearly and in detail for the GPS community.

    Steven Strom
    El Segundo, California

  • Out in Front: Welcome to Accuracy Anonymous

    The following was delivered as an invited presentation at the Civil GPS Service Interface Committee plenary session, held September 20 in Portland, Oregon.

    Hi, my name is Alan, and I’m an accuracy addict.

    I got my first taste of accuracy back in 2000 when I started at GPS World, and discovered the vast range of very advanced things that people were doing with the signals of the Global Positioning System.

    This filled me with a great feeling of elation, expansiveness, and effectiveness. I can position anything. I can track anything. I can go anywhere, and know where I am. I can direct something else to go somewhere, and have it hit exactly on target. I can examine the minute movements of the earth, the swaying of skyscrapers, the moisture content of the atmosphere, and I can know all.

    I began to feel the illusion of omnipotence — of power over all.

    The more I found out about accuracy, the more I used it, the more addicted I became.

    Very early, I learned that advanced practitioners, such as some of the people in this room, had developed ways of taking two GPS signals, not just one, but two signals, including one that they weren’t even entitled to use, and combining them, distilling them, refining them to produce an even more potent product: high precision.

    High. I was getting pretty high. Almost as high as some of you.

    Because we’re all in this together. In this room, we are all addicts. And when our supply of accuracy gets cut off, or restricted, or we learn that it might soon be diminished in some way, or even that its projected future rate of increase might not be as rapid as expected, or that it might not increase at all, it might just simply stay the same­ — well then, we get upset.

    We want to get high precision, we want to stay high precision, and we want to get higher precision.

    We may have a problem with our accuracy habit.

    It’s not just us, the highly educated, highly equipped, highly advanced users, with near-lifelong histories of accuracy use. Outside this room, outside this convention center and all who gather here this week, outside our offices and labs, the great unwashed masses are getting their first taste of low-grade accuracy. With their cell phones or smart phones, maybe 50-meter, maybe 15-meter, maybe even 5-meter accuracy.

    They’re liking it, that first taste. Once they learn how to exploit it, and learn that higher accuracy is possible, they’re going to demand it.

    And some enterprising young engineers are going to build a high-powered LBS app that needs high accuracy, just like other new apps need broadband or WiFi or 3G or 4G. If the capability exists, someone wants to make money off it.

    We may be raising a generation of monsters, who will absorb our habit into their bloodstreams and into their lifestyles.

    Things might get ugly. We know they’re going to change, altering the landscape in ways we may not recognize.

    I’m not talking about just the social landscape, the way accuracy users behave. Not just the user segment. I’m talking about the way accuracy is produced and administered. I’m talking about

    the supply of accuracy, the supply of a substance that is in high demand and to which an increasing number of people are becoming addicted.
    I’m talking about the ground control segment and the space segment.

    Ultimately, I’m talking about who makes the decisions, who funds the decisions, who enacts the decisions, and who enforces the decisions about how much accuracy can and will be produced.

    Today, we know, or think we know who those people are: the GPS Wing, the Air Force, the Department of Defense, the Administration of the U.S. government. We may think we know that those same people will be in charge tomorrow.

    I’m not so sure. Revolutions have happened before.

    I don’t mean to be U.S.-centric. The same developments are taking place, perhaps a bit lagged, in Europe and Russia and China. When the great mass of the Chinese market gets into using accuracy, gets the habit, you’re going to see some effects.

    Returning to the United States, simply because it has the most known and most established of these systems, it is not inconceivable that some Tea Party-like movement, a groundswell should roll right up to Washington, into Congress, and say:

    “Higher accuracy is possible. We are paying for GPS with our taxes, and we want you to spend that money producing and supplying us with a higher grade of accuracy. Don’t give us this talk of responsible stewards. We are calling the shots now. Just do it. Revise the ICD. Up the ante.
    “Give me accuracy or give me death.”

    Ladies and gentlemen, I have expanded, exaggerated only slightly, and perhaps exploded the old dictum that I’ve heard attributed to Charlie Trimble, I don’t know who first said it, but it bears repeating and repeating often: accuracy is addictive.

    Indeed it is. I’m here to tell you.

    I was asked to give you a user perspective. I’ve chosen what is today a relatively small user segment, but a very real one, and a growing one. And most important, one that augurs for the future.

    Perhaps the scenario I just imagined for you exaggerates a bit. Perhaps. I am consciously trying to push further out the boundaries of our thinking.

    We’ve been waiting, some of us, for a long time for the mass market to get involved in GPS. This is now happening, bit by bit. But it has not yet fully happened. When it does, great changes will come. When LBS figures out the key to making money out of location, you’ll see changes you can’t imagine today.

    I started to become aware of how pervasive and how strong accuracy addiction has grown when we experienced a succession of anomalies in the GPS constellation over the last year or so: SVN-49, the last IIR-M satellite; carrier-phase anomalies detected on SVN-48; and now SVN-62, a small variance in the L5 signal on the first IIF. “The signal variation results in no more than a 5-centimeter error with a predictable periodicity of about six hours.”

    In each case, GPS performed within spec, and some therefore viewed these issues as non-issues. “What seems to be lacking is context: what relevance their findings on unspecified and unrequired signal characteristics really have to do with the real-world GPS IIF mission and requirements.”

    I’ve repeated here two printed quotes in the magazine; offline, the point-counterpoint discussion grew a good deal more inflamed. Passions run high when the supply and quality of accuracy appears in question.

    This might seem a minor flare-up today, off in a corner of the field: specialized scientific research spatting with industry giants and their military-industrial complex benefactors.

    But today’s developing applications in aviation, ground transportation, structural monitoring, machine control, infrastructure, and more use techniques such as carrier phase that are not governed, are not even mentioned in the GPS ICD.

    When LBS gets figured out, and high-accuracy LBS and vehicle navigation and crash avoidance become regularly supplied commercial services, when the dependence of financial and communications infrastructure on high precision becomes fully understood and appreciated, then you’ll see some large corporate money that has become accuracy-addicted. Imagine this room in another few years, with GM, Ford, Google, Microsoft, AT&T, and Verizon attending and very interested, very much so, in aspects of user accuracy that are not currently addressed in the ICD.

    This community will change. Its needs will change. Balances of power and funding will shift. Are we prepared for that? Are we prepared to be surp
    rised? Or are we prepared only to be left behind by tides of change, to become obsolete?

  • Expert Advice: An EPIC Start for Coordination

    John Wilde
    John Wilde

    By John Wilde

    The new European Positioning, Navigation, and Timing (PNT) Industry Council (EPIC) will be a forum for organizations with an interest in all PNT systems including Global Navigation Satellite Systems (GNSS). EPIC shall serve as an information and distribution portal between all stakeholders in the PNT community. Its mandate includes all GNSS constellations and related augmentation systems worldwide, both operational and in development/modernization.

    EPIC will undertake to serve the interests of all stakeholders within Europe, and on behalf of Europe on the global stage, recognizing that understanding and cooperation between the world’s stakeholders is key to the successful deployment of new and improved GNSS applications. We also envision that EPIC will become a thriving forum for the exchange of new ideas and best practices, as well as becoming a knowledge center hosting working groups and task forces focusing on specific GNSS issues. EPIC would thus not only serve as a gateway but actually assist stakeholders in developing common solutions to common problems in-house.

    Representation

    GNSS has applications in many commercial and non-commercial fields: academia, agriculture, airline operators, civil aviation authorities, air navigation service providers, emergency services, energy suppliers, logistics, manufacturing, maritime, communications, petrochemical, rail, surveyors, and more. Therefore, EPIC will work on behalf of all GNSS stakeholders regardless of their application or business model and represents the whole community, integral to the ongoing success of GNSS. In addition it will represent the needs of users and developers of downstream applications.

    International

    EPIC stands with sister organizations in North America and Asia:

    • United States GPS Industry Council
    • Japan GPS Council
    • Korean GNSS Technology Council

    EPIC will maintain close ties to these organizations and will profit from shared practices and knowledge when mutually beneficial. Joint representation with these organizations to government GNSS authorities will be a key coordination activity.

    Communication

    EPIC will encourage communication and cooperation among its membership to develop new associations and partnerships to create new applications or share ideas and expertise. It will organize regional meetings, workshops, focus groups and social gatherings.

    The organization will update members on the latest developments within GNSS and work to ensure that information is made available in a sensible, secure manner and shared as publicly as possible. We intend to keep EPIC a dynamic organization, reflecting the world of GNSS, responsive and adaptable to the needs of its members. Therefore, active involvement from the membership of EPIC will be crucial to its success in both setting the agenda and then realizing it. It is no accident that EPIC is intended as a forum — not just a place for debate but literally a marketplace of ideas where real transformative change can take place.

    To get the ball rolling, EPIC will conduct a market survey over the next few months with potential members to clarify their requirements and ensure that EPIC starts with the issues and people that matter.

    For further details, visit www.epicforum.org, or contact [email protected].

    John Wilde has great experience in the GNSS field, specializing most recently in aviation requirements. He is the founder of EPIC. See also his February 2008 interview on the same subject.

  • The System: GLONASS Forecast Bright and Plentiful

    At the Civil GPS Service Interface Committee meeting in Portland, Oregon, on September 20, Sergey Revnivykh, Deputy Director General of Roscosmos’s Central Research Institute of Machine Building, reported on the status and future of GLONASS.

    He provided a number of details on the present constellation and how it will be augmented in the future, stressing that GLONASS is doing well and that a full constellation of 24 primary satellites will be in operation within months. The average signal-in-space range error has improved by a factor of five in the past three years and presently stands at about 1.8 meters (one sigma).

    Figure 1. The GLONASS satellite generations through GLONASS-K2.
    Figure 1. The GLONASS satellite generations through GLONASS-K2.
     

    The present constellation consists of 20 healthy satellites with two reserve satellites, GLONASS 714 and 726. Revnivykh stated that GLONASS 726 had a failure of its navigation payload. It is known that the signal generator on the satellite is faulty and it had been set unhealthy since August 31, 2009. Nevertheless, it was placed in reserve status on March 19, 2010. GLONASS 714 is nominally healthy and could be brought back to service if needed. These initial reserve satellites are also being used to train the ground team to operate spare satellites in a full or nearly full constellation.

    GLONASS 727, in orbital slot 3, which was taken out of service on September 8, has also had a failure of its navigation payload and may not be returning to service. The three new satellites launched on September 2 are expected to enter service in early October. About 11 more GLONASS-M satellites will be launched by the end of 2012.

    Revnivykh announced that there will be two versions of the new GLONASS-K satellites: GLONASS-K1 and GLONASS-K2. GLONASS-K1 satellites will have a 10-year design life and a daily clock stability of 5 x 10-14.

    The first GLONASS-K1 satellite will be launched this December from the Plesetsk Cosmodrome about 800 kilometers north of Moscow. This will be the first launch of a GLONASS satellite from other than the Baikonur Cosmodrome. Only one more GLONASS-K1 satellite will be built and launched after that. The K1 satellites will test an open service CDMA signal on the GLONASS L3 frequency in the 1205 MHz band. Although the launch of the first GLONASS-K1 satellite will occur in December, the design process for the CDMA signal structure is not yet finished, according to a subsequent e-mail message from Dr. Revnivykh. When the process is completed, the structure will be made public.

    A completely new design, GLONASS-K2, will start launching in 2013. GLONASS-K2 satellites will have a 10-year design life and a daily clock stability of 1 3 10-14. Besides the CDMA signals on L3, CDMA signals will also be transmitted on L1 and L2. The GLONASS-K satellites will transmit the legacy FDMA satellites in addition to the CDMA signals.

    A modernized GLONASS-K satellite, GLONASS-KM, for launch after 2015, is now under study. In addition to transmitting legacy FDMA signals on L1 and L2 and CDMA signals on L1, L2, and L3, CDMA signals may also be transmitted on the GPS L5 frequency at 1176.45 MHz. Also being studied is an alternative to the present three-plane, equally spaced satellite constellation. A different constellation design would be possible using CDMA signals. Such a move would require that the legacy FDMA signals be switched off. Revnivykh stated that any such move would require at least 10 years’ advance notice.

    The signals that will be transmitted by the future generations of GLONASS satellites as well as those transmitted by the initial GLONASS satellites and the GLONASS-M satellites now on orbit are shown in Figure 2.

     
    Figure 2. Signals transmitted by the different generations of GLONASS satellites. OF 5 open-access FDMA, SF 5 special (military) FDMA, OC 5 open-access CDMA, OCM 5 open-access CDMA modernized.
    Figure 2. Signals transmitted by the different generations of GLONASS satellites. OF 5 open-access FDMA, SF 5 special (military) FDMA, OC 5 open-access CDMA, OCM 5 open-access CDMA modernized.

    Revnivykh also spoke on the satellite-based augmentation system under development, System for Differential Correction and Monitoring (SDCM). Correction and integrity data will be transmitted by Luch geostationary communication satellites now under development. Luch 5A, to be launched in 2011 and positioned at 16°W longitude, and Luch 5B, to be launched in 2012 and positioned at 95°E longitude, will transmit signals on an L1 frequency. Luch 4, to be launched in 2013 and positioned at 167°E longitude, will transmit on two frequencies. The three satellites will provide almost global coverage. The satellite payloads are under development.

    According to Revnivykh, the SDCM will make use of 12 monitor stations currently in operation in Russia and one in Antarctica at Russia’s Bellingshausen research station. However, the SDCM website indicates only 10 Russian stations currently in the test network. This anomaly might be explained by the fact that some locations have multiple monitor stations. Eight more monitor stations will be added in Russia and five more outside Russia. Revnivykh showed a map revealing the locations of the additional overseas stations as Cuba, Brazil, Vietnam, Australia, and an additional station in Antarctica. It is not intended, at least initially, that these stations would be used in generating the orbit and clock data broadcast by the GLONASS satellites themselves.

    Finally, Revnivykh stated that a GLONASS performance document will be released in the 2012–2013 time frame. His full presentation is available on the U.S. Coast Guard Navigation Center website (www.navcen.uscg.gov).
    Meanwhile, the three GLONASS-M satellites launched on September 2 have arrived at their designated orbital slots: GLONASS 736, plane 2, slot 9; 737, plane 2, slot 12; 738, plane 2, slot 16.

    The operating frequencies are not yet fully known. GLONASS 736, in physical slot 09, is currently undergoing experimental tests. It is included in the broadcast almanac at slot 16 and is transmitting on frequency channel -6. Stations in the International GNSS Service ground network are tracking the satellite. According to the Roscosmos Information-Analytical Centre, when the tests are completed, GLONASS 736 will transmit on channel -2 and be identified as slot 09 in the almanac. It is unclear if GLONASS 736 will replace GLONASS 722 also currently in slot 9, with the latter becoming a spare, or if GLONASS 736 will become the spare as previously inferred.

    GLONASS 737 and 738 have not started normal transmissions. Their assigned shared frequency channel is not yet known but -6 would be a likely candidate.

    Future GPS Control Segment Advances

    The Raytheon Company team developing the next-generation GPS Advanced Control Segment (OCX) successfully completed on schedule an integrated baseline review with the U.S. Air Force.

    When completed, GPS OCX will deliver a control segment designed to provide secure, accurate, and reliable navigation and timing information to military, commercial, and civil users. Raytheon is the prime contractor on the $886 million program. The team includes ITT, The Boeing Company, Infinity Systems Engineering, Braxton Technologies, and NASA’s Jet Propulsion Laboratory.

    Power Flex Positive

    From September 7 to 12, the U.S. Air Force Space Command (AFSPC) activated the long-awaited Flex Power demonstration for GPS, a power increase on L1 and L2. The trial of a new capability designed for military use under special circumstances was deemed a success, essentially going off without a hitch, according to Colonel David Buckman, AFSPC Command Lead for PNT, and Colonel Bernie Gruber, GPS Wing Commander.

    Officially, the flex power assessment ensured that the GPS control segment baseline (AEP 5.5) is properly integrated with the space segment with regard to command and control of High-Y Flex Power, a capability that increases the nominal transmit power of the desired signal by shifting power between signals (M-code and P(Y)) within a particular L-band. The net sum gain remains the same. High-Y Flex Power does not change total transmit power, does not affect phase stability between L1 and L2, is ICD-GPS-200E compliant, and does not affect the navigation message.

    Only a handful of 10-year-old reference receivers may have been adversely affected, possibly due to an outdated algorithm. Many government, commercial, and civil agencies were involved in the test, and hundreds of GPS receivers were closely monitored. As far as impacts to the overwhelming majority of global users, it was a non-event. The 2nd Space Operations Squadron (2SOPS) was able, over the course of five days, to make power changes to several GPS satellites without causing a phase shift and without the majority of users even knowing what was happening, although various announcements and press releases had appeared to alert them of the fact.

    All GPS satellites and signals have now returned to their normal power levels.

    Air Force Fends off GAO Zinger

    The U.S. Government Accountability Office has issued a follow-up to its alarming and much-criticized report, issued 16  months ago, on the health and prospects of the GPS constellation. Senior officers at the Air Force Space Command and Space and Missile Systems Center have characterized the new report as “overly pessimistic.”

    The report’s principal findings ­— that the Air Force continues to face challenges in launching its satellites as scheduled, which could affect the availability of the baseline GPS constellation, that on-orbit performance of IIF satellites remains uncertain, that a disconnect exists between GPS III and OCX, and that a predicted possible delay in GPS III could affect GPS constellation performance — are discussed and rebutted in detail by GPS World defense editor Don Jewell, with further commentary (paraphrased) by Air Force Space Command, in his October column.

    New Galileo ICD Embraced

    European Commission (EC) officials held a briefing during ION-GNSS in Portland for industry representatives, to discuss the new Galileo Open Service Signal-in-Space Interface Control Document (OS SIS ICD). Hosts Paul Verhoef and Michel Bosco said they were pleased with what they characterized as positive feedback from U.S., European, and Japanese industry representatives regarding collaboration and consultation over changes made in the ICD. The updated version is available.

    The EC grants free access to the technical information on the future Galileo open service signal: the specifications manufacturers and developers need to process data received from satellites. Anyone who wishes to use the intellectual property rights contained in the document simply needs to send an e-mail to [email protected] mentioning their request for a license agreement, which is without any exclusivity or geographic limitation.

    FAA Green-Lights ADS-B

    The U.S. Federal Aviation Administration (FAA) gave the go-ahead signal for full-scale, nationwide deployment of the satellite-based surveillance system called Automatic Dependent Surveillance – Broadcast (ADS-B) following its successful roll-out at four key sites. Air traffic controllers are now able to use the new technology to separate aircraft in areas with ADS-B coverage. Controller screens in those areas will show aircraft tracked by radar as well as aircraft equipped with ADS-B avionics, which broadcast their positions.

    The new system tracks aircraft with greater accuracy, integrity, and reliability than the current radar-based system, the FAA said. ADS-B targets on controller screens update more frequently than radar and display information including aircraft type, call sign, heading, altitude, and speed.

    Nationwide ADS-B coverage is scheduled to be complete in 2013. According to the FAA, every part of the country now covered by radar will have ADS-B coverage. More than 300 of the approximate 800 ADS-B ground stations that will comprise the entire network have been installed.

    By 2020, aircraft flying in controlled airspace in the U.S. must be equipped with ADS-B avionics that broadcast their position.

  • Sparse Network: Wide-Area, Sub-Decimeter Positioning for Airborne LiDAR Surveys

    The use of a precise wide-area positioning technique for airborne trajectory solutions for LiDAR surveys provides both relative and absolute accuracies similar to those derived from using a local GNSS reference station.

    Airborne light detection and ranging (LiDAR) surveys are among the most advanced means of producing high-resolution, accurate surface elevation models used for many applications in surveying and civil engineering. Precise geolocation and orientation (or georeferencing) of the LiDAR instrument with a combination of on-board GNSS and inertial sensors at the times when the measurements are made provides the key to high-quality elevation products.

    The usual practice deploys reference GPS/GNSS land receivers in the area where the aircraft will be flying, to obtain a precise trajectory by short-baseline differential GNSS techniques. This could mean installing and operating receivers at many sites during a flight mission if the area surveyed is a large one.

    We have tried a different approach: using as reference receivers those of a sparse network of Continuously Operating Reference Stations (CORS) in New South Wales known as CORSnet-NSW, and a wide-area differential GPS technique for obtaining the aircraft trajectory with sub-decimeter accuracy even with baseline lengths of several hundred kilometers. This may be comparable in precision and accuracy to the short-baseline method, but without the cost and logistical complications. This opens up a new level of operational capability, allowing flexibility for weather conditions and priority response applications.

    The tests described here were organized and conducted by the NSW government’s Land and Property Management Authority, in collaboration with the University of New South Wales, in June 2009. CORSnet-NSW consists, at this writing, of 46 stations and by 2012 will provide statewide GNSS positioning infrastructure across NSW with a planned 70 stations in operation.

    Precise Wide-Area Positioning

    We used a technique for long-baseline differential, off-line positioning, able to deliver centimeter precision for fixed receivers and sub-decimeter precision for moving receivers. This choice was dictated by three considerations:

    • The intended application was the geolocation of the data of an airborne scanning LiDAR sensor to be used in the generation of high-accuracy digital elevation models (DEM).
    • Off-line processing, where all the GNSS data collected during the flight are available for processing and (as in this case) there is no need for immediate results, is intrinsically more reliable than real-time processing, where the data are available only up to the present epoch, and accurate results must be obtained right away, with no chance for a second try.
    • Differential processing makes it possible to resolve the carrier-phase ambiguities using well-understood methods.

    Technique. It is common practice in airborne LiDAR surveys to use GNSS both to position the instrument precisely, and to assist an inertial navigation system (INS) to obtain the orientation of the aircraft in space, as both position and orientation are needed to interpret the data properly. FIGURE 1 illustrates the relationship between the sensors used for airborne LiDAR surveys. The aircraft uses a GNSS antenna combined with an INS to georeference its trajectory. The bore-sight calibration process aligns the individual sensor orientations and standardizes the range measurements. However, if the survey is to achieve the now-expected high level of vertical accuracy (615 centimeters, 1 sigma), then the position of the GNSS/INS-derived aircraft trajectory for each laser swath must be determined with a relative precision in the order of just a few centimeters. This is achieved via differential GNSS post-processing of the kinematic airborne data together with static observations collected on precisely surveyed ground reference stations. The GNSS positions are then blended with high-frequency measurements taken by the onboard INS to produce the final trajectory and reference orientations.

    Colombo-1
    Figure 1. Airborne LiDAR reference frame.

    To such ends, the aircraft trajectory is usually determined by short-baseline differential GNSS, with ground receivers deployed near the intended flight path of the aircraft. In this way it is possible to use GNSS data analysis techniques that are both precise and quite straightforward to implement in software. The simplicity of these techniques is possible because, in short-baseline differential solutions, the data of the aircraft receiver and any nearby network receivers have much the same systematic errors (due to such things as satellite ephemerides errors, transmission delays, and so on) that cancel out — or nearly so — when their observations are differenced between them. This also makes it possible to resolve quickly and reliably the cycle ambiguities in the observed carrier phase, the most precise type of GNSS data, overcoming one of the main obstacles to obtaining good results. Furthermore, it is possible to get such results with single-frequency receivers, as ionospheric delay is one of the systematic effects that can be largely canceled out.

    In wide-area solutions, those cancellations are not complete enough to ignore the systematic data errors, and they have to be included in the form of additional unknown parameters in the observation equations. Also, it is necessary to account for the ionospheric delays using dual-frequency data, which means using more expensive GNSS receivers and antennas.

    Resolving the carrier-phase ambiguities is no longer straightforward or assured. The standard way of dealing with the ambiguities is to include them as unknowns in the observation equations and adjust them along with the other unknowns: this is often referred to as “floating the ambiguities.” Fixing (or resolving) those ambiguities to their most likely integer values in a matter of seconds to a minute is possible on occasion, when the aircraft is within less than 20 kilometers from a ground receiver, or very precise corrections for the ionospheric delay are available; otherwise slower techniques, that require tens of minutes, may be used. It is also necessary to correct as well as possible such things as the neutral atmospheric delay of the GNSS radio signals, the movement of the “fixed” stations due to plate tectonics, the solid earth tide using mathematical models, and, in the case of the tropospheric delay, estimating the error in the corrections made using a standard formula as an additional unknown per receiver.

    Over the years all these difficulties have been gradually dealt with more effectively, more efficiently, more reliably and, from the user’s point of view, less painfully. Originally developed for the repeated determination of station positions to measure the slow tectonic deformations of the Earth’s crust, and to calculate precisely the orbit of Earth-observing satellites, these days, after nearly 30 years of steady progress, GNSS wide-area techniques and the corresponding software find many applications in science, engineering, and navigation, and are becoming widely used in remote sensing.

    Software. We used the Interferometric Translocation (IT) wide-area positioning software developed by one of us for the long-baseline aircraft trajectory solutions and also to re-position in the IGS05 international reference frame some CORSnet-NSW stations, so their data could be used consistently in the differential wide-area solutions. These stations were originally given in the Geocentric Datum of Australia (GDA94). For both purposes we used the precise final GPS orbits computed and distributed by the IGS.

    To validate the aircraft trajectories calculated with the wide-area method, we relied mainly on the quality of the LiDAR DEM results obtained with those trajectories. We also used commercial software to generate short-baseline differential solutions with receivers deployed near the intended aircraft flight-path, as is common practice in this type of survey, and compared them with the wide-area solutions (they turned out to be quite similar to short-baseline solutions obtained with the wide-area software).

    Airborne Tests

    This study has used data from two airborne LiDAR surveys conducted by the NSW Land and Property Management Authority (LPMA) in June 2009. The first took place near the township of Glen Innes, and the second was a bore-sight calibration flight near the city of Bathurst. For both LiDAR surveys, the following data were acquired:

    • Aircraft trajectory, raw dual-frequency GPS (1 Hz) and IMU data (200 Hz).
    • LiDAR (raw return data for each laser pulse).
    • GPS reference station data from local receivers and multiple CORSnet-NSW sites.

    Glen Innes Test. This operational LiDAR survey established GND1 as the local reference station within the survey area. CORSnet-NSW data were collected for the test from GNSS receivers in Ballina (BALL), Grafton (GFTN), Nowra (NWRA), and Wagga Wagga (WGGA). FIGURE 2 shows the distribution of the reference stations and the flight runs.

    Colombo-3A copy Colombo-3B copy

    Figure 2. Glen Innes survey of June 9, 2009, showing the distribution of reference stations with baseline lengths and the survey area with (numbered) flight runs.Bathurst Test. Bathurst Airport is LPMA’s LiDAR calibration site and has various arrays of accurate ground checkpoints. AIR2, near the runway of the Bathurst airport, is the locally established GNSS reference station. CORSnet-NSW data were collected for the test from receivers in Ballina (BALL), Dubbo (DBBO), Grafton (GFTN), Newcastle (NEWC), Nowra (NWRA), and Wagga Wagga (WGGA). FIGURE 3 shows reference-station distribution and a schematic of the flight runs.

    Colombo-4A copy Colombo-4B copy
    Figure 3. Bathurst test of June 16, 2009, showing the distribution of reference stations with baseline lengths and the survey area with (numbered) flight runs.

    Effect on LiDAR Data

    Rather than simply comparing aircraft trajectories, this study aimed to determine what effect the use of wide-area GNSS positioning has on the actual LiDAR point data and associated elevation surfaces. In terms of the horizontal accuracy required for LiDAR surveys, initial tests showed that the differences between the horizontal positions of various trajectories was negligible; therefore, only the vertical component was considered in this analysis.

    To quantify differences between LiDAR data generated from trajectories using various combinations of distant GNSS reference sites, we applied four types of analysis:

    • Comparison of trajectories — directly compare the locally computed trajectory (assumed to be truth) with each wide-area derived trajectory.
    • Relative LiDAR point comparison — compare the positions for a sample of LiDAR ground points derived from the locally computed trajectory with those derived from each wide-area derived trajectory.
    • DEM comparison — difference the raster surfaces derived from the locally computed trajectory and a wide-area derived trajectory to find the effect over a LiDAR run.
    • Absolute LiDAR ground control comparison — compare the LiDAR derived surface from various trajectories to the surveyed ground control (Bathurst Calibration test site only). This also involves vertically shifting the resulting surface so that its offset relative to the one used as control is zero, thus removing the effect of using different reference frames for the GNSS trajectories and the control surface.

    Trajectory Comparison

    The comparison between the locally determined and each wide-area derived trajectory was made along the entire trajectory for each flight. The importance of this step lies in the assumption that all LiDAR data are directly positioned from the trajectory and so any systematic effect in the trajectory should be reflected on the ground. For each test site the locally derived solution is assumed to be “truth” with the vertical difference computed against wide-area solutions for each combination of reference stations used (TABLE 1).

    Colombo-T1

    Glen Innes Test. FIGURE 4 shows the vertical comparison of two wide-area derived trajectories (using BALL and GFTN, and WGGA and NWRA, respectively) against the locally derived trajectory (using GND1). It can be seen that once the aircraft attained its stable operating altitude, the wide-area derived trajectories are generally within 5 centimeters of the locally derived solution.

    Figure 4. Trajectory elevation differences for entire Glen Innes flight.
    Figure 4. Trajectory elevation differences for entire Glen Innes flight.

    Bathurst Test. The Bathurst test differs from the Glen Innes test in that both the duration of the flight and the length of each run are significantly shorter. FIGURE 5 shows the vertical component of five wide-area derived trajectories, using several combinations of CORSnet-NSW reference stations, compared against the locally derived trajectory (using AIR2). The results once again show a remarkably consistent comparison with the locally derived solution. Data spikes showing up in the DBBO/WGGA/NEWC (yellow) solution were attributed to small data glitches at the DBBO CORSnet-NSW site. Unfortunately, LiDAR data were not collected at those instances; therefore, the effect on ground data could not be fully assessed.

    Figure 5. Trajectory elevation differences for entire Bathurst calibration flight.
    Figure 5. Trajectory elevation differences for entire Bathurst calibration flight.

    Relative Comparison

    Regardless of the trajectory and orientation used to georeference LiDAR data, the same number of points will be created. It is therefore possible to create a LiDAR dataset using the same raw LiDAR data but different GNSS trajectories, and compare the results to determine the relative positioning differences on the ground.

    Given the large number (many millions) of points in a LiDAR dataset, we used a representative sample of evenly spaced 10 2 10 meter areas each containing 50–100 points (on level ground) for statistical analysis. We calculated displacement vectors between points computed from the locally derived trajectory and those using wide-area trajectories. Results from flight run 002 at Glen Innes (see Figure 2) and run 7 at the Bathurst Calibration test site (see Figure 3) are presented here.

    Glen Innes Test Run 002. The displacement vectors from 46 sample areas (4,620 points) are summarized in TABLE 2, being points computed using the two wide-area solutions compared with the locally derived solution using reference station GND1. Note the high accuracy achieved in the all important vertical component.

    Colombo-T2

    Bathurst Test Run 7. The displacement vectors from 25 sample areas (1,700 points) are summarized in TABLE 3, being points computed using the five wide-area solutions compared with the locally derived solution using reference station AIR2. Once again the results clearly show that the height values agree to within a few centimeters, even over baselines of more than 600 kilometers in length.

    Colombo-T3

    DEM Comparison

    To investigate how the LiDAR surfaces derived from each trajectory compare across the entire data swath, we created raster surfaces from the LiDAR point data. Each surface was then subtracted from the local solution to create a difference surface. Visual inspection and interpretation was then used to discern any patterns or effects.

    The result shown in FIGURE 6 (Bathurst Calibration flight run 7) was typical of the cyclical effect evident for all solutions. The magnitude of the difference was in the order of 2–3 centimeters and is in the direction of flight (north to south). If this cyclical variation is compared with the trajectory comparison for just the 33-second duration of flight run 7, a clear (expected) correlation with the variation in height is evident (FIGURE 7).

    Figure 6. Subtraction surface for Bathurst Calibration run 7 (AIR2 vs. BALL).
    Figure 6. Subtraction surface for Bathurst Calibration run 7 (AIR2 vs. BALL).
    Figure 7. Trajectory comparison for Bathurst Calibration run 7 (031318).
    Figure 7. Trajectory comparison for Bathurst Calibration run 7 (031318).

    No DEM comparison results are presented for the Glen Innes data because of significant variation in terrain and vegetation, making interpolation difficult and unreliable.

    Absolute LiDAR Comparison

    Ground control points serve two purposes in a LiDAR survey:

    • The calculation of statistics to describe vertical accuracy, that is, quantifying the match of the surface to the local height datum.
    • The calculation of a surface adjustment to enable transformation of the LiDAR points to fit the local height datum.

    Additionally, ground control points with accurate heights are used to calibrate the sensor before use in active LiDAR surveys to account for internal electrical delays in the ranging and measurement system. LPMA maintains a calibration site at Bathurst Airport for this purpose, and regularly surveys the area to ensure the sensor is operating at maximum accuracy. It should be noted that the sensor was calibrated using Bathurst Airport ground control data prior to this study.

    Surveyed Ground Control. The airport runway centerline vertical profile for the Bathurst Calibration site (FIGURE 8) was re-computed in terms of the same IGS05 reference frame determined for the LiDAR trajectories, thereby allowing an independent comparison with ground truth.

    Figure 8. Runway vertical profile at the Bathurst Airport calibration site.
    Figure 8. Runway vertical profile at the Bathurst Airport calibration site.

    Point Comparison. Data from Bathurst run 7 were used to compare LiDAR results with the established ground control using a basic triangulated irregular network (TIN) surface comparison (FIGURE 9 and TABLE 4). In Figure 9, the TIN surface is indicated by the white line, while the ground control points are shown with yellow buffers.

    Colombo-10 copy
    Figure 9. Comparison of LiDAR surface and ground control points.

    Colombo-T4

     

    The first trajectory in Table 4 is the original calibration comparison using commercial software and orthometric height data. All wide-area solutions display a similar vertical offset, because of the use of different reference frames for the GrafNav and wide-area solutions (IGS05 vs. GDA94), and differences in the implementation in software of, for example, antenna corrections and atmospheric modeling. At first glance, the significant differences to the GrafNav trajectory caused the wide-area result to not satisfy the accuracy specifications for LiDAR. However, had the wide-area solutions been used for the sensor calibration, the figures would have been much closer to the ground truth.

    Block-Shifted Data Comparison. In an operational environment, because of systematic errors in the resulting DEM relative to the local height datum, this mean vertical offset is a common occurrence with comparisons against ground control similar to those shown in FIGURE 10. Again, the TIN surface is indicated by the white line, and the ground control points are shown with yellow buffers.

    Colombo-11 copy
    Figure 10. Usual operational comparison of LiDAR surface and ground control points.

    In standard LiDAR operations, the mean vertical offset between the initial results and the ground control, at the control points, produces a zero-mean offset. Following this procedure in this case results in the variation in the comparison of LiDAR data with ground truth now being well within the required limits of 615 centimeters (TABLE 5). The values show that after a block shift, trajectory solutions are virtually identical with a root mean square error of 32 millimeters. Thus, local GNSS reference stations can be replaced by distant CORS sites without loss of accuracy.

    Colombo-T5

    Conclusions

    A precise wide-area positioning technique for airborne trajectory solutions provides both relative and absolute accuracies similar to those derived from usinga local GNSS reference station. Irrespective of which reference sites are used and once calibration and antenna modeling issues are addressed, the absolute comparison with ground control is well within the required accuracies. With the configuration of a GNSS network such as CORSnet-NSW (when complete, at least one site will always be within 150 kilometers of any point within New South Wales), an airborne LiDAR survey in the network’s service area can provide data for computation of an accurate sensor trajectory. This potentially negates the need to place and maintain ground reference stations close to the survey area — an exercise which not only requires significant resources but also reduces the operational flexibility of the aircraft.

    The challenge for this technique in an operational environment is to define and maintain a precise reference frame for all CORSnet-NSW sites and observations, including the use of a stable ellipsoidal height datum with compatible geoid modeling in order to provide local orthometric elevation data. The knowledge base required for computation of wide-area GNSS solutions is significant and requires understanding of geodesy, GNSS positioning, absolute antenna modeling, application of precise ephemerides, and derivation of the other parameters inherent to successful ambiguity resolution over long distances.

    Regardless of processing method, a LiDAR survey will always require independent ground surveys for collection of vertical checkpoints, which provide quality control to ensure the accuracy meets specifications, and the means to define any transformations necessary to fit LiDAR data with local height datum.

    Manufacturer

    NovAtel’s WayPoint GrafNav software was used for comparison purposes.

    The ”IT” Software

    • Runs under Windows, Unix, Linux, and FreeBSD.
    • Source code compatible with most Fortran compilers.
    • Follows the IERS 2003 conventions.
    • Available mainly for collaborative research purposes, with a Free Software Foundation General Public Lice
      nse.

    Type of solutions:

    • Recursive, post-processing (Kalman filter + smoothing).
    • Kinematic and static.
    • Stop-and-go for rapid mobile surveys with pre-surveyed waypoints.
    • Differential, precise point positioning, mixed mode (precise differential + point positioning).

    Data corrected for: Earth tide, neutral atmosphere radio signal delays, carrier phase windup, and so on.

    Estimated parameters:

    • Receiver position in the IGS05 reference frame, with the WGS84 reference ellipsoid, earth spin-rate, light speed, GM constant.
    • Biases in ionosphere-free carrier-phase linear combination (“floated” ambiguities).
    • Neutral zenith delay correction error.
    • Broadcast orbit errors (allows precise differential near-real time solutions).
    • Integer ambiguity resolution available in differential mode, with short baselines up to 20 kilometers (in minutes), and baselines of unlimited length (in tens of minutes — or just minutes, with a precise ionosphere correction).

    Oscar L. Colombo received a degree in electrical engineering from the National University of la Plata, Argentina, and a Ph.D. in electrical engineering from the University of New South Wales, Australia. He is an independent consultant.

    Shane Brunker is an airborne LiDAR and imaging specialist working in a consulting capacity for specialized LiDAR survey company Network Mapping (United Kingdom).

    Glenn Jones is a senior surveyor at the NSW Land and Property Management Authority in Bathurst, Australia.

    Volker Janssen is a GNSS surveyor (CORS Network) in the Survey Infrastructure and Geodesy branch at the NSW Land and Property Management Authority in Bathurst, Australia. He holds a Ph.D. from the University of New South Wales.

    Chris Rizos is head of the School of Surveying and Spatial Information Systems of the University of New South Wales, has a surveyor’s degree and a Ph.D. from the same university, and is an specialist in geodesy and GNSS positioning.

  • Can GNSS Drive V2X?

    Can GNSS Drive V2X?

    By Chaminda Basnayake, Tom Williams, Paul Alves, and Gérard Lachapelle

    Communication-enabled vehicle safety has the potential to change transportation’s future, particularly vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), collectively represented as V2X. An automakers’ consortium conducted extensive field trials to determine GNSS service availability and accuracy for the V2X challenge.

    V2X can include applications based on communications between any two or more entities on the road. Of all the potential V2X applications, V2V applications probably lead the way in terms of maturity of prototype development and test efforts. General Motors (GM) demonstrated the first working prototype V2V system in 2005. Information on further industry collaborative efforts in V2V system developments can be found at the U.S. Department of Transportation’s (DOT’s) IntelliDrive website. While a multitude of applications could be developed based on V2I capability, most of the related system prototype development efforts have taken place under the DOT’s Cooperative Intersection Collision Avoidance (CICAS) program.

    B-Figure-4-copy
    Driving environments encountered in testing. Clockwise from top left: deep urban, urban thruway, local roads, mountains.

    Accuracy Requirements

    In terms of positioning accuracy requirements, Vehicle Safety Communications-Applications (VSC-A) prototype system capabilities as well as all V2X applications can be classified as:

    Which Road. In this case, accuracy is only required to the extent of identifying the road traveled. For instance, if a vehicle is in a service road parallel to a freeway, knowing that it is on the service road and not on the freeway is sufficient. The need of a typical vehicle navigation device is another good example of this requirement category. The typical accuracy requirement for this case is better than 5 meters. However, this could be a relative accuracy requirement for certain applications. For instance, in a V2V scenario, one vehicle may only need to know if the other is on the same road or not, while in the absolute sense both vehicles could be in error by more than 5 meters. For V2I applications, however, this becomes an absolute accuracy requirement, as the infrastructure is always mapped and identified with respect to a global coordinate frame.

    Which Lane. This accuracy level enables applications to identify other entities with lane level resolution. The typical requirement is 1.5 meters or better, which approximately corresponds to half of a lane width. A blind-spot advisor is a good example that requires this accuracy.

    Where-in-Lane. This accuracy level enables the relative positioning of entities to better than 1 meter. Further refinements of blind-spot advisor-like applications are examples.

    Availability Requirements

    GNSS as a line-of-sight technology has obvious limitations in certain environments, and these limitations are well understood by the GNSS community. The focus of this study was to understand the limitations associated with a GNSS-only V2X solution such that requirements for augmentation technologies can be defined. Therefore, no availability requirements were set for the system; estimating availability of a GNSS-only solution was the goal.

    Why So Complicated? At first glance, what needs to be done is straightforward; all V2X-capable entities need to be aware of each other’s positions. Hence, if all entities transmit their own location with respect to the same coordinate system, the problem is solved. Unfortunately, it’s not that simple.

    Designing the system so that hundreds of entities, potentially using all sorts of GNSS software and hardware, can work together presents a significant challenge. This includes keeping backward compatibility way out into the future.

    Even within the same receiver make and type, inclusion of a particular satellite in the solution of one vehicle can significantly affect the solution difference between vehicles. Inclusion of SBAS also contributes as a differentiator. In a V2X scenario, out of two adjacent vehicles, one vehicle may use SBAS while the other may not, due to hardware configuration or visibility. If none of the above situations occurred and everything else were ideal, transmitting just the current horizontal position of a V2X entity over-the-air (OTA) would be sufficient to do everything needed.

    V2X thus requires a positioning system architecture that minimizes the impact of these complications and many other potential compatibility issues. Major system design considerations include:

    Performance Requirements. The system must provide relative positioning accuracy that fits Which Road, Which Lane, or Where-in-Lane category and should identify the solution quality. For instance, a vehicle on a freeway with relatively open sky view may function in the Which Lane mode and may transition to Which Road mode as it enters an urban area with sky visibility limitations.

    Deployment Constraints. The system must be affordable for automotive applications. This may also include considerations such as antenna placement, processing resource requirements, and power requirements.

    Bandwidth Constraints. The volume of data transmission constitutes a major consideration for OTA communications. While some methods manage communication range and frequency as a way of optimally using the communication channels, keeping the OTA data volume to a minimum by design was a goal.

    Study Goals

    This study investigated the performance of two relative positioning methods: DPOS, a method of using the difference in position reported by two entities to calculate the 3D separation between the points; and real-time kinematic (RTK). While there are many other possible relative positioning methods, these two were selected as they collectively represent the most desirable availability and accuracy performance. In DPOS, vehicle coordinates are transmitted between vehicles in order for position differences between vehicles to be derived at each vehicle. In RTK, raw code and carrier-phase data is transmitted between vehicles, and the inter-vehicle position differences are calculated using RTK software in either fixed or float carrier-phase ambiguity mode at each vehicle. The RTK method is more intensive both from a data transmission and computational aspect, but retains only common satellites in the solution, eliminating the problem described earlier. Its use of carrier-phase measurements also makes it more accurate.

    The study included two GPS receiver types. The first, a single-frequency L1 automotive-grade receiver, is identified as Type B receiver in this study. The second, identified as Type A, was of a higher quality with proprietary multipath mitigation technologies. Both receivers were capable of using WAAS support. Receiver B also allowed the user to reject selected satellites from its solution. These two devices were selected as they were capable of supporting both processing methods, and represent on the one hand an existing automotive-grade receiver, and on the other hand one that is expected to be a good representation of a product with technologies available for automotive deployment a few years from now.

    Specific study goals were:

    • Accuracy performance of DPOS and RTK methods when all vehicles use same GPS receiver type.
    • Same when a receiver type or a receiver configuration mix is used.
    • Dependency of the accuracy performance on the driving environment.
    • Solution availability with same receiver and mix receiver combinations.
    • Implications of non-continuous V2I coverage.

    Prototype System

    The system prototype (Figure 1) used for the study was a replica of the prototype relative positioning system implemented in the VSC-A project. It consists of a dedicated short-range communicatin (DSRC) interface with a DSRC radio, a GPS receiver/relative positioning module, and a sensor data handler.

    Figure 1. VSC-A prototype relative positioning system.
    Figure 1. VSC-A prototype relative positioning system.

    In operation, a vehicle generates its own location information and GPS raw data in RTCM format and shares this data with other vehicles. OTA messaging was done using the SAE J2735 messages set with GPS raw data in RTCM format attached as optional data. As shown in Figure 1, RTCM v3 1002 messages were used to exchange VSC-A data. The system was also capable of using RTCM v3 messages 1001 & 1005 for V2I operation. The DPOS relative positioning logic was implemented in the sensor data handler, while the RTK implementation was done in a separate relative positioning module. This module takes in local and remote 1002 messages and outputs RTK data to the sensor data handler. Applications could access both RTK and DPOS relative positioning information from the sensor data handler.

    Vehicle Setup. Two vehicles were used for the V2X data collection. Four different GPS L1-only test receiver types were installed on each vehicle:

    • AW: high-quality receiver using WAAS corrections.
    • BW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections enabled.
    • BNW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections disabled.
    • B24W: high-sensitivity automotive-grade receiver using a maximum of the four primary satellites in each of the six planes (minimum guaranteed constellation) and with WAAS ranging and corrections enabled.

    As shown in Figure 2, the AW and B type receivers were connected to different GNSS antennas. These antennas were mounted on roof-racks attached to the vehicles (see Photo). The patch antenna for the Type B receivers was mounted on an aluminum-topped wooden pedestal to bring it to approximately the same height as that used by the AW receivers, to provide a ground plane and to prevent shading from other equipment on the roof-racks. The spacing between the antennas was accounted for in all analysis.

    Figure 2. High-level V2V hardware setup on each of the two test vehicles.
    Figure 2. High-level V2V hardware setup on each of the two test vehicles.

    Figure 2 also shows that only three of the four test receivers, AW, BW, and BNW, were connected to the computer that ran the RTK software. This computer calculated the inter-vehicle vector (IVV) using information exchanged over the DSRC radio link in real time. The vehicles each had a designated base relative to which the IVV was calculated; for Vehicle 1 it was BW and for Vehicle 2 it was AW. Thus the computer on each vehicle calculated three instances of the IVV, for example, the computer on Vehicle 1 calculated BW1–BW2, BW1–BNW2, and BW1–AW2 (where Ri denotes the receiver of type R on vehicle i).

    Transmission and reception of data between the two vehicles required for the IVV RTK calculations were achieved using wave radio modules with two magnetically mounted 802.11p antennas on each vehicle for redundancy. During testing, Vehicle 1 generally followed Vehicle 2. To minimize potential interference of roof-mounted instruments on between-vehicle communications, the antennas on Vehicle 1 were located close to the front of the roof, while those on Vehicle 2 were located close to the rear of the roof. In each case, 15 centimeters of roof space were left to provide ground planes for the antennas.

    We used the single-point navigation solutions logged from each test receiver to calculate the IVV for each receiver combination using the DPOS method in post-processing. No real-time data transfer between the vehicles was used for this method.

    Reference values of the IVV were calculated in post-processing using both geodetic grade GPS/GLONASS L1/L2 receivers and GPS/INS integrated systems in differential mode. Both were connected to the antenna used by the AW receiver. Differential GPS calculations were enabled by using stationary receivers with antennas at precisely known WGS84 locations on top of a building at the University of Calgary.

    Two study vehicles with antennas attached to the roof-racks.
    Two study vehicles with antennas attached to the roof-racks.

    Test Scenarios

    V2V data was collected in and around the city of Calgary in August 2009. In the majority of the tests, Vehicle 1 followed Vehicle 2 with a separation of less than 300 meters, the stated effective range of the DSRC link. For most tests the inter-vehicle separation was between 30 and 150 meters. Some driving environments forced modifications of the default behavior; for example, on highways, vehicles moved in between the two test vehicles, necessitating lane changes. Approximately 52 hours of data was collected over 12 days. After rejecting data due to various faults such as reference-system malfunction, more than 45 hours of data remained.

    Data was collected in the seven test environments listed in Table 1. These environments were selected in accordance with Federal Highway Administration descriptions. Each environment provided different challenges for GNSS-based positioning. Obviously the deep urban environment was challenging because the reduced number of visible satellites and the large amount of multipath meant that navigation solutions were both rare and of poor quality. As another example, the mountain environment was interesting because often almost half the sky was occluded by trees on the mountain side, leading to an asymmetrical visible GPS satellite constellation with the associated solution degradation. The photos at the beginning of this article show selected driving environments encountered during testing.

    B-Table1
    Table 1. Description of driving environments used in V2V tests.

    V2V Solution Accuracy. Positioning accuracy of the individual receiver was first investigated to estimate the V2V relative positioning accuracy when using the DPOS method. This was done for the entire dataset.

    Figure 3A shows a representative freeway dataset to illustrate overall trends: the absolute 2D mean position errors observed from all eight GPS receivers used in both vehicles. The first set of four receivers shown were the AW, BW, BNW, B24W receivers in the first vehicle (V1), and the second set of receivers were the same type in the second vehicle (V2). As a general trend, Type A receivers provided better absolute accuracy meeting the Which Lane accuracy, whereas the Type B receivers provided Which Road accuracy. Also, the use of WAAS with receiver Type B has yielded some absolute accuracy improvement. Limiting the constellation to 24 (B24W) did not significantly degrade accuracy in this case.

    As a second step, V2V relative accuracy when the same receiver type was used was estimated, and the mean errors are shown in Figure 3B. Based on the mean error for each pair, all four receiver pairs were able to provide Where-in-Lane relative position accuracy. The geodetic grade Type A receiver pair (AW–AW) yields the best relative accuracy at around 0.5 meters relative 2D error. In comparison with the mean absolute errors, the V2V relative accuracy is greatly improved as a result of cancellation of correlated errors, indicating a high degree of correlation of absolute errors in receivers under these test conditions.

    The relative accuracy with mixed receiver types or configurations was also estimated. With r
    espect to receiver type mixes, the Type A receiver from vehicle 1 was used with the three Type B receivers in vehicle 2, yielding three combinations as AW–BW, AW–BNW, and AW–B24W. Mean error statistics for these three combinations and the combination of BW from vehicle 1 and B24W from the second vehicle are shown in Figure 3C. In comparison to the same type receiver pairing, this shows much larger mean errors. For instance, for all AW receiver mixes, the mean relative error is around 2 meters. Therefore, it is fair to conclude that error characteristics and modeling in the navigation solutions in receiver A and B are type-dependent, and they may not be compatible when a receiver mix is used. The BW–B24W combination does not show a significant increased mean error, indicating that the constellation difference in this test was not significant enough to result in an increased relative positioning error.

    Figure-5a copy
    Figure 3A. Individual receiver absolute accuracy.
    Figure-5b copy
    Figure 3B. Relative accuracy with same receiver type.
    Figure-5c copy
    Figure 3C. Relative accuracy with receiver/configuration mix.

    V2V Solution Availability

    Availability statistics were generated for all accuracy categories (Which Road, Which Lane). At a more abstract level, solution availability statistics were also calculated for the DPOS and RTK methods. RTK solutions were defined as available whenever the software yielded a solution for that particular epoch. Data gaps in the RTK method could be caused by either communication failure due to, for example, a large truck entering the line of sight between vehicles, or one vehicle disappearing around a corner, or because insufficient observations from common satellites were available at the two vehicles. DPOS solutions, calculated in post-processing, were defined to be available whenever both receivers had observations from four or more satellites and were therefore able to calculate the necessary independent position solutions. While the two definitions of availability are not quite congruous, because only that for the RTK includes the possibility of communication failure, comparison of logs of data transmitted between the vehicles showed that out of approximately 45 hours of data, only 0.22 percent of missing RTK solutions could be attributed to failure of the DSRC link.

    Figure 4 plots the distribution of GPS service outages observed by AW and BW receivers in individual vehicles in all of the test environments including deep urban. Here, as described for the DPOS method, an outage for a single receiver is identified on an epoch basis whenever the receiver has observations from less than four satellites. The total driving time included in this dataset is 45 hours and 4 minutes for each receiver. Figure 4 [deep urban] shows the same statistics for deep urban environment driving only, and this contains 1 hour and 40 minutes of driving for each receiver. The latter was selected specifically as this environment contained the most challenging conditions.

    Figure-6 copy
    Figure 4. Distribution of GPS service outages for individual vehicles.

    An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages lasted less than 30 seconds. However, when using the high-sensitivity enabled Type B receivers, 100 percent of the outages lasted less than 5 seconds. No significant outage difference is seen between the observations from the same receiver type in the two vehicles.

    GPS service availability for V2V applications was calculated using two approaches for the two relative positioning methods. For the DPOS method, individual vehicle service availabilities were time-synchronized in post-mission, and V2V DPOS solution availability was estimated. Figure 5 compares V2V solution outages using both receiver types and both relative positioning methods.

    Figure-7 copy
    Figure 5. Distribution of GPS service outages for V2V applications.

    The DPOS method yields better solution availability statistics than RTK. With both receiver types, more than 95 percent of DPOS solution outages are less than 10 seconds. With the RTK method, relatively longer outages were observed, especially for Type B receivers. With Type A receivers, the difference is only significant for outages shorter than 30 seconds. For Type B receivers, larger percentages of longer RTK outages were observed; this can be potentially attributed to poor carrier-phase tracking loop performance of these receivers and the impact on RTK.

    Using GNSS Data

    We anticipated performance issues arising from receiver type and configuration incompatibilities going into the prototype development effort. We identified use of raw GPS measurements instead of the DPOS method as one method to overcome this limitation, as the differencing techniques with measurement data guarantees correlated error cancellation. This was one reason to include the RTK capability in the prototype system. Therefore, confirming the fact that use of raw measurements eliminates the receiver type and configuration-related incompatibilities was a major goal of the study.

    As discussed earlier, V2V relative position solutions using RTK were logged in real time as a part of the test setup. We compared these real-time RTK solutions and the DPOS solutions estimated in post-mission for all datasets. Figure 6 shows three cumulative probability distribution (CDF) plots generated using RTK and DPOS accuracy data from a freeway test dataset. The first CDF plot (left) shows the comparison of accuracy when both vehicles use Type A receivers with RTK and DPOS methods. The second CDF plot (center) shows the same CDFs when both vehicles use the Type B receivers. The third shows the DPOS and RTK accuracy CDFs when vehicle 1 uses Type A receiver and the other uses Type B receiver.

    Figure 6 demonstrates that if higher quality GPS receivers similar to Type A are used in both vehicles, both RTK and DPOS methods would provide a solution of better than Which Lane accuracy more than 90 percent of the time. However, if Type B receivers are used, a solution with similar accuracy will only be available 60 percent of the time if the DPOS method is used for relative positioning of the vehicles. If the RTK method is used, this availability can be increased up to 90 percent.

    The performance difference between the two methods becomes even more prominent when the two vehicles use a mix of receiver types. In the right-most CDF of Figure 6, a solution with Which Lane accuracy is only available 30 percent of the time if DPOS method is used with the mixed receiver configuration. The RTK solution availability still remains around 90 percent even with the mixed configuration. This confirms that use of measurement data eliminates some of the limitations associated with the DPOS method.

    Comparison of only the RTK performance between all three CDFs in Figure 6 shows that RTK V2V performance is only limited by the worst-performing receiver in the receiver combination. Out of the three CDFs, the middle (both vehicles using Type B) and the right (Type A and B mix) CDFs have almost identical RTK performance curves. Given that the RTK curve with both using Type A receivers shows much better performance, it is fair to conclude that in the mixed-receiver case, the RTK curve is limited by the performance of th
    e Type B receiver. Figure 6 also shows that at Which Road accuracy, all receiver combinations and both processing methods yield almost identical performance.

    B-Figure-8
    Figure 6A. Comparison of V2V solutions using RTK and DPOS methods.
    B-Figure-8B
    Figure 6B. Comparison of V2V solutions using RTK and DPOS methods.
    B-Figure-8C
    Figure 6C. Comparison of V2V solutions using RTK and DPOS methods.

    Other Approaches

    Given that carrier-phase measurements are subject to cycle slips in some road environments, we ran a test using code measurements only in relative mode, using selected data sets collected on a mountainous highway. Only common satellites were used. Given that code measurements are not affected by a loss of phase lock, such a solution is more robust, but is subject to code noise and multipath. The RMS horizontal position differences between these solutions and the reference inter-vehicle separations were 25 centimeters and 1 meter for receiver Types A and B, respectively. Both receiver types meet the Where-in-Lane requirement in this test. Type A, with its low code noise and excellent code multipath-reduction capability, has a clear advantage.

    Such an approach would represent a compromise between the DPOS and RTK approaches. Its advantage over the RTK approach is a lower data transmission-rate requirement, while that over the DPOS approach is the use of common satellites only. The latter is quite significant, since low-elevation satellites contribute the most to horizontal position solutions, but their measurements are affected more by atmospheric transmission errors that are most effectively removed in differential mode on a satellite-by-satellite basis.

    V2V Operation with V2I

    While infrastructure support can almost always improve the performance of other V2X applications, it can pose a challenge for positioning when such coverage is not continuous. The complication arises as a result of vehicles transitioning in and out of V2I coverage areas. V2I systems are highly likely to include GNSS augmentation capability so that vehicles within a coverage area benefit from better positioning capability. However, when vehicles transition from standard (V2V) operation mode to a V2I enhanced mode, some effects in the vehicle position domain can pose potential challenges for DPOS-based V2V.

    The field study included test scenarios with limited V2I coverage in different driving environments: all of those described above with the exceptions of deep urban and mountains. In deployment, the infrastructure points (IPs) would broadcast aiding information to the vehicles within their coverage area, allowing real-time calculations. In the field study, in which the role of the IP was filled by a stationary high-grade receiver with a tripod-mounted antenna, all V2I estimates of the IVV were calculated using post-processing. Further, V2I estimates of the IVV were only calculated when at least one of the vehicles was within the coverage area of the IP, here chosen to be a circle of radius 300 meters centered at the IP. This range was chosen since it is the nominal effective range of the DSRC link.

    The location of the IP, that is, the phase center of the stationary antenna, was determined using commercial RTK network software with additional stations at precise locations on the rooftop of a building at the University of Calgary. The estimated accuracy of this position was 5 millimeters (1 sigma). The distances of the vehicles from the IP, used to indicate when the vehicles transitioned into and out of the IP coverage area, were determined using the GPS/INS reference trajectories. In post-processing, once a vehicle was identified as having entered the IP coverage area, commercial RTK software was used to estimate the position of the vehicle, using the IP as base and each of the test receivers on that vehicle as rovers. The IVV was then calculated using the difference of the positions of the two vehicles. Thus, the V2I estimate of the IVV was determined using what is essentially the DPOS method with stationary base RTK-indicated vehicular positions, instead of the less accurate single-point GPS position solutions. When only one vehicle was within the coverage area, single-point solutions were used for the distal vehicle, resulting in a solution called V2I-S.

    Figure 7 shows two sets of CDFs generated to illustrate the V2V positioning accuracy with V2I capability. The left plot corresponds to AW–AW receiver combination, and the right plot corresponds to the BW–BW combination. Each plot includes four curves. One pair of curves shows the V2V positioning accuracy without V2I, which includes performance when using the DPOS method (green) and another when using RTK (blue). The second pair shows the accuracy of the V2I and V2I-S estimates.

    The most striking observation from Figure 7 is the separation of the V2I-S case from others for both receiver combinations (purple). It shows much worse positioning accuracy compared to the other three curves. For instance, using a BW–BW pair, the system will meet the Which Lane accuracy requirement around 80 percent of the time for either DPOS or RTK V2V without V2I support. However, when V2I coverage is available to only one vehicle, the V2I-S case, the accuracy requirement is only met at 40 percent confidence.

    B-Figure-9A
    Figure 7A. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).
    B-Figure-9B
    Figure 7B. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).

    Thus, system accuracy performance degrades when vehicles are operating in DPOS mode and are transitioning in and out of the V2I zones. This is because the V2I-S estimate is the difference of an accurate position solution for the vehicle within the coverage zone, and a potentially inaccurate single-point solution for the one outside the coverage zone. The beneficial cancellation of similar errors that occurs for DPOS estimates (using similar receivers and with common satellite observations) does not occur for V2I-S.

    Potential solutions to this problem include using a V2I method of IVV calculation that is not dependent on the estimated position alone (that is, use RTK or other measurement-based methods as opposed to DPOS), or using a position-mode indicator with the DPOS mode such that a DPOS-based V2V solution is only generated when both vehicles are operating in the same mode (that is, V2I). However, the latter does not provide a remedy for the complications when the two vehicles are operating in two different modes. One could also consider a variation of the latter method whereby a V2I-augmented position and a non-augmented position is maintained by each vehicle, such that one of them could be used to generated a mode-matched DPOS V2V solution for a given sender.

    Recommendations

    These extensive trials provided valuable data demonstrating technical challenges associated with V2X positioning.

    • Error characteristics and modeling in the navigation solutions in receivers A and B are type-dependent, and they may not be compatible when a receiver mix is used with the DPOS mode. This is very likely to be the case for many other commercial receivers. Therefore, it is important to develop receiver hardware and software minimum-performance standards that define acceptable performance for measurement quality, satellite tracking and selection criteria, reliability estimates, navigation-solution parameters, and other such indicators.
    • Findings with RTK confirm the fact that use of measurement data eliminates some of the limitations associated with the DPOS method. While RTK is the most demanding raw data-based method in terms of processin
      g requirements and OTA data needs, the study also conducted limited investigation on other methods that use raw code data and are less resource-intensive, and at the same time better performing than DPOS. Such an approach would represent a compromise between the DPOS and RTK approaches.
    • An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages were less than 30 seconds. These statistics are useful for future research on suitable GNSS augmentation methods.
    • System accuracy performance degrades when vehicles operate in DPOS mode and transition in and out of the V2I zones. Potential solutions should be incorporated into the systems to take care of these limitations.

    Acknowledgments

    The authors thank the Crash Avoidance Metrics Partnership Vehicle Safety Communications-Applications team, in particular the Vehicle Positioning Technology Development team, for input. This work was conducted as a part of a CAMP VSC-A project under a cooperative agreement with the U.S. DOT.

    CHAMINDA BASNYAKE is a senior research engineer at General Motors Global Research and Development and GNSS technology expert for GM OnStar. He leads GNSS-based vehicle navigation technology R&D efforts at GM and holds a Ph.D. in geomatics engineering from the University of Calgary.

    TOM WILLIAMS is a postdoctoral researcher in the PLAN group in the Department of Geomatics Engineering at the University of Calgary.

    PAUL ALVES is a Calgary-based geomatics consultant specializing in RTK. He obtained his doctorate from the University of Calgary.

    GERARD LACHAPELLE holds an iCORE/CRC Chair in Wireless Location and heads the PLAN Group in the Department of Geomatics Engineering at the University of Calgary.

  • First quasi-zenith satellite, Michibiki, achieves final orbit

    The Japan Aerospace Exploration Agency (JAXA) has controlled the orbit of the first quasi-zenith satellite system (QZSS) satellite, Michibiki. JAXA inserted Michibiki into the quasi-zenith orbit from the drift orbit starting on Sept. 21. The final orbit control operation was performed for about 50 seconds from 6:28 a.m. on Sept. 27 JST (Japan Standard Time).

    After the operation, JAXA confirmed that the satellite was successfully injected into its preordained quasi-zenith orbit with its center longitude of about 135 degrees through the orbit calculation. The calculation results are as follows.

    Michibiki was launched from the Tanegashima Space Center at 8:17 p.m. JST on Sept. 11.

    JAXA will carry out the initial functional verification of the onboard mission devices in cooperation with organizations that will perform technological verifications for about three months. These organizations include the Geospatial Information Authority of Japan, the National Institute of Advanced Industrial Science and Technology, the National Institute of Information and Communications Technology, the Electronic Navigation Research Institute, and the Satellite Positioning Research and Application Center.

    JAXA provided these definitions of drift and quasi-zenith orbit:

    Drift orbit: The last step orbit prior to the quasi-zenith orbit. The orbit altitude and inclination (angle against the equator) are equal to those of the quasi-zenith orbit, but the longitude of the center of the figure-8 orbit is not above Japan. After being injected into the drift orbit, it will take a few days to maneuver the satellite to have its figure-8 center above Japan, thus it will ultimately fly in the quasi-zenith orbit.

    Quasi-zenith orbit: While the quasi-zenith orbit has the same orbit period of 23 hours and 56 minutes as the geostationary orbit, it can let a satellite stay over Japan longer by taking an elliptical orbit with higher altitude above Japan and flying in a figure-8 orbit.

  • Air Force to Respond to GAO Report on GPS

    Global Positioning System experts from Air Force Space Command and the Space and Missile Systems Center will hold a media roundtable teleconference tomorrow, September 24, at 2:30 p.m. Mountain Time (4:30 p.m. Eastern Time) to discuss the recent GAO report titled “Global Positioning System: Challenges in Sustaining and Upgrading Capabilities Persist.” Colonel David Buckman, AFSPC command lead for positioning, navigation and timing, and Colonel Bernard Gruber, commander of the Global Positioning System Wing at Los Angeles Air Force Base, will participate in the teleconference.

    Air Force Space Command, which has responsibility for sustaining and maintaining the Global Positioning System, feels that the GAO report is overly pessimistic and doesn’t adequately acknowledge what AFSPC has done to address constellation sustainment, according to a press release issued from the Air Force, Peterson Air Force Base, Colorado. “The Air Force has created the largest, most accurate constellation, with the greatest capability, in the history of GPS, with 31 operational satellites currently on orbit,” stated the press release. “This is well above the 24 minimum satellites needed for a full constellation and to meet constellation performance standards. Since 1995, GPS has never failed to exceed performance standards.”

    The release continued, “AFSPC is working to mitigate the challenges identified by the GAO through a number of activities, including: applying a ‘back-to-basics’ approach to acquisition, continuing to identify additional ways to maximize the life of our operational satellites, implementing robust mission assurance processes, and transforming our launch enterprise.”

    The first GPS IIF satellite completed on-orbit testing and checkout and was set operational on August 26 as planned, the Air Force said, The GPS IIF program is ready for full rate production and continues to build confidence in its production line.  Through the institution of robust mission assurance processes, AFSPC is confident in the future of the GPS IIF program.

    The follow-on program, GPS IIIA, recently completed critical design review, two months ahead of schedule, the Air Force said. “AFSPC is optimistic that its ‘back-to-basics’ approach, including stable requirements, mature technologies, and more government oversight, will ensure a successful program, providing the GPS IIIA and its ground segment, OCX, within a timeframe that maintains a robust GPS constellation and supports GPS users.”

  • GLONASS Update Delves into Constellation Details

    At the Civil GPS Service Interface Committee meeting in Portland, Oregon, on Monday, Sergey Revnivykh, Deputy Director General of Roscosmos’s Central Research Institute of Machine Building reported on the status and future of GLONASS.

    He provided a number of previously unpublished details on the present constellation and how it will be augmented in the future.

    The present constellation officially has two reserve satellites, GLONASS 714 and 726. Revnivykh stated that neither of these satellites would ever be brought back to active service. 714 was a flight-test satellite, apparently, and 726 had a failure of its navigation payload. Rather than being possible replacement satellites, these vehicles are being used to train the ground team to operate spare satellites in a full or nearly full constellation.

    GLONASS 727, in orbital slot 3, which was taken out of service on 8 September 2010 has also had a failure of its navigation payload and will not be returning to service. About 11 more GLONASS-M satellites will be launched by the end of 2012.

    Revnivykh announced that there will be two versions of the new GLONASS-K satellites: GLONASS-K1 and GLONASS-K2. GLONASS-K1 satellites will have a 10-year design life and a daily clock stability of 5 ´ 10-14. The first GLONASS-K1 satellite will be launched this December from the Plesetsk Cosmodrome about 800 km north of Moscow. This will be the first launch of a GLONASS satellite from other than the Baikonur Cosmodrome. Only one more GLONASS-K1 satellite will be built and launched after that. The K1 satellites will test an open service CDMA signal on the GLONASS L3 frequency in the 1205 MHz band. When asked when the specific structure of the CDMA signal would be announced, Revnivykh said he didn’t know.

    A completely new design, GLONASS-K2, will start launching in 2013. GLONASS-K2 satellites will have a 10-year design life and a clock stability of 1 ´ 10-14. Besides the CDMA signals on L3, CDMA signals will also be transmitted on L1 and L2. The GLONASS-K satellites will transmit the legacy FDMA satellites in addition to the CDMA signals.

    A modernized GLONASS-K satellite, GLONASS-KM, for launch after 2015 is now under study. In addition to transmitting legacy FDMA signals on L1 and L2 and CDMA signals on L1, L2, and L3, CDMA signals may also be transmitted on the GPS L5 frequency at 1176.45 MHz. Also being studied is an alternative to the present three-plane, equally spaced satellite constellation. A different constellation design would be possible using CDMA signals. Such a move would require that the legacy FDMA signals be switched off. Revnivykh stated that any such move would require at least 10-years’ advance notice.

    The design characteristics of the various generations of GLONASS satellites are shown in Figure 1, taken from Revnivykh’s presentation.


    Figure 1. The GLONASS satellite generations through GLONASS-K2.

    The signals that will be transmitted by the future generations of GLONASS satellites as well as those transmitted by the initial GLONASS satellites and the GLONASS-M satellites now on orbit are shown in Figure 2.

    Figure 2. Signals transmitted by the different generations of GLONASS satellites. OF = open-access FDMA, SF = special (military) FDMA, OC = open-access CDMA, OCM = open-access CDMA modernized.

     

    Revnivykh also spoke on the satellite-based augmentation system, SDCM (System for Differential Correction and Monitoring), under development. Correction and integrity data will be transmitted by Luch geostationary communication satellites now under development. Luch 5A, to be launched in 2011 and positioned at 16°W longitude and Luch 5B, to be launched in 2012 and positioned at 95°E longitude, will transmit signals on an L1 frequency. Luch 4, to be launched in 2013 and positioned at 167°E longitude, will transmit on two frequencies. The three satellites will provide almost global coverage. The satellite payloads are under development.

    According to Revnivykh, the SDCM will make use of 12 monitor stations currently in operation in Russia and one in Antarctica at Russia’s Bellingshausen research station. However, the SDCM website indicates only 10 Russian stations currently in the test network. Eight more monitor stations will be added in Russia and five more outside Russia. Revnivykh showed a map revealing the locations of the additional overseas stations as Cuba, Brazil, Vietnam, Australia, and an additional station in Antarctica. It is not intended, at least initially, that these stations would be used in generating the orbit and clock data broadcast by the GLONASS satellites themselves.

    And finally, Revnivykh stated that a GLONASS performance document will be released in the 2012-2013 time frame.

    Revnivykh’s full presentation will be available on the U.S. Coast Guard Navigation Center website by the end of next week.
     

  • First QZSS Satellite, Michibiki, Launched

    News courtesy of CANSPACE Listserve.

    Launch of the first Quasi-Zentih Satellite System (QZSS) satellite, dubbed Michibiki, took place Saturday, September 11, at 11:17:00 UTC from the Tanegashima Space Center in Japan. At 28 minutes and 27 seconds after liftoff, Michibiki was separated from the launch rocket, H-IIA Launch Vehicle No. 18, according to Mitsubishi Heavy Industries, Ltd. and the Japan Aerospace Exploration Agency (JAXA).

    JAXA performed the third apogee engine firing (AEF) for about 7 minutes from 12:21 p.m. on September 14
    (Japan Standard Time, JST) by sending commands from the Okinawa station in Japan. Telemetry sent from the satellite confirms the third burn was a success. The satellite is in good health.

    The launch plan is available here.

    Initial NORAD transfer orbit elements:
    1 37158U 10045A   10254.58030900  .00025279  00000-0  12437-1 0    29
    2 37158 031.8130 195.1731 7247071 180.1473 075.8512 02.30802581    08

    Here is a video of the launch:

    Another video can be viewed here. Below is an image of the ground track of the new orbit after the third AEF. Another two AEFs followed by thruster firings are required before the final orbit is obtained. Altogether the process is expected to take about two weeks.

     

  • The System: Michibiki Takes Up Station and Other GNSS Constellation Updates

    The System: Michibiki Takes Up Station and Other GNSS Constellation Updates

    As this issue goes to press in late August, the first Japan Aerospace Exploration Agency Quasi-Zenith Satellite System (QZSS) space
    vehicle, nicknamed Michibiki, holds steady for a September 11 launch.

    QZSS will use multiple satellites in inclined orbits, placed so that one satellite always appears near zenith above Japan, well known for its high-rise cities. The design provides high-accuracy satellite positioning service covering almost all of the country, including urban canyons and mountainous terrain.

    QZSS Phase One will validate technological enhancement of GPS availability, performance, and application. Phase Two will demonstrate full system capability using three QZSS satellites, including Michibiki.

    The satellites will generate and transmit their own signals, compatible with modernized GPS signals. QZSS also transmits GPS corrections and availability data.

    Michibiki Profile. Dual-box shape with wing-type solar-array paddles; overall dimensions, 2.9 x 3.1 x 6.2 meters, paddles extending 25.3 meters; weight approximately 4,000 kilograms; altitude approximately 32,000–40,000 kilometers; inclination approximately 40 degrees;
    period, 23 hours 56 minutes.

    Compass. In early August, the first Beidou/Compass inclined geosynchronous orbit (IGSO) satellite achieved near-geosynchronous orbit. The mean east longitude of the sub-satellite ground point is currently 117 degrees, 19 minutes (see figure 1). This is one of the first, if not the first, satellite to use such a highly inclined circular geosynchronous orbit.

    QZSS-orbits
    Figure 1. Left, the orbit path of three QZSS satellites will eventually keep at least one of them directly over Japan at all times. Right, the inclined geosynchronous orbit of the fifth Compass satellite, launched in July, has a similar ground track and mission goal.

    Multi-GNSS Campaign. An international collaboration is poised to take advantage of a coming proliferation of satellites, led by Compass and QZSS but also including GPS, GLONASS, and Galileo, over the Asia/Pacific region. The website www.multignss.asia/campaign.html states, “The Asia and Oceania region is a unique place where the number of usable modernized navigation satellites will increase much faster than other areas in the world. We will see great improvement of PNT capability and hence there is a great opportunity to try, test, and validate new receiver hardware, algorithms, and applications in order to address user requirements.”

    The web page also carries an animation of the availability of more than 100 GNSS space vehicles that will operate over the region in the next decade. An initial campaign workshop in Bangkok, Thailand, in January drew 195 participants from 18 countries. A second workshop is scheduled for November 21–22 in Melbourne, Australia.

    GLONASS September. Three GLONASS-M satellites to be launched on September 2 completed pre-launch testing and mating to the upper stage of the booster rocket at Baikonur Cosmodrome. Numbered 36, 37, and 38, the satellites will constitute the Block 42 triad.

    GPS III Design: Done. The Lockheed Martin team developing GPS III has successfully completed the program’s Critical Design Review (CDR) phase, two months ahead of baseline schedule. CDR completion validates the detailed GPS III design to ensure it meets warfighter and civil requirements. It culminates many rigorous assembly, subsystem, element, space vehicle and system-level CDR events, validates the overall design maturity of the GPS III space vehicle, and allows Lockheed Martin to enter production phase. Col. Bernard J. Gruber, U.S. Air Force GPS Wing Commander, certified the completion. Lockheed Martin, ITT, and General Dynamics are working under a $3 billion development and production contract for up to 12 GPS IIIA satellites. The team is on track to launch the first GPS IIIA satellite in 2014.

    GPS Interface Specs. New IS-GPS-200E, IS-GPS-705A, and IS-GPS-800A documents have been posted to www.gps.gov/technical.
    SVN62 Rubidium Clock. The U.S. Naval Research Laboratory issued a preliminary report on the rubidium atomic clock currently in use on the SVN62 Block IIF satellite. While documenting excellent short-term performance, the report notes anomalous fluctuations in the clock signal with distinct 12-hour and 6-hour periodicities. The exact cause has not been identified although it is speculated that the fluctuations are of thermal origin like SVN-62’s L5 phase variance detected earlier. But note that the clock signal analysis relies only on L1 and L2 measurements.

    GPS IIF Got Active. The 50th Space Wing’s 2nd Space Operations Squadron formally took over command and control of the first Block IIF satellite on August 26 from the GPS Wing, and the satellite was set healthy on August 27, making 31 healthy GPS satellites on orbit.

    Advisory Board Update
    GPS World Editorial Advisory Board member Art Gower has been elected a Lockheed Martin Fellow, an honor recognizing pre-eminent technical individual contributors in the company, delivering mission success and vision by undertaking the most difficult technical challenges facing the company and its customers. Art started his career with IBM Federal Systems Division (now part of Lockheed Martin Integrated Systems and Global Solutions) in 1983, developing displays and performing navigation upload and command and control system engineering for the GPS control segment, and becoming chief engineer for the GPS control segment in 1990. He has spent the majority of his career working on GPS, GNSS, and SBAS systems.