A Case History Using the New Galileo E6-B/C Signal
By Sergei Yudanov, JAVAD GNSS
A method of decoding an unknown pseudorandom noise code uses a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation satellites.
Decoding an unknown GNSS pseudorandom noise (PRN) code can be rather easily done using a high-gain steerable dish antenna as was used, for example, in determine the BeiDou-M1 broadcast codes before they were publicly announced. The signal-to-noise ratio within one chip of the code is sufficient to determine its sign. This article describes a method of getting this information using a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation (IOV) satellites. In spite of the fact that only pilot signal decoding seems to be possible at first glance, it is shown that in practice data signals can also be decoded.
Concept
The idea is to do coherent accumulation of each chip of an unknown signal during a rather long time interval. The interval may be as long as a full satellite pass; for medium Earth orbits, this could be up to six hours. One of the receiver’s channels is configured in the same way as for signal tracking. The I and Q signal components are accumulated during one chip length in the digital signal processor, and these values are added to an array cell, referenced by chip number, by the processor. Only a limited amount of information need be known about the signal: its RF frequency; the expected chip rate; the expected total code length; and the modulation method.
The decoding of binary-phase-shift-keying (BPSK) signals (as most often used) is the subject of this article. It appears that the decoding of more complicated signals is possible too, but this should be proved. A limitation of this method (in common with that of the dish method) is the maximum total code length that can be handled: for lengths greater than one second and bitrates higher than 10,000 kilobits per second, the receiver’s resources may not be sufficient to deal with the signal.
Reconstructing the Signal’s Phase
This method requires coherency. During the full accumulation period, the phase difference between the real signal phase and the phase of the signal generated by the receiver’s channel should be much less than one cycle of the carrier frequency. Depending on the GNSS’s available signals, different approaches may be used. The simplest case is reconstruction of a third signal while two other signals on different frequencies are of known structure and can be tracked.
The main (and possibly the only significant) disturbing factor is the ionosphere. The ionospheric delay (or, more correctly, the variation of ionospheric delay) is calculated using the two known tracked signals, then the phase of the third signal, as affected by the ionosphere, is predicted.
The final formula (the calculations are trivial and are widely available in the literature) is:
where:
φ1 , f1 are the phase and frequency of the first signal in cycles and Hz, respectively
φ2 , f2 are the phase and frequency of the second signal in cycles and Hz, respectively
φ3 , f3 are the phase and frequency of the third signal in cycles and Hz, respectively.
It was confirmed that for all pass periods (elevation angles less than 10 degrees were not tested), the difference between the calculated phase and real phase was always less than one-tenth of a cycle. GPS Block IIF satellites PRN 1 and PRN 25 were used to prove this: the L1 C/A-code and L5 signals were used as the first and second signals, with the L2C signal as the third unknown.
If two known signals are not available, and the ionospheric delay cannot be precisely calculated, it is theoretically possible to obtain an estimate of the delay from one or more neighboring satellites with two signals available. Calculations and estimations should be carried out to investigate the expected precision.
The Experiment
The Galileo E6-B/C signal as currently transmitted by the IOV satellites was selected for the experiment, as its structure has not been published. The E6 signal has three components: E6-A, E6-B and E6-C. The E6-A component is part of the Galileo Public Regulated Service, while the two other components will serve the Galileo Commercial Service. The E6-B component carries a data signal, while the E6-C component is a pilot signal.
From open sources, it is known that the carrier frequency of the E6 signal is 1278.75 MHz and that the E6-B and E6-C components use BPSK modulation at 5,115 chips per millisecond with a primary code length of one millisecond. E6-B’s data rate is 1,000 bits per second and the total length of the pilot code is 100 milliseconds (a secondary code of 100 bits over 100 milliseconds is also present in the E6-C signal, which aids in signal acquisition).
A slightly modified commercial high-precision multi-GNSS receiver, with the E6 band and without the GLONASS L2 band, was used for this experiment. The receiver was connected to a conventional GNSS antenna, placed on a roof and was configured as described above. The E1 signal was used as the first signal and E5a as the second signal. The E6 code tracking (using 5,115 chip values of zero) was 100 percent guided from the E1 code tracking (the changing of the code delay in the ionosphere was ignored). The E6 phase was guided from E1 and E5a using the above equation. Two arrays for 511,500 I and Q samples were organized in firmware. The integration period was set to one chip (200 nanoseconds).
Galileo IOV satellite PRN 11 (also variously known as E11, ProtoFlight Model and GSAT0101) was used initially, and the experiment started when the satellite’s elevation angle was about 60 degrees and lasted for only about 30 minutes. Then the I and Q vectors were downloaded to a PC and analyzed.
Decoding of Pilot Signal (E6-C)
Decoding of the pilot signal is made under the assumption that any possible influence of the data signal is small because the number of ones and zeros of E6-B in each of 511,500 chips of the 100-millisecond integration interval is about the same. First, the secondary code was obtained. Figure 1 shows the correlation of the first 5,115 chips with 5,115 chips shifted by 0 to 511,500 chips. Because the initial phase of the E6 signal is unknown, two hypotheses for computing the amplitude or signal level were checked: [A] = [I] + [Q] and [A] = [I] – [Q], and the combination with the higher correlation value was selected for all further analysis.
Figure 1. Un-normalized autocorrelation of E6-C signal chips.
In Figure 1, the secondary code is highly visible: we see a sequence of 100 positive and negative correlation peaks (11100000001111 …; interpreting the negative peaks as zeros).This code is the exact complement (all bits reversed) of the published E5a pilot secondary code for this satellite. More will be said about the derived codes and their complements later. It appears that, for all of the IOV satellites, the E6-C secondary codes are the same as the E5a secondary codes.
After obtaining the secondary code, it is possible to coherently add all 100 milliseconds of the integration interval with the secondary code sign to increase the energy in each chip by 100 times. Proceeding, we now have 5,115 chips of the pilot signal — the E6-C primary code.
To understand the correctness of the procedure and to check its results, we need to confirm that there is enough signal energy in each chip. To this end, a histogram of the pilot signal chip amplitudes can be plotted (see Figure 2). We see that there is nothing in the middle of the plot. This means that all 5,115 chips are correct, and there is no chance that even one bit is wrong.
Figure 2. Histogram of pilot signal chip amplitude in arbitrary units.
But there is one effect that seems strange at first glance: instead of two peaks we have four (two near each other). We will shortly see that this phenomenon results from the influence of the E6-B data signal and it may be decoded also.
Decoding the Data Signal
The presence of four peaks in the histogram of Figure 2 was not understood initially, so a plot of all 511,500 signal code chips was made (see Figure 3).
Interestingly, each millisecond of the signal has its own distribution, and milliseconds can be found where the distribution is close to that when two signals with the same chip rate are present. In this case, there should be three peaks in the energy (signal strength) spectrum: –2E, 0, and +2E, where E is the energy of one signal (assuming the B and C signals have the same strength).
Figure 3. Plot of 511,500 signal code chip amplitudes in arbitrary units.
One such time interval (starting at millisecond 92 and ending at millisecond 97) is shown in Figure 4. The middle of the plot (milliseconds 93 to 96) shows the described behavior. Figure 5 is a histogram of signal code chip amplitude for the signal from milliseconds 93 to 96.
Figure 4. Plot of signal code chip amplitude in arbitrary units from milliseconds 93 to 96.
Then we collect all such samples (milliseconds) with the same data sign together to increase the signal level. Finally, 5,115 values are obtained. Their distribution is shown in Figure 6.
The central peak is divided into two peaks (because of the presence of the pilot signal), but a gap between the central and side peaks (unlike the case of Figure 5) is achieved. This allows us to get the correct sign of all data signal chips. Subtracting the already known pilot signal chips, we get the 5,115 chips of the data signal — the E6-B primary code. This method works when there are at least some samples (milliseconds) where the number of chips with the same data bit in the data signal is significantly more than half.
Figure 5. Histogram of signal code chip amplitude.Figure 6. Histogram of the signed sum of milliseconds chip amplitude with a noticeable presence of the data signal.
Proving the Codes
The experimentally determined E6-B and E6-C primary codes and the E6-C secondary codes for all four IOVsatellites (PRNs 11, 12, 19, and 20) were put in the receiver firmware. The receiver was then able to autonomously track the E6-B and E6-C signals of the satellites.
Initial decoding of E6-B navigation data has been performed. It appears that the data has the same preamble (the 16-bit synchronization word) as that given for the E6-B signal in the GIOVE Interface Control Document (ICD). Convolutional encoding for forward error correction is applied as described in the Galileo Open Service ICD, and 24-bit cyclic redundancy check error detection (CRC-24) is used. At the time of the analysis, all four IOV satellites transmitted the same constant navigation data message.
Plots of PRN 11 E6 signal tracking are shown in Figure 7 and in Figure 8. The determined codes may be found at env-gpsworld-integration.kinsta.cloud/galileo-E6-codes. Some of these codes may be the exact complement of the official codes since the code-determination technique has a one-half cycle carrier-phase ambiguity resulting in an initial chip value ambiguity. But from the point of view of receiver tracking, this is immaterial.
Figure 7. Signal-to-noise-density ratio of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.Figure 8. Pseudorange minus carrier phase (in units of meters) of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.
Acknowledgments
Special thanks to JAVAD GNSS’s DSP system developers. The system is flexible so it allows us to do tricks like setting the integration period to one chip, and powerful enough to be able to do required jobs within a 200-nanosecond cycle. This article was prepared for publication by Richard Langley.
Manufacturers
A JAVAD GNSS TRE-G3T-E OEM receiver, a modification of the TRE-G3T receiver, was used in the experiment, connected to a conventional JAVAD GNSS antenna. Plots of E6 code tracking of all four IOV satellites may be found on the company’s website.
Sergei Yudanov is a senior firmware developer at JAVAD GNSS, Moscow.
Off-the-Shelf Antennas for Controlled-Reception-Pattern Antenna Arrays
By Yu-Hsuan Chen, Sherman Lo, Dennis M. Akos, David S. De Lorenzo, and Per Enge
INNOVATION INSIGHTS by Richard Langley
THE ANTENNA IS A CRITICAL COMPONENT OF ANY GNSS RECEIVING EQUIPMENT. It must be carefully designed for the frequencies and structures of the signals to be acquired and tracked. Important antenna properties include polarization, frequency coverage, phase-center stability, multipath suppression, the antenna’s impact on receiver sensitivity, reception or gain pattern, and interference handling. While all of these affect an antenna’s performance, let’s just look at the last two here.
The gain pattern of an antenna is the spatial variation of the gain, or ratio of the power delivered by the antenna for a signal arriving from a particular direction compared to that delivered by a hypothetical isotropic reference antenna. Typically, for GNSS antennas, the reference antenna is also circularly polarized and the gain is then expressed in dBic units.
An antenna may have a gain pattern with a narrow central lobe or beam if it is used for communications between two fixed locations or if the antenna can be physically steered to point in the direction of a particular transmitter. GNSS signals, however, arrive from many directions simultaneously, and so most GNSS receiving antennas tend to be omni-directional in azimuth with a gain roll-off from the antenna boresight to the horizon.
While such an antenna is satisfactory for many applications, it is susceptible to accidental or deliberate interference from signals arriving from directions other than those of GNSS signals. Interference effects could be minimized if the gain pattern could be adjusted to null-out the interfering signals or to peak the gain in the directions of all legitimate signals. Such a controlled-reception-pattern antenna (CRPA) can be constructed using an array of antenna elements, each one being a patch antenna, say, with the signals from the elements combined before feeding them to the receiver. The gain pattern of the array can then be manipulated by electronically adjusting the phase relationship between the elements before the signals are combined. However, an alternative approach is to feed the signals from each element to separate banks of tracking channels in the receiver and form a beam-steering vector based on the double-difference carrier-phase measurements from pairs of elements that is subsequently used to weight the signals from the elements before they are processed to obtain a position solution. In this month’s column, we learn how commercial off-the-shelf antennas and a software-defined receiver can be used to design and test such CRPA arrays.
“Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick. To contact him with topic ideas, email him at lang @ unb.ca.
Signals from global navigation satellite systems are relatively weak and thus vulnerable to deliberate or unintentional interference. An electronically steered antenna array system provides an effective approach to mitigate interference by controlling the reception pattern and steering the system’s beams or nulls. As a result, so-called controlled-reception-pattern-antenna (CRPA) arrays have been deployed by organizations such as the U.S. Department of Defense, which seeks high levels of interference rejection.
Our efforts have focused on developing a commercially viable CRPA system using commercial off-the-shelf (COTS) components to support the needs of Federal Aviation Administration (FAA) alternative position navigation and timing (APNT) efforts. In 2010, we implemented a seven-element, two-bit-resolution, single-beam and real-time CRPA software receiver. In 2011, the receiver was upgraded to support all-in-view, 16-bit-resolution with four elements.
Even though we can implement these CRPA software receivers in real time, the performance of anti-interference is highly dependent on the antenna array layout and characteristics of the antenna elements. Our beamforming approach allows us to use several COTS antennas as an array rather than a custom-designed and fully calibrated antenna. The use of COTS antennas is important, as the goal of our effort is to develop a CRPA for commercial endeavors — specifically for robust timing for the national airspace. Hence, it is important to study the geometry layout of the individual antennas of the array to assess the layouts and to determine how antenna performance affects the array’s use.
In our work, we have developed a procedure for calculating the electrical layouts of an antenna array by differential carrier-phase positioning. When compared to the physical layout, the results of electrical layouts can be used to determine the mutual coupling effect of each combination. Using the electrical layout, the resultant gain patterns can be calculated and used to see the beamwidth and the side-lobe issue. This is important as these factors have significant effects on anti-interference performance. This study focuses on understanding the performance effects of geometry and developing a method for describing the best geometry.
We adopted three models of COTS antenna and two possible layouts for a four-element array. Then, signal collection hardware consisting of four Universal Software Radio Peripheral (USRP) software-defined radios and one host personal computer was assembled to collect array data sets for each layout/antenna combination. Our developed CRPA software receiver was used to process all data sets and output carrier-phase measurements.
In this article, we will present the pattern analysis for the two selected layouts and describe how we collected the experimental data. We’ll then show the results of calculating the electrical spacing for the layouts are compare them to the physical layouts. Lastly, we’ll show the resulting patterns, discuss the antenna mutual coupling effects, and give our conclusions.
Antenna Array Pattern Analysis
Pattern is defined as the directional strength of a radio-frequency signal viewed from the antenna. The pattern of an antenna array is the product of the isotropic array factor and the isolated element pattern. We assume that the pattern of each element is identical and only consider the isotropic array factor. FIGURE 1 shows the coordination of an antenna array. The first element is set as a reference position. The x-axis is the east direction, the y-axis is the north direction, and the z-axis is the up direction. The baseline vector of the ith antenna is given by and is the unit vector to the satellite.
Figure 1. Antenna array geometry and direction of satellite. Array elements are identified as E#1, E#2, E#3, and E#4.
The isotropic array factor is given by
(1)
where λ is wavelength, and Ai is a complex constant. Currently, we only implement a four-element-array CRPA software receiver in real time. Hence, we analyze two kinds of layout of half-wavelength four-element arrays: a symmetrical Y array and a square array. Each antenna is separated from its nearest neighbor by a half wavelength. FIGURE 2 shows photos of the two layouts. FIGURE 3 shows the physical layouts.
Figure 2. Photos of antenna arrays (left: Y array; right: square array).Figure 3A. Physical layout of antenna arrays (Y array).Figure 3B. Physical layout of antenna arrays (square array).
The antenna patterns towards an elevation angle of 90 degrees, computed using equation 1 and the design layouts, are shown in FIGURE 4. One of the key characteristics of a pattern is the beamwidth, which is defined as the angle with 3-dB loss. FIGURE 5 shows the patterns in elevation angle where the beamwidth of the Y layout is 74 degrees and 86 degrees for the square layout. A narrow beamwidth will benefit anti-interference performance particularly if the interference is close to the direction of a target satellite.
Figure 4. Patterns of antenna arrays (left: Y array; right: square array).Figure 5. Pattern beamwidths of Y and square arrays (3 dB beamwidth shown).
Specifications of COTS Antennas
Typically, the COTS antenna selection is determined by high gain and great out-of-band rejection. TABLE 1 shows the specifications of the three antenna models used in this article. These antennas are all patch antennas. The antennas are equipped with surface-acoustic-wave filters for rejecting out-of-band signals. A three-stage low noise amplifier with over 30 dB gain is also embedded in each antenna.
Table 1. Specifications of COTS antennas used.
Signal Collection Hardware and Experimental Setup
The hardware used to collect the antenna array datasets is shown in FIGURE 6 with block-diagram representation in FIGURE 7. The hardware includes a four-element antenna array, four USRP2 software radio systems and one host computer. The signal received from the COTS antenna passes to a USRP2 board equipped with a 800–2300 MHz DBSRX2 programmable mixing and down-conversion daughterboard. The individual USRP2 boards are synchronized by a 10-MHz external common clock generator and a pulse-per-second (PPS) signal. The USRP2s are controlled by the host computer running the Ubuntu distribution of Linux. The open-source GNU Radio software-defined radio block is used to configure USRP2s and collect datasets. All USRP2s are configured to collect the L1 (1575.42 MHz) signal. The signals are converted to near zero intermediate frequency (IF) and digitized to 14-bit complex outputs (I and Q).
Figure 6. Photo of the signal collection hardware.Figure 7. Block diagram of the signal collection hardware.
The sampling rate is set as 4 MHz. The host computer uses two solid state drives for storing data sets. For our study, a 64-megabytes per second data transfer rate is needed. The fast solid state drives are especially useful when using high bandwidth signals such as L5, which will require an even higher data streaming rate (80 megabytes per second per channel).
To compare the physical and electrical layouts of the antenna arrays, we set up the signal collection hardware to record six data sets for the two layouts and the three antenna models as shown in TABLE 2. All of the data sets were five minutes long to obtain enough carrier-phase measurements for positioning.
Table 2. Experimental setups.
Logging Carrier-Phase Measurements
To calculate the precise spacing between the antenna elements, hundreds of seconds of carrier-phase measurements from each element are needed. The collected data sets were provided by our in-house-developed CRPA software receiver. The receiver was developed using Visual Studio under Windows. Most of source code is programmed using C++. Assembly language is used to program the functions with high computational complexity such as correlation operations. The software architecture of the receiver is depicted in FIGURE 8. This architecture exploits four sets of 12 tracking channels in parallel to process each IF signal from an antenna element. Each channel is dedicated to tracking the signal of a single satellite. The tracking channels output carrier-phase measurements to build the steering vectors for each satellite. The Minimum Variance Distortionless Response (MVDR) algorithm was adopted for adaptively calculating the weights for beamforming. Here, there are 12 weight sets, one for each satellite in a tracking channel, for the desired directions of satellites.
Figure 8. Block diagram of the software architecture.
Using the pre-correlation beamforming approach, the weights are multiplied with IF data and summed over all elements to form 12 composite signals. These signals are then processed by composite tracking channels. Finally, positioning is performed if pseudoranges and navigation messages are obtained from these channels. FIGURE 9 is the graphical user interface (GUI) of the CRPA software receiver. It consists of the channel status of all channels, carrier-phase differences, positioning results, an east-north (EN) plot, a sky plot, a carrier-to-noise-density (C/N0) plot and the gain patterns of the array for each tracked satellite. In the figure, the CRPA software receiver is tracking 10 satellites and its positioning history is shown in the EN plot. The beamforming channels have about 6 dB more gain in C/N0 than the channels of a single element. In each pattern, the direction with highest gain corresponds to the direction of the satellite. While the CRPA software receiver is running, the carrier-phase measurements of all elements and the azimuth and elevation angle of the satellites are logged every 100 milliseconds. Each data set in Table 2 was processed by the software receiver to log the data.
Figure 9. Screenshot of the controlled-reception-pattern-antenna software-receiver graphical user interface.
Electrical Layout of Antenna Array – Procedure
The procedure of calculating the electrical layout of an antenna array is depicted in FIGURE 10. The single-difference integrated carrier phase (ICP) between the signals of an element, i, and a reference element, j, is represented as:
(2)
where rkijis differential range toward the kth satellite between the ith and jth antenna elements (a function of the baseline vector between the ith and jth elements), δLij is the cable-length difference between the ith and jth antenna elements, Nkijis the integer associated with Φkij, εkijand is the phase error. The double-difference ICP between the kth satellite and reference satellite l is represented as: (3)
The cable-length difference term is subtracted in the double difference. Since the distances between the antenna elements are close to one wavelength, equation (3) can be written as: (4)
where is the unit vector to satellite k,pij is the baseline vector between the ith and jth elements. By combining all the double-difference measurements of the ijth pair of elements, the observations equation can be represented as: (5)
From the positioning results of composite channels, the azimuth and elevation angle of satellites are used to manipulate matrix G. To solve equation (5), the LAMBDA method was adopted to give the integer vector N. Then, pij is solved by substituting Ninto equation (5). Finally, the cable-length differences are obtained by substituting the solutions of N and pij into equation (2).
This approach averages the array pattern across all satellite measurements observed during the calibration period.
Figure 10. Procedure for calculating antenna-array electrical spacing.
Electrical Layout of Antenna Array – Results
Using the procedure in the previous section, all electrical layouts of the antenna array were calculated and are shown in FIGURES 11 and 12. We aligned the vectors from element #1 to element #2 for all layouts. TABLE 3 lists the total differences between the physical and electrical layouts. For the same model of antenna, the Y layout has less difference than the square layout. And, in terms of antenna model, antenna #1 has the least difference for both Y and square layouts. We could conclude that the mutual coupling effect of the Y layout is less than that of the square layout, and that antenna #1 has the smallest mutual coupling effect among all three models of antenna for these particular elements and observations utilized.
Figure 11. Results of electrical layout using three models of antenna compared to the physical layout for the Y array.Figure 12. Results of electrical layout using three models of antenna compared to physical layout for the square array.Table 3. Total differences between physical and electrical layouts.
To compare the patterns of all calculated electrical layouts, we selected two specific directions: an elevation angle of 90 degrees and a target satellite, WAAS GEO PRN138, which was available for all data sets. The results are shown in FIGURES 13 and 14, respectively. From Figure 13, the beamwidth of the Y layout is narrower than that of the square layout for all antenna models. When compared to Figure 5, this result confirms the validity of our analysis approach. But, in Figure 14, a strong sidelobe appears at azimuth -60º in the pattern of Y layout for antenna #2. If there is some interference located in this direction, the anti-interference performance of the array will be limited. This is due to a high mutual coupling effect of antenna #2 and only can be seen after calculating the electrical layout.
Figure 13. Patterns of three models of antenna and two layouts toward an elevation angle of 90 degrees.Figure 14. Patterns of three models of antenna and two layouts toward the WAAS GEO satellite PRN138.
Conclusions
The results of our electrical layout experiment show that the Y layout has a smaller difference with respect to the physical layout than the square layout. That implies that the elements of the Y layout have less mutual coupling. For the antenna selection, arrays based on antenna model #1 showed the least difference between electrical and physical layout. And its pattern does not have a high grating lobe in a direction other than to the target satellite.
The hardware and methods used in this article can serve as a testing tool for any antenna array. Specifically, our methodology, which can be used to collect data, compare physical and electrical layouts, and assess resultant antenna gain patterns, allows us to compare the performances of different options and select the best antenna and layout combination. Results can be used to model mutual coupling and the overall effect of layout and antenna type on array gain pattern and overall CRPA capabilities. This procedure is especially important when using COTS antennas to assemble an antenna array and as we increase the number of antenna elements and the geometry possibilities of the array.
Acknowledgments
The authors gratefully acknowledge the work of Dr. Jiwon Seo in building the signal collection hardware. The authors also gratefully acknowledge the Federal Aviation Administration Cooperative Research and Development Agreement 08-G-007 for supporting this research. This article is based on the paper “A Study of Geometry and Commercial Off-The-Shelf (COTS) Antennas for Controlled Reception Pattern Antenna (CRPA) Arrays” presented at ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, held in Nashville, Tennessee, September 17–21, 2012.
Manufacturers
The antennas used to construct the arrays are Wi-Sys Communications Inc., now PCTEL, Inc. models WS3978 and WS3997 and PCTEL, Inc. model 3978D-HR. The equipment used to collect data sets includes Ettus Research LLC model USRP2 software-defined radios and associated DBSRX2 daughterboards.
Yu-Hsuan Chen is a postdoctoral scholar in the GNSS Research Laboratory at Stanford University, Stanford, California.
Sherman Lo is a senior research engineer at the Stanford GNSS Research Laboratory.
Dennis M. Akos is an associate professor with the Aerospace Engineering Science Department in the University of Colorado at Boulder with visiting appointments at Luleå Technical University, Sweden, and Stanford University.
David S. De Lorenzo is a principal research engineer at Polaris Wireless, Mountain View, California, and a consulting research associate to the Stanford GNSS Research Laboratory.
Per Enge is a professor of aeronautics and astronautics at Stanford University, where he is the Kleiner-Perkins Professor in the School of Engineering. He directs the GNSS Research Laboratory.
FURTHER READING
• Authors’ Publications
“A Study of Geometry and Commercial Off-The-Shelf (COTS) Antennas for Controlled Reception Pattern Antenna (CRPA) Arrays” by Y.-H. Chen in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of The Institute of Navigation, Nashville, Tennessee, September 17–21, 2012, pp. 907–914 (ION Student Paper Award winner).
“A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors” by J. Seo, Y.-H. Chen, D.S. De Lorenzo, S. Lo, P. Enge, D. Akos, and J. Lee in Sensors, Vol. 11, No. 9, 2011, pp. 8966–8991, doi: 10.3390/s110908966.
“Real-Time Software Receiver for GPS Controlled Reception Pattern Array Processing” by Y.-H. Chen, D.S. De Lorenzo, J. Seo, S. Lo, J.-C. Juang, P. Enge, and D.M. Akos in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 1932–1941.
“A GNSS Software Receiver Approach for the Processing of Intermittent Data” by Y.-H. Chen and J.-C. Juang in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of The Institute of Navigation, Fort Worth, Texas, September 25–28, 2007, pp. 2772–2777.
• Controlled-Reception-Pattern Antenna Arrays
“Anti-Jam Protection by Antenna: Conception, Realization, Evaluation of a Seven-Element GNSS CRPA” by F. Leveau, S. Boucher, E. Goron, and H. Lattard in GPS World, Vol. 24, No. 2, February 2013, pp. 30–33.
“Development of Robust Safety-of-Life Navigation Receivers” by M.V.T. Heckler, M. Cuntz, A. Konovaltsev, L.A. Greda, A. Dreher, and M. Meurer in IEEE Transactions on Microwave Theory and Techniques, Vol. 59, No. 4, April 2011, pp. 998–1005, doi: 10.1109/TMTT.2010.2103090.
Phased Array Antennas, 2nd Edition, by R. C. Hansen, published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2009.
• Antenna Principles
“Selecting the Right GNSS Antenna” by G. Ryley in GPS World, Vol. 24, No. 2, February 2013, pp. 40–41 (in PDF of 2013 Antenna Survey.)
“A USRP2-based Reconfigurable Multi-constellation Multi-frequency GNSS Software Receiver Front End” by S. Peng and Y. Morton in GPS Solutions, Vol. 17, No. 1, January 2013, pp. 89-102.
Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory by I.G. Petrovski and T. Tsujii with foreword by R.B. Langley, published by Cambridge University Press, Cambridge, U.K., 2012.
“A Real-Time Software Receiver for the GPS and Galileo L1 Signals” by B.M. Ledvina, M.L. Psiaki, T.E. Humphreys, S.P. Powell, and P.M. Kintner, Jr. in Proceedings of ION GNSS 2006, the 19th International Technical Meeting of The Institute of Navigation, Fort Worth, Texas, September 26–29, 2006, pp. 2321–2333.
The ADIS16488 is a Micro Electromechanical System (MEMS) Inertial Measurement Unit (IMU) from Analog Devices. It features low noise gyros and accelerometers in a rugged, environmentally sealed enclosure. The ADIS16488 enables precision measurements for applications that require low cost, high performance, and rugged durability.
At 71 x 46 x 11 millimeters, the OEM-ADIS-16488 features low noise gyros and accelerometers in a small, lightweight form factor. The custom device is designed to be paired with a NovAtel OEM6 receiver card to provide continuously available position, velocity, and attitude (roll, pitch, yaw).
SPAN tightly couples NovAtel’s precise GNSS technology with highly accurate inertial measurement technology to provide robust, stable, and continuous 3D navigation. Tight coupling of the two technologies enables continuous robust positioning in difficult environments.
The Jupiter SE880 by Telit Wireless Solutions is a small, ultra sensitive 48-channel GPS module that features an advanced 3D technology that improves time to first fix and brings indoor location fix to reality with cold start and tracking sensitivity down to -148 dBm and -165 dBm. The turnkey solution enables the design of ultra-compact applications, requiring minimal external BOM and a footprint of less than 40 millimeters compared to conventional PCB technology.
The Jupiter SE880 is designed for high-volume GNSS ultra-compact mobile / tracking devices as well as advanced consumer devices such as sport watches and cameras.
Accurate, Reliable, Available, Continuous Positioning for Cooperative Driving
By Scott Stephenson, Xiaolin Meng, Terry Moore, Anthony Baxendale, and Tim Edwards
Adoption of network real-time kinematic GNSS positioning can lead to major improvements in vehicle localization, although implementation must overcome some real-world challenges. This article assesses the extent of GNSS signal outage in a motorway environment. The average total GNSS outage period and the average time to resolve ambiguity for the network RTK solution can help assess complimentary sensors for a ubiquitous positioning system.
Real-time vehicle localization is one of three key enabling technologies for the concepts of vehicle-to-vehicle and vehicle-to-infrastructure (V2V and V2I, collectively termed V2X, see opening graphic), a classification of intelligent transport systems (ITS). The further enabling technologies are ad-hoc dynamic networking of agents, and accurate dynamic local traffic maps. Jointly, these require that positioning be accurate, reliable, available, and continuous.
A natural evolution in road transport, V2X promises to deliver the next major safety breakthrough. The concept moves away from vehicles making individual decisions about road safety, as in advanced driver assistance systems, and towards a cooperative driving approach that shifts the emphasis from collision protection to collision prevention. The U.S. National Highway Traffic Safety Administration estimates that V2X technology can avoid or minimize up to 80 percent of collisions of unimpaired drivers, and that even a small number of deployed vehicles will provide tangible safety benefits.
Network RTK GNSS positioning, like V2X applications, requires a communication system; and by its nature V2X has a positioning solution requirement. Thus it is envisioned that network RTK will play an essential role in the implementation of V2X systems. The consensus between car manufacturers and research organizations is that the future of V2X communication lies with Dedicated Short Range Communication (DSRC) devices, and a large pilot study is currently under way. However, in the short term many V2X applications could be achieved using existing technology, such as cellular communication, offering a legacy solution, and initiating early uptake of V2X applications.
Previous research by the Nottingham Geospatial Institute (NGI) at the University of Nottingham showed that network RTK positioning can provide a high-accuracy positioning solution during real-world trials, but also revealed two areas of concern: the loss of the fixed-integer ambiguity during satellite line-of-sight outages; and the fragility of the data communications service that delivers the real-time correction information. During road tests, a fixed-ambiguity network RTK solution was available for less than 50 percent of the time on United Kingdom (UK) roads.
Network RTK Vehicle Positioning
Figure 1. OS Net reference station network in Britain, owned by Ordnance Survey.
Networks of continuously operating reference stations (CORS) extend across Europe, North America, Australia, and East Asia. Networks vary in size from five or six reference stations for agriculture to systems of hundreds of CORSs providing national or regional service. Figure 1 shows the location of the OS Net CORS run by Ordnance Survey in Great Britain.
Figure 2 shows the main advantage of network RTK as compared to traditional RTK. The individual reference stations on the left suffer from the spatial decorrelation of errors as distance between reference and rover receivers increases. Adequate vehicle positioning would require individually operating reference stations to be placed approximately 20–30 kilometers apart. However, a CORS network can be used to develop a model of differential corrections, as shown at right, from which a rover receiver can interpret RTK correction information and use this during the computation of its position. The geometry of a CORS network allows two adjacent reference stations to be located up to 80–100 kilometers apart without degrading the accuracy, although in practice most systems tend to locate them closer together than this. This is essentially a reduction from 30 reference stations per 10,000km² for conventional RTK, to 5–10 reference stations for network RTK, delivering high-precision services to virtually unlimited users.
Figure 2. The improved navigation performance from RTK (left) to network RTK (right).
It is expected that the CORS networks will become a critical part of a country’s spatial infrastructure, and countries like the UK are leading the way. This makes network RTK one of the most promising positioning technologies for road vehicles and ITS applications.
As shown in previous research, network RTK can deliver a vehicle positioning accuracy of better than 5 centimeters, and in real-world tests this level of accuracy had an availability of 41–45 percent, depending on the environment. It was also found that the correction information was available via the GSM network for more than 80 percent of the time. In these same tests, the total time without any GNSS position solution (network RTK, DGNSS, or stand-alone) was up to 16 percent in a motorway environment. Network RTK was able to provide lane-level positioning accuracy, but the sensitivity of the technique to GNSS signal loss and coverage of the communication network had a significant effect on availability. GNSS outages could be caused simply by passing under a road bridge, and the network RTK solution would be lost, although there would continue to be a DGNSS solution for a short period. Finding effective solutions to these current barriers, which prevent wide adoption of network RTK, is a key enabling step for ITS.
Accuracy Assessment
In much more controlled tests to assess the accuracy of network RTK on a dynamic vehicle, the network RTK GNSS receiver was compared to an inertial navigation system (INS). This test was carried out using the NGI roof laboratory, which houses a 120-meter rail track running an electric locomotive.
Both the network RTK receiver and the INS used the same antenna, fed separately through a signal splitter. The network RTK solution was recorded in real time onto an SD card in NMEA GGA format. The INS data was recorded and post-processed in a tightly coupled solution using a continuously operating dual-frequency GNSS receiver base station located inside the rail track circuit. There were no recorded GNSS outages as there is a clear-sky view from the roof laboratory.
The antenna point was also tracked using a total station, recording observations at 10 Hz stamped with GPS time. Although the accuracy of the tracking mode of the total station is not high enough to assess the accuracy of the network RTK solution (because of time synchronization issues), it ensures that any gross errors in GNSS observations that could affect both the network RTK and INS solutions did not occur.
The results in Table 1 show that the network RTK solution consistently performs to a high accuracy, giving a low standard deviation from the mean in all directions. Listed are three laps of the rail circuit recorded at different times. There are a small number of epochs that encounter large differences of more than 200 millimeters, such as during laps 2 and 3, although these appear to be very short-term anomalies, possibly caused by dynamic GNSS signal multipath or delays and message loss in the communication system.
Table 1. Comparison of the tightly coupled (GPS+IMU) solution with the N-RTK solution.
The worst absolute accuracy is shown during lap 3, although even in this case, with a mean of 21 millimeters and 99 percent of the observations lying within 15 millimeters, this solution still delivers a solution within 36 millimeters of the ground truth. 50 percent of the network RTK observations are within 1 millimeter of the mean difference between the two solutions, showing remarkable consistency and precision.
Challenge: Comm Signal Strength
A fundamental aspect of network RTK is the delivery of reference station data used in the processing of the receiver’s position. Although there are various methods used to deliver this data, the most secure and reliable method involves transmitting raw reference station observations, so that the receiver may perform the calculation of the position with all possible data. This provides the highest integrity. The vulnerability here is not the algorithmic method used to transmit the data, but the communication system, in three ways:
There is no connection between reference and rover receivers.
There is data loss from the connection.
There is an unacceptable delay in the transmission of the data.
Lack of Coverage. The preferable communication system is to use mobile Internet over the GSM/GPRS cell network, which is already well established. The major network operators claim over 99 percent coverage of the population in the UK, but this does not take into account physical and local conditions such as land and building obstructions, atmospheric conditions, and inter-ference from vegetation and other
radio signals.
A 2011 BBC survey in the UK found that when users had a cell-phone data connection it was 3G for 75 percent of the time (2G otherwise), but significant “notspots” include major rail and road networks. An ongoing study by OpenSignalMaps has found that a 3G service is only available 58 percent of the time. A 2011 government report detailed the extent of 2G and 3G services, shown in Figure 3. Areas with poor data communication coverage (below 50 percent) pose a significant problem for network RTK in vehicles.
Figure 3. 2G (left) and 3G (right) coverage by geographic area in the UK: green, >90 percent; yellow, 70–90 percent; blue, 50–70 percent; purple, 25–50 percent; red,
Data Loss. Continuity tests show that when using GSM/GPRS mobile communications to transfer the network RTK corrections, the availability was approximately 88 percent, and the connection could be lost after a few hours of continuous use. This can be caused either by SIM cards that use dynamic IP addresses, creating interruptions when renewing the addresses, or where voice data was prioritized on the network. Other research has shown that a typical mobile Internet connection (a combination of wired public Internet and GPRS) suffers from approximately 20 percent data loss.
Message Delay. A network RTK receiver imposes a transmission time limit on the correction messages that are used to fix the common integer ambiguity (in this case, the Leica GS10 limit is 10 seconds), although messages younger than 60 seconds can be used to give an accurate DGNSS solution. Messages older than 60 seconds result in the receiver only being able to output a standalone position, by which time the accuracy will decay beyond vehicle positioning requirements. Earlier research found the typical mobile Internet connection suffers from an average delay of 0.85 seconds.
Challenge: GNSS Outages
The majority of the transport infrastructure is outside and has a clear view of the sky, particularly away from heavily urbanised areas. However, the receiver gets no warning of impending signal obstruction, so that even momentary obstructions such as an overhead gantry on the motorway can cause significant loss of positioning accuracy, and often causes a receiver to output no solution at all, as shown in Figure 4. Here the vehicle is traveling in a northern direction in lane 1 of the left-hand carriageway and passes underneath a series of bridges at a motorway junction. This causes both GNSS outages and deteriorated positional accuracy, so much so that the vehicle is positioned in the southern carriageway (note that the underlying map image is of unknown accuracy).
Figure 4. The typical effect of overhead obstructions on vehicle GNSS positioning.
GNSS outages can occur in several ways: the obstruction of the GNSS signals can lead to a loss of signal lock; a momentary obstruction or partial obstruction can cause cycle slips to occur (during carrier-phase positioning); if the visible satellites at the rover receiver are not the same as at the reference receiver, then the ambiguity cannot be resolved; there may be intentional or unintentional signal jamming or interference; and if the receiver assessed the integrity or accuracy to be poor then it may not provide a solution.
NGI test vehicle.
Experiment Set-Up
The test vehicle was equipped with a GNSS receiver and antenna, receiving real-time corrections using a GSM/GPRS connection. The signal strength was measured simultaneously using the Android application RF Signal Tracker on an Android-based mobile phone.
The data recorded includes: GNSS raw data, RINEX format; network RTK real time output, NMEA format; GSM signal strength, CSV format. As the experiments were not intended for the analysis of the accuracy of the GNSS receiver, there was no need to utilize the ground truth system onboard the NGI test vehicle.
RF SIGNAL TRACKER. Android application and mobile phone used to record the GSM signal strength (left), and GNSS receiver (right).
Test Environment. Two test scenarios were chosen for the experiments. To assess the GNSS signal outages, the test vehicle was driven along the M1 motorway, a length of approximately 100 kilometers. The M1 is a major road transport artery linking London in the South to Leeds in the North of England, typically with three or four lanes in each direction. This route passes under 214 overhead obstructions (northbound and southbound directions), of known classification (gantry, footbridge, road bridge). This scenario was chosen as the environment is quite rigid, allowing repeatable tests, and it is the area in which future ITS technology is most likely to be adopted first.
To test the variation of GSM signal strength in real-world conditions, a small circuit was chosen close to the Nottingham Geospatial Institute (shown in Figure 5), which incorporates a variety of environments from open sky to bridge underpasses, and dense tree coverage. Using a repeatable path allows the identification of issues that are attributable to problems with the communications link as opposed to other issues (such as hardware problems and GNSS signal outages), and despite the short distance, the loop also provides a wide range of GSM signal strengths. During the experiments to follow, the data was measured during three consecutive laps of the circuit.
Experiment Results
GSM Signal Strength. The variation in color along the NGI test route is an indication of the RSSI (Received Signal Strength Indicator). In this area, the RSSI varies between –50 dBm and –105 dBm, which are the typical maximum and minimum strengths of a cellular network. This is despite the assessment from the network provider that this entire area delivers high-speed Internet and email. Figure 5 also shows the subjective rating and expected performance of the RSSI.
Figure 5. The GSM signal strength around the NGI circuit in Nottingham, with the subjective RSSI ratings.Table 2. The spread of RSSI observations recorded during the trials around the NGI circuit.
Table 2 details the RSSI observations measured during the signal strength trials around the NGI circuit. The range of values shows the typical maximum and minimum RSSI values experienced by a cell-phone user (other than no signal being received). The signal strength is recorded every 5 meters, in order to achieve a good geographic spread across the area (as opposed to biasing the results with observations recorded whilst the vehicle is stationary). The RSSI observations do not correspond to a typical Gaussian distribution, suggesting that there are external influences on the strength of the signal and the handover between one cell tower and the next.
Figure 6 shows an increase in the age of correction (AoC) of the messages following a drop in signal strength (RSSI) to approximately –100 dBm. This is visible from the peaks in the age of correction message to over 8 seconds. The graph shows three laps of the NGI circuit, noticeable by the repeated pattern of signal strength. The increase in the AoC occurs at approximately the same geographic location on each lap — an area in the northwest of the circuit that suffers from weak signal strength, as seen in Figure 5. The received signal strength is the sum of the direct and indirect (or reflected) waves, varying with distance between a series of maximum and minimum values. On a moving vehicle, the RSSI will vary with time as it moves between these maximum and minimum values, and is especially complicated in urban areas where there may be no direct waves at all, and waves are propagated by a series of reflections. A moving receiver also suffers from a Doppler shift in the received signal’s frequency.
Figure 6. The effect of GSM RSSI on the age of correction messages.
During network RTK positioning, the receiver considers messages older than 10 seconds unusable for a fixed network RTK solution, although messages younger than 60 seconds can be used to give an accurate DGNSS solution. This scenario has a brief occasion during the loop in which loss of the network RTK solution is attributable to weak GSM signal strength.
A close inspection of Figure 6 highlights a slight delay between the drop in RSSI to –100 dBm and the increase in the AoC. This delay needs further analysis, but is assumed to relate to the slower update rate of the ionospheric and tropospheric corrections (10 seconds and 60 seconds respectively). There are also periods of increased AoC that are uncorrelated with a drop in RSSI, for which there is no clear explanation, although none of these occasions results in a loss of the fixed ambiguity network RTK solution.
Eighty cell handovers were recorded during the trials, which is higher than average as this area is liable to carry a large volume of cellular traffic (there is a university, a large hospital, and major roads, as well as general housing and business properties). The cell handovers showed an average improvement of +1.2 dBm from just before the handover until just after. The maximum improvement is +22 dBM, although there are occasions when the RSSI gets worse, the biggest fall in received signal strength being –12 dBM. Figure 7 displays the frequency distribution of the change in RSSI during a cell handover. The resolution of the RSSI measurements is 2 dBm.
Figure 7. Frequency histogram of the RSSI change during a cell handover (2 dBm bins).
Cell handovers occur at a range of RSSI, not just low signal strength. This suggests that cell handovers are managed by the network operator in a way that does not disrupt the data connection. There appears to be no correlation between a cell handover and a problem with the correction message delivery.
Although this part of the experiment was not a test of receiver performance, during the NGI circuit trial 63.1 percent of the receiver observations were network RTK fixed, and 33.0 percent of the observations were DGNSS observations. Therefore, 3.9 percent of the possible epochs had no observations, partly due to passing under bridges. The largest GNSS outage during circuit trials was 4.85 seconds. These values show an improvement over previous research, particularly as this is considered a difficult GNSS positioning environment.
GNSS Outages. During the GNSS outages tests, the vehicle traveled at a constant speed of 60 mph, mostly in lane 1 of the motorway. Table 3 shows statistical breakdown of the GNSS outages and the resulting reacquisition of the fixed ambiguity in network RTK positioning.
Table 3. Statistical breakdown of GNSS outages caused by overhead objects.
The longest total GNSS outage caused by an overhead obstruction was 4.65 seconds, when passing under a road bridge. At 60 mph this translates into a distance of almost 130 meters without any GNSS solution, which is much further than the width of the overhead object. Once the GNSS signal is reacquired, there is a short period during which the fixed integer ambiguity is resolved, in order to achieve the centimeter-level accuracy. The longest duration between start of a GNSS outage and reacquisition of the fixed ambiguity for the network RTK solution is 52.10 seconds, or 1,450 meters. Although during this period, a DGNSS solution is available as soon as the satellites are reacquired.
Discussion
Nationwide adoption of cellular Internet services by cell phone users has provided a useful communication system for positioning systems. But network providers do not guarantee the type of communication service demanded by advanced ITS and V2X applications. The quality of service is too easily disrupted by passing into an area with weak signal strength, or when many users congest the bandwidth.
Future generations of cell networks, such as 4G, will significantly increase the available bandwidth and increase download speeds, but there is an unknown increase in the demand on the system from non-critical cell-phone users. The issues in the existing system can be minimized slightly through improvements at the user end, such as using stronger gain antennae or accessing multiple networks with different SIM registrations. The nature of cell networks also leads to a decrease in signal strength occurring prior to the cell handover, which can cause delays in the message delivery, so the management of this process could be improved. Future testing of the GSM network can be carried out at the new innovITS ADVANCE test facility at MIRA in the UK, where the private network can be controlled and manipulated as desired.
An alternative communication method, that has the same wide area coverage of a cell network, is satellite communication. In tests, observation of static positions showed 98 percent of messages were received correctly at a latency of less than 10s. This compares with the High-Speed Download Packet Access (HSDPA) cell network figures of 99.8 percent and 1.2s. When in a kinematic mode, the satellite communications fared less well. Testing three separate satellite communication systems, problems were encountered with reacquisition, long latency, and static initialization. At best, 70 percent of correct messages were received, with a latency of 4.2s, although often over 20s.
Digital Audio Broadcasting (DAB) is capable of being used as a future communication method for network RTK positioning. Compared to traditional VHF and UHF radio communication, it uses the frequency more efficiently and is more robust to degradation.
The design of the GNSS receiver used in testing is aimed at delivering a very reliable and highly accurate solution. It was not intended for use on vehicles and in dynamic environments. The receiver deals well with multipath, rejecting low-strength GNSS signals, allowing the resolution of the integer ambiguity. However, this means that in city environments it may provide fewer solutions than a modern smartphone, albeit with a much higher accuracy when it does. Recent research shows it is possible to increase the speed of ambiguity resolution, and customize integrity controls, making the resolution process close to instantaneous in certain circumstances.
Conclusions
As cellular communications networks evolve in the UK and other countries, the performance of the network RTK receiver also improves. We found that once the RSSI drops to approximately –100dBm, the correction messages suffer from either message loss or message delay that causes the receiver to underperform. The performance of the communication link during a cell tower handover has shown that there is no deterioration in the performance linked to the handover, although cell tower handovers generally occur at the limits of a cell tower’s coverage, and hence at low signal strengths.
The resolution of the fixed integer ambiguity is crucial for the high-accuracy solution available with a network RTK receiver. The resolution is relatively fast, typically within two minutes from a cold start, or fewer than 20 seconds from a hot start. During tests on the M1 motorway, passing under an overhead obstruction caused a maximum total GNSS outage of 4.65 seconds, and a maximum time until the ambiguity was resolved of 52.10 seconds. On average, the GNSS outage was 1.14 seconds with an average re-fix time of 13.13 seconds. Until the ambiguity is resolved, the receiver can continue with a DGNSS solution delivering lane-level accuracy.
Manufacturers
NGI’s inertial nav system is an Applanix POS/RS, which consists of a NovAtel OEM4 dual-frequency GPS receiver combined with a navigation-grade Honeywell consumer IMU. The network RTK position was provided by a Leica GS10 receiver and Leica SmartNet correction service over the Vodafone network. Both receivers used a Leica AS10 antenna.
Scott Stephenson is a Ph.D. student at the Nottingham Geospatial Institute within the University of Nottingham.
Xiaolin Meng is associate professor, theme leader for positioning and navigation technologies, and MSc course director for GNSST and PNT at the Nottingham Geospatial Institute of the University of Nottingham.
Terry Moore is director of the Nottingham Geospatial Institute (NGI) at the University of Nottingham, where he is the professor of satellite navigation and also an associate dean within the Faculty of Engineering.
Anthony Baxendale is head of Advanced Technologies & Research at MIRA Ltd.
Tim Edwards is the lead engineer of the Intelligent Transportation Systems (ITS) research group at MIRA Ltd
BlackBerry (formerly RIM) has announced the release of its highly anticipated BlackBerry 10 operating system, as well as its first BlackBerry 10 smartphones, the Z10 and Q10, which come outfitted with assisted, autonomous and simultaneous GPS and with a Blackberry Maps application preloaded. One reviewer, however, has said the Blackberry Maps application is “worse than Apple Maps.”
Issues listed include lack of street view or satellite view, lack of public transit directions, and no way to look up news and reviews of venues based on Yelp, Zagat, or a similar service. Another reviewer said, “Although full details of BB10’s map app have yet to be revealed, early reports are mixed. Although it now includes turn-by-turn directions, those who have had an opportunity to try it describe it as basic and underwhelming.”
A TIME magazine review said, “The consensus among BlackBerry Z10 reviews is that its Maps app is subpar. The Verge complained about inaccurate data, and said the software couldn’t reliably find local businesses. CNet bemoaned a lack of features, such as walking directions, transit maps and street views. Apparently the software doesn’t even let you jump into the Maps app by tapping on an address or map in the web browser. That’s just basic stuff. At least the Maps app includes voice-guided turn-by-turn directions.”
The Z10 is equipped with a 4.2-inch touchscreen and the Q10 has a 3.2-inch handset with a Blackberry’s physical keyboard. Besides GPS, the phones have 4G connectivity, Bluetooth Smart and NFC connectivity. The use of Bluetooth Smart in the BlackBerry 10 platform will open it up developers who will be able to take advantage of a growing market of connectable devices such as health and wellness monitors and sports and fitness monitoring equipment, in addition to other devices such as home automation equipment that also uses Bluetooth Smart. According to the IHS study “Wireless Opportunities in Health and Wellness Monitoring – 2012 Edition,” more than 69 million Bluetooth low energy health and sports monitors will ship between 2012 and 2017.
Phillip Maddocks, market analyst at IHS, states, “By incorporating Bluetooth Smart into the BlackBerry 10 platform, BlackBerry will be able to provide a platform that is renowned for its enterprise use, in addition to meeting casual users needs who might want to use their device to monitor their health or sports performance, or in some instances, turn a light off inside their house using their phone. Several sports and fitness vendors such as Nike, Polar, Garmin and Wahoo Fitness have already either released or announced products that utilize Bluetooth Smart.”
BlackBerry has followed a trend set by other smartphone manufacturers on the hardware side by providing up-to-date connectivity, in addition to providing 4G support and support for future mobile payment platforms. Near Field Communication was previously included within the BlackBerry Bold 9900/9930 and enabled users to make payments using their smart phone. Other platforms such as Android have also been offering the same functionality, utilizing Google Wallet on NFC enabled phones. According to IHS, more than 75 million NFC enabled cellular handsets were shipped in 2012, and with today’s announcement, and the expectation that other major phone manufacturers incorporate the technology, this will continue to grow.
BlackBerry’s announcement brings its handsets in line with where the industry is heading, despite being delayed, and later to the market than expected.
Original Broadcast Date: Thursday, January 31, 2013
Summary:
Highway Patrols monitor highways and catch those who violate speed limits. There is no serious monitoring of GNSS bands. GNSS bands are routinely intentionally or un-intentionally violated. This webinar focuses on GNSS interference awareness and how to defend, monitor, and report such interferences.
Javad Ashjaee
Speaker: Javad Ashjaee
President and CEO, JAVAD GNSS
Javad Ashjaee received his Ph.D. in electrical engineering from the University of Iowa. He was chairman of the Computer Engineering Department, Tehran University of Technology, 1976-1981. He began his GPS engineering career at Trimble Navigation, 1981–1986. Founder and president of Ashtech Inc., 1986–1995, the company that produced the first integrated GPS-GLONASS receivers; founder and CEO of Javad Positioning Systems, 1996–2000, which he sold to Topcon Corporation. He founded JAVAD GNSS in 2007, and is currently president and CEO. In 2010, the company introduced the integrated geodetic receiver TRIUMPH-VS, with a GNSS Interference Analyzer, capable of tracking current and next-generation signals of GPS, GLONASS, QZSS, and Galileo signals. In 2011, the company introduced a LightSquared-compatible GNSS receiver.
Taoglas USA, Inc., provider of antenna solutions to the M2M and connected device market, has launched the FXP.611 Cloud, a GPS/GLONASS flexible loop antenna that the company says outperforms most active patch antennas with an efficiency of 80 percent and a peak gain of 3 dBi across the GPS and GLONASS bands (1575 to 1610Mhz).
This antenna can resist external detuning effects due to dual resonance and has a small form factor of 38 x 37 x 0.1 millimeters. At less than half the cost of heavy active patch antennas, this peel and stick, flexible loop antenna is suitable for any GPS/GLONASS M2M device, Taoglas said.
“We’ve been blown away by the performance of this linear polarized A-GPS GLONASS antenna,” said Dermot O’Shea, director at Taoglas. “Before we developed the FXP.611 Cloud, we had only seen this kind of performance from active patch antennas. We ran a drive test in downtown San Diego and were surprised by the real-time performance and first time to fix from cold-start of this passive loop, particularly in urban canyons where you expect active patches to out perform.”
Original equipment manufacturers will find the FXP.611 suitable for assisted GPS/GLONASS applications for industrial handheld devices, tablets and smartphones. According to Taoglas, the patent-pending FXP.611 Cloud antenna
eliminates the need for a filter or low noise amplifier (LNA), and can connect directly to a module or to a connector on a board.
offers a “peel and stick” mounting with 3M tape that can be attached to plastic device housings freeing up board space.
costs half the price of active patch antennas.
incorporates a detuning design with dual resonance.
radiates power uniformly with an omnidirectional design, making it suitable for use in devices that have fixed positions.
The FXP.611 Cloud antenna is available for purchase immediately from Taoglas by contacting [email protected] and online later in the first quarter of 2013 with Taoglas distributors.
NOAA’s National Geodetic Survey (NGS) is conducting a 12-year project, called Gravity for the Redefinition of the American Vertical Datum (GRAV-D), to redefine the vertical datum of the United States by flying airborne gravity missions. The accuracy of the resulting vertical datum depends directly on the quality of the aircraft’s GNSS position solutions.
In August 2010, NGS issued a Kinematic GPS Challenge to seek community input on the best practices for processing this large positioning data volume. Ten international groups answered the call, submitting 16 different position solutions calculated with a variety of software and techniques. However, the majority of solutions were corrupted by a characteristic “sawtooth” pattern which was tracked back to the aircraft receiver used in the initial challenge; for this challenge reissue, a second onboard GNSS receiver is used. Also in this new call for participation, inertial measurement unit (IMU) data are made available for joint GPS+IMU processing.
“To further facilitate our software and method development, we invite interested researchers and practitioners to compute and submit solutions from samples of actual GRAV-D data,” said Gerry Mader and Theresa Diehl, NGS, in an invitation email. “In this new call, NGS requests that all participants submit a GPS-only solution utilizing the new aircraft GPS data. For those able to process with IMU data, we request additional submission of a second IMU+GPS solution. NGS would like to receive all solutions by April 1, 2013.
“This is a strictly voluntary exercise for those interested in such a comparison and we will share our results with the participants. We are also interested in possibly co-authoring a publication with the participants on the topic if results are significant.”
Detailed information on the challenge is available here:
Those interested in participating should read through the PDF (link above), then email Gerry Mader (gerald.l.mader at noaa.gov) and Theresa Diehl (theresa.diehl at noaa.gov) with any questions.
That’s been one of the mantras — a controversial one, granted — of technological advance in the late 20th and early 21st centuries. It has succeeded in penetrating the global positioning, navigation, and timing vanguard, as evidenced by a handful of key presentations on the first day of the Institute of Navigation (ION) International Technical Meeting in San Diego on Monday.
Skybox Imaging, a company that is “passionate about bringing Moore’s Law to space via disruptive microsatellite technology, rapid development cycles, and a scalable web-based delivery platform,” spoke to the ION ITM plenary session in the person of Ronny Votel, an engineer leading the company’s guidance, navigation and control division. Skybox’s goal is to provide “easy access to reliable and frequent high-resolution images . . . through a “constellation of imaging microsatellites delivering high-resolution imagery of any spot on Earth multiple times per day.”
To achieve that goal, Skybox is developing a low-cost imaging satellite system:
design life of the satellites, 3 years;
size of the satellites, a mini-fridge;
size of the constellation, in the tens.
Skybox will pair that flying system with web-accessible big data processing platform to capture video or images of any location on Earth within a couple of days — an unheard of delivery turnaround in the current global imaging industry, unless you happen to be a government (as in central, high, federal, perhaps military) customer.
The low-cost nature of the satellite opens the possibility of deploying tens of satellites which, when integrated together, have the potential to image any spot on Earth within an hour. Votel several times made the analogy in his talk of using an iPhone camera to capture desired imagery, and indeed that could be a next logical step in FBC development: just throw a bunch of camera phones up into orbit.
Skybox expects to launch its first two satellites later this year.
In April of last year, Wired published a fascinating history and analysis: “Smaller, Quicker, Secret, Robotic: Inside America’s New Space Force.” Between Between 1992 and 1999, the U.S. National Aeronautics and Space Administration (NASA) launched 16 faster, smaller, cheaper missions, including Mars probes and space telescopes. Ten missions succeeded; six failed. Analysts declared the initiative a failure, and to a large extent it has been forsaken. Recent public writings, though, show second thinking. “I would like to respectfully suggest that success-per-dollar is a more meaningful measurement of achievement than success per-attempt,” stated one Air Force lieutenant colonel in a treatise on program management lessons from NASA.
Could such an approach work for GNSS satellites, some of which are burdened with extraneous non-PNT payloads that make them far from FSC? Time will tell the wiser.
Microtechnology
In that FSC vein, at one of the afternoon’s technical sessions, Andrei Shkel of UC-Irvine had been scheduled to deliver a paper on “Precision Navigation and Timing Enabled by Microtechnology,” but apparently something came up and he was not able to appear. I had looked forward very much to what I anticipated would be an update to his September 2011 article in GPS World, “Microtechnology Comes of Age,” which was itself an update to a plenary talk he gave at ION ITM back in 2011. For now, that article will have satisfy us.
Other presentations in the same MEMS, atomic clock, and MicroPNT session:
Michael Bulatowicz of Northrop Grumman talked about a DARPA-backed project, the nuclear magnetic resonance (NMR) gyroscope. Northrop’s development and research has shown a viable solution to producing a small (size of a U.S. quarter coin) low-power navigation grade gyro using non-vibratory technology. The company has produced two prototypes and is at work on two more. Feed the NMR gyro into Shkel’s work and who knows what you’ll get in terms of FBC PNT? Well, maybe not cheaper in the immediate future. Bulatowicz said that as an assembled device he expected its cost, at least initially, to be substantially higher than MEMS technology.
Richard Waters of Lumedyne Technologies spoke on next-generation MEMS inertial sensors with white-noise characteristics, a new paradigm based on time-domain switching for how MEMS sensors might work. TDS inertial sensors provide some key benefits: a purely digital approach, recalibration due to bias drift is not required, output is independent of oscillator conditions. Power is low, less than 1 millwatt. The device demonstrated switch stability under static conditions to –170 db. The same TDS concept can also be applied to a mechanical gyro.
QZSS
In other ION ITM first-day news, H. Tokura of the Tokyo University of Marine Science and Technology talked about “The Possibility of Precise Automobile Navigatin using GPS/QZS and Galileo E5 Pseudoranges.” Currently, research and prototype automobile high-precision PNT is done with real-time kinematic (RTK) networks, but this has some disadvantages, as discussed in an article by authors from the University of Nottingham, UK, in the February issue of GPS World.
Japan’s QZSS now broadcasts L5 signals. Japan has essentially leapfrogged the United States, since the L5 signals with full CNAV message is already broadcast by satellite QZSS-1. Currently, three U.S. GPS satellites are L5 CNAV-capable, but none are broadcasting such a signal.
Tokura showed results demonstrating that pseudorange observables from L5 are basically robust enough for this task. Further investigation for L5 is required because manufacturers are still developing the tracing technique for the new L5 signal. A software-defined receiver is indicated for usage.
Hideki Yamada of Japan’s Electronic Navigation Research Institute spoke about the possibility of using only the QZSS constellation, “in case of GPS failure,” for RTK positioning in precision ag and machine control, with 4 to 7 QZSS satellites that could be launched in a future version of the constellation. QZSS has been shown to provide 10-meter accuracy in absence of GPS; now the research looks at an RTK method.
With only one satellite in orbit, RTK-QZSS cannot be tested in the field. The researchers simulated a fuller constellation by using QZS-1, Multifunctional Transport Satellites (MTSAT), a set of geostationary weather and aviation control satellites, and GPS signals. Using a JAVAD Alpha receiver, Trimble and NovAtel antennas, they obtained results with low multipath error (about 30 centimeters) in a Tokyo environment. Multi-epoch processing is necessary for RTK-QZSS. This solution can work well as a minimum backup system of high-precision position under relatively moderate DOP condition.
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Living may be easy, dying may be hard. But I’m wide awake, staying up late, sending my regards.
Talking to GIS managers over the past several months, I’ve heard a lot of hand wringing from many regarding the future and I share much of the concern. We seem to be at loggerheads on the budget or lack thereof, taxes, energy, gun control and many other issues that make optimism difficult to maintain. For those of you that haven’t faced lean budgets, the next few years may get difficult. GIS operations are especially vulnerable since many politicians don’t really understand “that technical stuff” like GIS, and that GIS doesn’t cost money — it saves money, and even more important, it saves lives.
There is a general misconception among non-GIS people and politicians that we don’t need our own geospatial operation, we can just use Google. In some cases, like planning a trip, that may be true, but for many of your organizations that may not be enough. One example that struck close to home when I was the GIS manager for the Atlanta Regional Commission (ARC) was a failed drug raid in one of our counties. The drug dealers were clever enough to switch house numbers with the house next door. When the SWAT team raided the wrong house, based only on Google Maps and the house number, the resulting lawsuit of the innocent family cost the county thousands even though no one was hurt.
People have to be educated that Google’s key purpose is to drive traffic to its website and sell advertising. Google is not intended to be an authoritative source, and reading the terms of use, one finds special Federal Use Restrictions and “FAR and DFARS provisions.” Google is intended for “planning purposes only” and “makes no representations or warranties regarding the accuracy or completeness of any content.”
Many of you are involved in creating the content that others, including Google, use. Google uses data from more than 12 federal agencies and augments them with commercial data. However, only your staff can determine your exposure to inappropriate use or incomplete data. At ARC, we spent considerable effort constantly updating our street base so planners and 911 call centers had the latest data. Many of those updates took over a year to make to Google Maps.
I learned through countless budget and reorganization battles that you have to be prepared with hard numbers, data and documentation to back up your position. There is nothing more gratifying than to pull out a stack of documents that completely shuts down your opponent’s unsubstantiated “opinion” during one of these battles. But this takes forethought and preparation. GIS professionals must constantly work to build data that shows a “return on investment (ROI).” ROI may be a common concept in the business world, but it’s not used as frequently in government service. We need to be ready at all times to show in real terms how GIS impacts our organization and how it helps improve efficiency. The easiest way is to accumulate and remember needed documentation, to collect it as you operate day to day by placing emails or other information into a physical or electronic drawer. That way you can pull out needed documentation on short notice and not miss key points.
As depressing as this potential damage control may sound, I’m very optimistic long term for the country, the planet and GIS. The real numbers seem to fly in the face of the “doom and gloom media” who only sell content covering plane crashes rather than successful landings. Recently John Stossel had a program in which he featured the noted author Matt Ridley who introduced his newest book, The Rational Optimist. Reading his book I was very encouraged by his research. Put in a nutshell, he cites that despite the depressing picture painted by the media, life is better for more people than it has ever been in history. Inflation-adjusted prices for goods and services are lower in all categories with the exception of health care and education. In his analysis he uses labor as cost measure. In the 1900s, an average person spent 76 percent of his income on food, clothing and shelter. In 2012 that number is down to 37 percent. That means more disposable income for fun stuff.
The environment is generally cleaner and life expectancies continue to rise. The chance of a world citizen dying in a war, disease or famine continues to fall. As a senior citizen I can confirm many of his points through personal experience and observations. Take transportation: In 1960, gas was 32 cents a gallon and my dad bought a brand new fully loaded Chevy Impala for a whopping price of $3,000. Today, both are 10 times higher but so are salaries. With regard to the environment, in 1950 I remember my mother having to scrub my dad’s white shirt collars and cuffs after only a few hours of wear in a coal-dust and soot-infused Chicago atmosphere. I know of no U.S. city that dirty today. When I was Naval Officer on a destroyer going through a 1974 overhaul in Brooklyn, New York, my sailors would have daily wagers as to the number, type and size of sewage objects that would float past the ship as they worked. Two years ago when I was in New York near the East River, I was surprised at the clarity to a depth of several feet of that same water.
Note this dramatic photo from the New York City Municipal Archive of Grand Central Station taken in 1930. It’s a beautiful photo, but it also shows the heavy particulates in the 1930 air reflecting the sunlight.
We in the GIS community can point with pride to the work we’ve done to make life better for everyone. When I was building street center-line files in the early 90s, I never dreamed that those simple files combined with GPS and cell-phone technology would be providing me with surprisingly accurate maps and imagery, including street level photos and turn-by-turn voice navigation, all delivered to my small cell phone. I no longer struggle with phone books and maps trying to find businesses. I just talk into my smartphone to get phone numbers and least cost routing, including traffic jam avoidance to businesses. It would be interesting to calculate how much fuel the GIS community has saved FedEx, UPS or public schools running a fleet of buses with optimal routing.
Google Earth has become a universal tool to find what you need along with directions, user opinions and alternate choices. I no longer go anywhere without reviewing the route, business hours and imagery. Today, buying a home and learning about neighborhoods, prices, schools, transportation is a breeze compared to 20 years ago. Regional planning including environment, agriculture and providing county services has been significantly helped with GIS. Just consider the effectiveness of police and fire responding to 911 calls.
The intelligence community has moved from analysis of static low-resolution ortho imagery to integration of multiple imagery, full motion video and incorporation of “big data” / social media to create pattern-of-life analysis. We can only speculate how many lives have been saved with this advanced intelligence and how the technology will evolve and improve over the next 10 years.
And that was the key point of Ridley’s book. That it takes a collective human intelligence and division of labor to raise the standard of living for all citizens. He used as an example a simple computer mouse. That small device that you are probably holding in your hand is the result of the intelligence and work of thousands of people ranging from the oil rig workers getting the oil to make the plastic, to mathematicians, engineers and chemist to design the parts and create the IC chips, to assembly-line workers putting the parts together, to a ship’s crew, truckers and retailer that got it to you and all that at a price of only a few dollars. Imagine building one from scratch with no outside assistance.
Even more amazing is how fast the learning curve has become. In just a few decades we’ve made more progress than the previous 50,000 years and the curve seems to be increasing. GIS has been part of that process and I look forward to an even better decade ahead.
I’m going to attend the ESRI Federal User’s Conference Feb 25-27. Please introduce yourself if you see me (my bald head is easy to spot) and let me know how we can improve our publications.
