Originally developed for navigation and timing applications, signals from global navigation satellite systems (GNSS) are now commonly used for geophysical remote sensing applications, including observation of Earth’s surface and atmosphere using near sea-level ground stations as well as mountaintop, airborne and spaceborne platforms. GNSS reflectometry (abbreviated GNSS-R), which is the technique of using reflected signals to measure properties of Earth’s surface, has been a growing area of research and application for GNSS remote sensing. Notably, the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission produces delay-Doppler maps (DDMs) that are used to monitor ocean surface wind speeds during hurricanes. Meanwhile, terrestrial and airborne GNSS-R has been used to monitor soil moisture, snow depth and vegetation growth. One area of increasing interest is precision reflectometry using signal carrier-phase measurements. The first attempt to perform precision (phase) altimetry over sea ice using GPS reflectometry measurements from the low-Earth orbiting TechDemoSat-1 was reported by researchers in 2017. Subsequently, researchers demonstrated the use of reflections collected by a Spire satellite to perform altimetry over Hudson Bay and the Java Sea and how reflections off ice in the polar regions can be used to measure ionospheric total electron content over the polar caps. While these demonstrations of GNSS-R for precision carrier-phase-based reflectometry are promising, more work needs to be done to characterize when carrier-based altimetry is feasible and what challenges it faces.

To study the challenges associated with processing reflected and low-elevation-angle radio occultation signals, the University of Colorado (CU) Boulder Satellite Navigation and Sensing (SeNSe) Laboratory has deployed a GNSS data collection site on top of Mount Haleakalā on the island of Maui, Hawaii. Recent collection campaigns aim to use this site as a testbed for GNSS-R algorithms that utilize multi-frequency and multi-polarization measurements. Previously, we carried out delay map processing for left-hand circular (LHC) and right-hand circular (RHC) polarizations for L1 and L2 GPS signals. Those results validate the open-loop processing methodology and provide an initial assessment of the data quality. We observed that the received reflected signals show deep and rapid fading in amplitude. In the work reported in this article, we extend our assessment to triple-frequency GPS (L1CA, L2C, L5Q) signals and document our methodology for extraction of the signal carrier phase. Our initial results indicate that coherent signal phase extraction is challenging, and may not be feasible for this particular experiment setup. We discuss ways in which the experiment may be improved for the purpose of obtaining coherent ocean surface reflections in the future.

EXPERIMENT BACKGROUND

The current form of the CU SeNSe Lab Mount Haleakalā GNSS experiment was deployed in June 2020. It consists of a side-facing dual-polarization horn antenna, which is shown in the left panel of FIGURE 1, along with a zenith-facing reference antenna. The horizontally- and vertically-polarized wideband signals from the horn antenna are fed into front-end hardware and are combined using 90-degree phase combiners to form LHC and RHC polarized signals, which are then recorded by a set of Ettus Universal Software Radio Peripherals (USRPs). Meanwhile, the signal from the reference antenna is sent to a Septentrio PolaRxS receiver. The right panel in Figure 1 illustrates the system setup. Note that the Septentrio onboard oven-controlled crystal oscillator is used to drive the USRPs. This allows us to use the Septentrio outputs to estimate the receiver clock variations and use them in the receiver clock component of our open-loop models, which we discuss below.

Figure 1: The side-facing horn antenna in its radome enclosure (left panel) and the hardware block diagram of the data collection system (right panel). (All figures provided by the authors)

Each USRP can record up to four signals at two different mixdown frequencies, allowing for recording of both the RHC and LHC polarized signals on up to four different bands. The first USRP records the L1 and L2 bands with center frequencies at 1575.42 and 1227.6 MHz, respectively, at a bandwidth of 5 MHz. The second USRP records the L5 and E6/B3 bands at center frequencies of 1176.45 and 1271.25 MHz and at a 20 MHz bandwidth. TABLE 1 lists the IDs for each receive channel along with its corresponding band, polarization and sampling rate. Note that the recorded signals covering the E6 band also capture BeiDou B3 signals, but we restrict our analysis to GPS L1, L2 and L5 signals in this article. The samples from these USRPs are written to disk along with the Septentrio Binary Format (SBF) output of the PolaRxS receiver.

Table 1: Receiver IDs with corresponding band and polarization.

Starting in June 2021, periodic collections were taken for around one hour at a time, which is about the amount of time it takes for a GPS satellite to pass from an elevation angle of 0 degrees to one of more than 20 degrees. The collection times were adjusted to target the passes of satellites whose specular reflection point passed within the azimuthal range of the horn antenna, which faces roughly to the south and has a beam width of around 60 degrees. FIGURE 2 summarizes the available datasets from the first month of collections. The right-most panels of FIGURE 3 show examples of the specular track for GPS PRN 6 as it sets over the horizon on June 13, 2022, at around 12:00-13:00 UT. This is the pass on which we focus in this work, since PRN 6 transmits the L1CA, L2C and L5 signals and consistently had a specular point in our region of interest.

Figure 2: Available data during the first month of collections. The average significant wave height in the region south of Haleakalā is also plotted. Numbers near the bottom indicate the datasets analyzed for this article.

METHODOLOGY

Our processing method for open-loop tracking of the reflected GNSS signals is based on our previous work in which we produced DDMs and delay maps of the signal-to-noise ratio (SNR) measurements for multiple signal frequencies and received polarizations.

Pseudorange Model. We start by generating a model of the pseudorange for both the direct and reflected signal. The model only needs to be accurate down to the chip level, since we correlate across several chips of delay for the received signals. Setting a somewhat arbitrary accuracy requirement of 0.5 chips (equivalent to a delay of around 150 meters for L1CA/L2C or 15 meters for L5 signals), allows us to ignore path delays from the ionosphere and troposphere, which should only account for up to several meters of delay. The model has three terms that we estimate relative to GPS System Time (GPST): the receiver clock error, the satellite transmitter clock error and the geometric range. We use a surveyed position of the horn antenna along with International GNSS Service precise orbit and clock products for the transmitter clock error and positions. These allow us to compute the transmitter clock error and path delay for the direct signal. The reflected signal path delay can be found by computing the specular reflection point on the WGS84 ellipsoid and adding the distances from the transmitter to the specular point and the specular point to the receiver. The remaining term to estimate is the receiver clock error. Recall that our USRPs are driven by the Septentrio internal oscillator. Therefore, the clock error in Septentrio measurements is associated with variations in the reference oscillator for the USRPs. We utilize a geodetic detrending technique to estimate these clock variations and apply them to our pseudorange model. To construct the full receiver clock error, we estimate the time-alignment of the samples near the beginning of the collections to GPST by tracking one minute of a strong, mid-elevation-angle satellite and decoding its timing information. This provides us with an estimate of GPST at the start of the file, which we can use to construct a full estimate of the GPST at any sample in the file. Also, given our pseudorange model, we can find the received code phase and the Doppler frequency.

Figure 3: Example of delay maps from GPS PRN 6. The panels to the left show delay maps for the L1CA, L2C and L5 signals, both RHC and LHC polarizations. The bottom panel shows the corresponding elevation angle over the duration of the pass. The maps to the right show the specular point location during the pass, along with a contour of the WW3 model for significant wave height and swell-significant wave height.

Signal Correlation. Using the established code phase and Doppler models, we generate correlations for both reflected and direct signals. We correlate a reference signal over each 1-millisecond interval, and for sanity-checking purposes, we compute correlations over ± 3 chips at 0.5 chip spacing. This results in in-phase and quadrature (I/Q) correlation outputs every 1 millisecond. The left panels in Figure 3 show examples of the processed reflected signals for RHC and LHC polarization L1CA, L2C and L5Q signals from PRN 6 on June 13, 2021, at 12:00-13:00 UT. Note that as the satellite sets, at around 4 degrees elevation angle, the reflected signals merge with the stronger direct signal on the L1 and L2 signals. This happens later on L5 due to its higher bandwidth. We use the 0.0 chip bin to obtain I/Q outputs for carrier-phase processing for L1 and L2. For L5, we use the 0.0, -0.5, or -1.0 chip bin to account for model mismatch toward the end of the file.

Signal Fading and the WW3 Ocean Model. An eventual goal of the Haleakalā reflectometry experiment is to compare the characteristics of processed reflected signals with the ocean surface parameters near the specular point and glistening zone. To this end, we have incorporated data from the Hawaii regional WaveWatcher 3 (WW3) model. The model outputs information about wave height, direction and period due to both wind and swell, and has a resolution of around 5 kilometers. The data from this model is available in NetCDF format from several web services. The right panels of Figure 3 show contours of the wind- and swell-significant wave height in the South Haleakalā region. Meanwhile, note that the reflected signals (left panels) show high variability in the received power throughout the duration of the collection. While we hoped to be able to immediately observe a correlation between these wave parameters and the power fluctuations, it is clear that we need additional processing to tease out such a signal, and the changing satellite geometry will likely make this difficult to observe and validate. Even still, our results at the end of this article will show that there is likely some correlation between fading and wind parameters, though to what extent is unknown. Finally, note that the LHC polarizations (RX6, RX8, RX2) show much stronger reflected signals than the RHC polarizations. Since we are interested in processing the phase for the reflected signals, we report exclusively on the use of the LHC polarization signals in the rest of this article.

Carrier-Phase Processing. Once the correlations are performed, we take the I/Q correlations for both direct and reflected signals and process them to retrieve the cleaned reflected signal phase. The first series of steps in this process involve processing the direct signal to determine navigation / overlay symbol alignment and to estimate any residual phase fluctuations, which are mostly due to unmodeled receiver clock fluctuations. FIGURE 4 illustrates this process for the L1CA signal. The raw I/Q correlations are shown in the top panel. To these we apply a Costas phase-lock loop (PLL) to track the residual phase fluctuations without being sensitive to navigation / overlay symbol transitions. Next, we remove these residual phase fluctuations to obtain the detrended I/Q values.

Figure 4: The I/Q data cleaning process for the L1CA direct signal.

As shown in the second panel, these quadrature components of the detrended I/Q values are centered at zero while the in-phase component now shows the data bits / overlay symbols. We use the detrended I/Q values to estimate the navigation bit sequence on the L1CA and L2C signals. Likewise, we estimate the alignment of the Neumann-Hoffmann overlay sequence for the L5 signal. Finally, we wipe off the estimated data bits or overlay sequence to verify the procedure. The results of wiping off the estimated navigation bits for the L1CA signal are shown in the third panel of Figure 4.

Having obtained the residual phase fluctuations and navigation / overlay symbols for the direct signal, we next apply these to clean up the reflected signal. Specifically, we remove residual phase fluctuations from the raw reflected signal I/Q values and then wipe off the corresponding navigation bits or overlay code. FIGURE 5 shows an example of the reflected I/Q data before and after this procedure. The navigation bits are clearly removed, but the reflected signal still shows fairly significant fluctuations in the cleaned I/Q values. It is from these values that we hope to extract the residual reflected signal phase.

Figure 5: The reflected signal raw I/Q (top) and the I/Q after detrending and wiping off navigation bits for the L1CA signal.

Under coherent conditions, the phase of the clean reflected I/Q data should contain only the unmodeled effects, including any signature of ocean surface height variation. However, the effect of multipath due to the rough ocean surface causes fluctuations in the received signal amplitude and phase, and can additionally cause cycle slips when we unwrap the phase. To filter out these cycle slips, we apply our simultaneous cycle slip and noise filtering (SCANF) method, which is essentially just a Kalman filter PLL with an additional step that tries to estimate and remove cycle slips. The figures in the next section show the results of applying this entire procedure to the reflected signals. The black and blue lines show the phase before and after applying SCANF. The reflected signal I/Q SNR is also included for reference. Note how the jumps in the black line coincide with SNR fades, and the blue line effectively recreates the phase trend of the black line without these jumps. This is good qualitative evidence that the SCANF algorithm was effective.

RESULTS

FIGURES 6, 7, 8, 9, 10, and 11 show the reflected signal SNR and phase for GPS PRN 6 on 6 different days. Note that these days correspond to the marked days in Figure 2, from which we observe that the wind-significant wave height is relatively high on days 1, 5, and 6, moderate on days 2 and 3, and relatively low on day 4. We noticed that the SNR fluctuations on days 1, 5, and 6 are comparatively more frequent than on other days, which we believe may be a signature of the ocean surface conditions. A more detailed analysis of this result is a topic for our future work.

Figure 6: Reflected signal residual phase before (blue) and after (black) applying the SCANF filtering for the June 11, 2021 dataset. Amplitude and phase are shown in alternating panels for L1CA, L2C and L5 respectively.

Figure 7: Phase processing results for June 13, 2021.

Overall, we observe that the phase trend is not consistent across the three signals (L1CA, L2C, L5) for any of the days. With all the multipath signatures in the cleaned reflected signal, it was uncertain whether the extracted phase will be useful for applications such as ocean surface altimetry, and these qualitative results suggest that they probably will not be. However, season and hours of the day that were processed for our work discussed in this article are very limited. It is possible that processing more data will shed further insight onto whether the reflected signal phase is usable in this experiment.

Figure 8 Phase processing results for June 21, 2021.

Figure 9: Phase processing results for June 25, 2021.

ACKNOWLEDGMENTS

The Haleakalā data collection system has been established with support from the University of Hawaii Institute of Astronomy, and the Air Force Research Laboratory. The authors appreciate the assistance from Michael Maberry, Rob Ratkowski, Daniel O’Gara, Craig Foreman, Frank van Graas and Neeraj Pujara. This research is funded by a subaward from the National Oceanic and Atmospheric Administration through the University Corporation for Atmospheric Research to CU Boulder and with partial funding support from the NASA Commercial Smallsat Data Acquisition program.

This article is based on the paper “Initial Carrier Phase Processing for the Haleakala Mountaintop GNSS-R Experiment” presented at ION ITM 2023, the 2023 International Technical Meeting of the Institute of Navigation, Long Beach, California, Jan. 23–26, 2023.

Figure 10: Phase processing results for July 1, 2021.

Figure 11: Phase processing results for July 5, 2021.

BRIAN BREITSCH is a postdoctoral fellow at the University of Colorado (CU) Boulder, where he received his Ph.D. in aerospace engineering sciences.
JADE MORTON is a professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences and the director of the Colorado Center for Astrodynamics Research at CU Boulder.

<p>The post Using GNSS Phase Reflectometry on Maui’s Haleakalā first appeared on GPS World.</p>

The Raspberry Pi Reflector (RPR) was designed by a team of scientists from the University of Bonn, the Federal University of Rio Grande do Sul, and the University of Colorado.

The solar-powered RPR is much less expensive (about US$150) than scientific-grade or geodetic GNSS instruments — the cost of which is “a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensor,” the team writes in their paper.

The Raspberry Pi Reflector (RPR) prototype includes a low-cost and low-maintenance single-frequency GPS module (an Adafruit GPS FeatherWing receiver) and an unspecified GPS antenna connected to an inexpensive Raspberry Pi microcomputer. One such unit has been successfully operating since March 2020 in Wesel, Germany, next to the Rhine river.

The RPR hardware array: (a) Raspberry Pi 4 Model B (b) Adafruit Feather Adalogger microcontroller (c) Adafruit GPS FeatherWing receiver (d) GPS external antenna (e) Configuration of RPR prototype setup. (Image: Karegar, et al)

The unit on the Rhine provides sub-daily and daily water levels retrieved using spectral analysis of reflection data, or GNSS-reflectometry. The river level measurements from the RPR are compared with a co-located river gauge.

By changing the orientation of the antenna from upright to sideways facing the river, which was done in August 2021, the root-mean-square error (RMSE) was lowered to from 7.6 cm to 3 cm (sub-daily) and 6 cm to 1.5 cm (daily), the team said.

“While satellite radar altimetry techniques have been utilized to monitor water levels with global coverage, their measurements are associated with moderate uncertainties and temporal resolution,” the team states. “Therefore, such low-cost and high-precision instruments can be paired with satellite data for calibrating, validating and modeling purposes.”

Information about the RPR is available on GitHub.

<p>The post Inexpensive sensor created to monitor Rhine river levels first appeared on GPS World.</p>

Innovation Insights with Richard Langley

AS CAT STEVENS (yes, he’s back to using his old name) famously sang on “Wild World”:

“… take good care
Hope you make a lot of nice friends out there
But just remember there’s a lot of bad and beware
Beware.”

While he was talking about a girlfriend leaving him, the warning can just as well apply to GNSS users — especially those relying on GNSS for safety-of-life navigation and the maintenance of critical public infrastructure systems.

GNSS signals are relatively weak and they are susceptible to unintentional and intentional jamming that can make reception of the signals difficult or impossible. The jamming of radio signals to hinder reception is nothing new. It’s been used by those wanting to interfere with the use of the radio spectrum ever since radio became an important tool for communication and navigation in the early 20th century. Jamming has been used in hot wars to try to defeat military communication as well as in cold wars to try to prevent a perceived enemy from broadcasting to a particular country’s citizens. Notably, the shortwave radio broadcasts from Western countries were jammed by the former Soviet Union. And even today, broadcasts directed at China, Cuba and some other countries are regularly jammed.

GNSS is also being intentionally jammed on a regular basis in some parts of the world for various purposes including the protection of politicians and civilian infrastructure and to foil GNSS-guided munitions. But while directed at supposed threats, the jamming affects all GNSS receivers in a certain radius of the jammer. Such jamming activities are being reported in the popular press with an increasing frequency.

While GNSS jamming is receiving increased attention in our troubled world, even more pernicious is GNSS spoofing. Spoofing is the attempt to mimic GNSS signals to try to trick a receiver into tracking them and thereby compute a wrong position and/or time at the receiver. This can have disastrous consequences if not detected immediately and the use of GNSS deactivated.

So, how do you detect GNSS signal jamming and spoofing? We have discussed this issue in several columns over the years, but in this month’s column, a team of researchers from Stanford University and the University of Colorado describe how they are using relatively inexpensive equipment and sophisticated software and analyses to detect and warn of GNSS jamming and spoofing. Clearly, they are heeding Cat Stevens’ warning.

By Leila Taleghani, Fabian Rothmaier, Yu-Hsuan Chen, Sherman Lo, Todd Walter, Dennis Akos and Benon Granite Gattis

GNSS signals are extremely low power by the time they reach users on Earth and are easily overwhelmed by nearby terrestrial signals. Such signals can interfere with a user’s ability to receive the desired GNSS signals or, even worse, replace them with simulated signals that cause the user to obtain the wrong position or time estimate. Two major types of radio-frequency interference (RFI) threats have been identified: jamming and spoofing. Jamming results from emissions that do not mimic GNSS signals, but interfere with the receiver’s ability to acquire and track GNSS signals. Spoofing is the emission of GNSS-like signals that may be acquired and tracked in combination with, or instead of, the intended signals.

Both threats have been studied at length by researchers, and their presence around the globe has been reported even in the popular press. Some research has been done into the prevalence of spoofing. Even so, there is no well-developed understanding of how widespread these threats are.

Terrestrial interfering signals may be fairly weak and only effective in a limited area. Complex environments with buildings or terrain may further limit their effective area of influence and hinder the ability of external interference detection. To create a better understanding of the presence and characteristics of jamming and even spoofing, we are developing a low-cost RFI detector based on a commercial, off-the-shelf GNSS receiver: the u-blox F9. We are pairing this receiver with a Raspberry Pi computer and are developing custom software to monitor the receiver outputs and store data surrounding interesting events.

We are developing a toolset in MATLAB and C/C++ with the intention of processing and analyzing the u-blox data. The toolset includes functionality to decode selected u-blox messages that contain parameters of interest. These metrics include automatic gain control (AGC), carrier-to-noise-density ratio (C/N₀) and spectral power. They also include raw pseudoranges from multiple constellations and internal u-blox interference metrics. With the volume of data that can be gathered from continuous monitoring, we have begun characterizing nominal performance and developing approaches to spoofing and jamming detection. The publicly available code can be accessed through our Git Repository at https://github.com/stanford-gps-lab/navsu.

With the raw pseudoranges and downloaded broadcast ephemeris data, we compute navigation solutions using different combinations of constellations and frequencies. When the individual and multi-constellation position solutions are compared to each other, discrepancies can be flagged and investigated for possible interference. We have begun characterizing nominal power metrics such as AGC and C/N₀. With the quantity of data that we can get from the RFI monitor, we are working to characterize other receiver-specific parameters such as the u-blox continuous wave (CW) jamming indicator. We leverage data collected under nominal and jammed conditions to understand and identify a threshold for what can be considered interference.

Many different methods have been proposed for GNSS interference detection and mitigation with large-scale data at multiple locations. In this article, we present our data-selection process, our development of thresholds for determining interference, and results from three u-blox receivers set up at different locations in the United States to glean information about nominal (non-spoofed) conditions. We inform our thresholds and analysis tools using datasets from nominal conditions, and then compare their performance to a dataset containing RFI events from a government-sanctioned jamming and spoofing test. Our results display how we leverage simple and powerful metrics informed by a low-cost receiver to understand nominal noise environments and successfully identify jamming and spoofing events.

Data and Metrics

We collect and analyze a variety of data types and metrics to help identify and characterize jamming and spoofing occurrences. The receiver model we started with, u-blox ZED-F9P-02B, can monitor two different RF bands and many signals, including GPS L1C/A, L2C; GLONASS L1OF, L2OF; Galileo E1B/C, E5b; BeiDou B1I, B2I; QZSS L1C/A, L1S, L2C; and SBAS L1C/A. It has 184 channels, which can be configured to sweep through an array of signals to be monitored. We are also developing monitors based on the recently released ZED-F9T-10B, which is capable of L1 and L5 signal reception. TABLE 1 describes which version of the u-blox receivers each dataset comes from.

TABLE 1. Locations of u-blox monitor for nominal noise environment characterization and jam/spoof test. (Data: Authors)

L1 and L5 are the primary frequencies used for aviation, hence a monitor for these frequencies would be more useful for protecting aviation than the F9P, which is only capable of L1 and L2 reception. The available data includes raw measurements such as code and carrier phase, position estimates, power level estimates including C/N₀, AGC and spectral power. It also has active CW interference detection. These metrics are all necessary for the consistency checks and power monitoring methods we summarize in this article. Consult our conference proceedings paper for details (see Acknowledgments). By examining all of these signals and measurements, we can observe changes in the RF environment and detect inconsistencies in the received signals.

Data Logging. The u-blox receiver logs messages in a specific format. The message types important to log are selected based on the desired data. Due to limited bandwidth, we prioritized messages that efficiently include all desired parameters for the interference detection methods we describe in this article. We have used both the u-blox F9P and the u-blox F9T. 

To characterize nominal noise environments, u-blox receivers were set up at three locations: Stanford University, the University of Colorado (CU) in Boulder, and at the Colorado Springs airport. All measurements from satellites below an elevation angle of 5 degrees were ignored. The results from these locations are summarized below. Results from a jamming/spoofing test sanctioned by the U.S. Department of Homeland Security are presented and labeled with the acronym “GET-CI” (GPS Testing for Critical Infrastructures) in the subsequent discussion. Table 1 describes the parameters of the u-blox receiver at each location.

Positioning Metrics Development. The nominal error of the single- and multi-constellation position solutions is made by noting the difference between the computed position and the known truth. The inter-constellation consistency check is defined as the difference between the positions computed from two constellations, with no reference to a known truth position. To analyze the nominal differences in the north, east and down (NED) directions, we use the position covariance matrix, R, computed in the least-squares solver, to set a covariance-bound threshold. The covariance for each constellation is assumed independent. We present our results using this threshold in our results sections. 

Our results in FIGURE 1 show that the Galileo position solution variance is higher than the dual-constellation and GPS-only solution. This is attributed in part to the fact that Galileo, while operational, has not filled out all planned satellite slots and therefore has fewer satellites and worse geometry than GPS. 

FIGURE 1a. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at Colorado Springs. (Image: Authors)

FIGURE 1b. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at CU Boulder. (Image: Authors)

FIGURE 1c. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at Stanford. (Image: Authors)

Nominal Noise Results

Here are some of our positioning and power monitoring results under nominal reception conditions.

Positioning. Based on the methods described earlier, we present a selection of our results from the positioning consistency checks. We present several informative visualizations of the error between the computed position solution and the known truth of each u-blox receiver and use the covariance threshold to bound the raw error. The error for dual-constellation, single-constellation and inter-constellation consistency checks are all displayed and compared to one another. The pseudorange residuals and their accompanying chi-squared (χ²) statistic are also evaluated and compared for the GPS and Galileo single-constellation position solutions.

Positioning Consistency Comparison Maps. From the maps in Figure 1, we observe that Galileo has the highest error, followed by GPS, and then the dual-constellation solution. The map also serves as a method to spatially visualize the tails of the error distribution.

NED Time Histories. We compare the time history of the dual-constellation, GPS and Galileo position solution error to the three sigma (3σ) covariance bound computed at each epoch (see FIGURE 2). We also compare the GPS vs. Galileo inter-constellation difference to the 3σ covariance bound. The covariance bound is never crossed, indicating that 3σ threshold is conservative for both the error and the inter-constellation difference between GPS and Galileo.

FIGURE 2a. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at Colorado Springs. (Image: Authors)

FIGURE 2b. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at CU Boulder. (Image: Authors)

FIGURE 2c. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at Stanford. (Image: Authors)

Pseudorange Residuals and χ² Statistic Threshold. Pseudorange residuals have a long history of being used as a consistency check between range measurements. As an example, the pseudorange residuals for the GPS position solutions are shown in FIGURE 3, and their corresponding χ² statistic is shown in FIGURE 4.

FIGURE 3a. GPS pseudorange residuals at Colorado Springs. (Image: Authors)

FIGURE 3b. GPS pseudorange residuals at CU Boulder. (Image: Authors)

FIGURE 3c. GPS pseudorange residuals at Stanford. (Image: Authors)

FIGURE 4a. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at Colorado Springs. (Image: Authors)

FIGURE 4b. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at CU Boulder. (Image: Authors)

FIGURE 4c. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at Stanford. (Image: Authors)

The χ² statistic is computed using the finite pseudorange residuals at each epoch, where the degrees of freedom are n − 4, where n is the number of satellites used at that epoch and 4 is the number of variables solved for (x, y, z, and the receiver time offset) when using a single constellation. A p-value is computed using the cumulative distribution function (CDF) of the χ² statistic, and indicates the probability that the χ² statistic at each epoch would be greater than the observed value. The statistic is compared to a theoretical 10⁻⁹ probability of false alert (P_FA) based on the theoretical χ² and the actual degrees of freedom of each epoch. Very low values for the χ² statistic, such as those obtained with Galileo, are attributed to regions where very few satellites are in view, thus decreasing the degrees of freedom. Any spikes in the pseudorange residuals are also reflected with a higher χ² statistic and low p-value, though those residuals are de-weighted in the position solution and ultimately do not trigger the 10⁻⁹ P_FA threshold or the 3σ threshold, thus indicating that a 10⁻⁹ P_FA is a conservative threshold. 

Power Monitoring. For each nominal location with a u-blox receiver, we analyze results from the power-monitoring metrics mentioned earlier. We also observe results from the internal u-blox jamming indicators in a region where a possible RFI event was observed.

For power monitoring, we analyze spectral power and programmable gain amplifier (PGA) results. 

For the nominal noise environments, the spectral power, PGA and corresponding C/N₀ results indicated no significant anomalies.

Threshold and Metric Validation Results

An examination of thresholds and other metrics are important for characterizing RFI.

GPS Testing for Critical Infrastructure. From a DHS-sanctioned RFI testing event, we identify five regions of interference or spoofing. To identify the interference, we use a combination of the power and positioning metrics as well as the thresholds we developed through the characterization of the nominal noise environments described in the previous sections of this article.

We use the thresholds and tests we’ve developed to identify regions of spoofing and RFI events (labeled C I1–C I5) in the GET-CI dataset. For ease of comparison, all regions are labeled on plots that display the full 5.5 hours of data collection. All details as to the truth location and time of the test have been removed. C I1 is identified through the power metrics. C I2–C I5 are identified as regions that the NED difference between GPS and Galileo clearly crossed the 3σ threshold in all three directions, as visualized in FIGURE 5.

FIGURE 5a. Map view of solutions using GPS, Galileo and GPS plus Galileo for the DHS-sanctioned RFI testing event (identifying coordinates and physical features removed). (Image: Authors)

FIGURE 5b. Corresponding log-scale visualization of the GPS vs. Galileo position solution difference in the north-east-down directions. (Image: Authors)

From our pseudorange residuals, it appears as though the most significant interference events happened on the GPS constellation, as indicated by the high pseudorange residuals that fall into the C I2 and C I5 regions. Using the GPS χ² statistic and p-value computations, we determined that the regions that crossed the 10⁻⁹ P_FA threshold line are consistent with the regions of interference identified in Figure 5. The Galileo χ² statistic, p-values and pseudorange residuals all show signs of possible interference. These regions are explored more in the power monitoring discussion below. 

Since the GPS pseudorange residuals and χ² statistic results show more signs of spoofing than the Galileo ones, we explore the Galileo-only position solution. Because the truth position is unknown, we take a point during the non-C I regions and define this as the “truth,” that is, a point in the position solution we believe has not been subject to spoofing. Any references to a truth position are from a position recognized as “truth” through post-processing rather than from a pre-determined and known location.

The p-values dip in each of the C I regions, but are lowest in regions C I5. Combined with the fact that the pseudorange residuals and NED error are the highest in C I5, we identify this as the region that likely experienced a significant spoofing event. We determined from an outlier at the beginning of the C I5 region (see Figure 5) that even the Galileo constellation is not immune to the spoofing in this scenario.

To further check the accuracy of our determination that GPS was spoofed, we evaluated the histograms of the Galileo error. With the biggest outlier in C I5 removed, we saw that the error appears relatively Gaussian, with some outliers and possible multi-modal behavior that were also seen in the nominal locations. The variance was higher than was observed at nominal locations, which could be attributed both to the presence of known RFI events, the fact that the nominal noise environment at the RFI event test has not been characterized (that is, it is possible there is a noisier nominal environment at this location), and that the “truth” position was not a known truth but obtained through post-processing of a dataset with increased RFI. Normalized error indicates that the error does not cross the 3σ threshold in any NED direction, further supporting the assertion that 3σ is a conservative threshold.

Important to note is that the major outlier around T+3.5 hours is visible in the NED plot (Figure 5), but the corresponding histograms do not contain that outlier. This indicates that the covariance also increases at that point. It dictates a need to monitor the covariance bound itself, as well as the positioning error. The NED time history plot and the raw error histograms serve this purpose, since it is clear that if we were to be only looking at the error normalized by 3σ, we would not have found significant evidence of the outlier, since the normalized error barely passes the 3σ threshold. This further supports our methods of combining multiple metrics, thresholds and visualizations rather than relying on a single metric to identify jamming and spoofing.

From the Galileo solution analysis, we increase our confidence that we have identified the regions with interference. We removed those areas and looked at the GPS vs. Galileo inter-constellation consistency difference. The normalized differences were now mostly within the 3σ threshold, and the raw error displayed some Gaussian behavior and is no longer on the order of the 10⁵-meter error we were seeing in Figure 5. While these regions still have a higher error than nominal conditions and thus still display signs of interference, we are able to use our spoofing analysis to identify epochs in which we should not trust the GNSS. Using times outside those regions, we are able to figure out a reasonable truth position within 20 meters rather than 200 kilometers.

Positioning analysis using the inter-constellation consistency check is a powerful tool for determining the reliability of a position solution, even when the truth location is unknown. With the power metrics, we can further corroborate the positioning results, as well as find events indicating interference that the positioning metrics were unable to track. 

FIGURE 6a. GPS pseudo range residuals for position solutions computed using only the GPS constellation. (Image: Authors)

FIGURE 6b. Galileo pseudorange residuals for position solutions computed using only the Galileo constellation for the DHS-sanctioned RFI testing event. (Image: Authors)

Next Steps and Summary

Leveraging the raw data collected by u-blox receivers in multiple locations with different nominal noise environments, we have developed the toolsets to do inter- and intra-constellation consistency checks to monitor for jamming and spoofing. Many further observables usable for RFI detection are being recorded by the u-blox receivers. Several power monitoring metrics have been evaluated in a preliminary analysis. The next step is to further characterize metrics such as C/N₀, AGC and u-blox internal jamming metrics under nominal conditions. 

In summary, the tools we have developed so far show that the u-blox receiver will allow for many different consistency checks on a variety of parameters to be running simultaneously. It would be difficult for a spoofer to interfere with all the dimensions we have covered in our detector. Continuously monitoring a wide variety of parameters will increase the chance that we are able to detect interference, thus lowering the chance that a spoofer is able to evade detection.

Acknowledgments

We gratefully acknowledge the support of both the FAA Satellite Navigation Team and The Aerospace Corporation under their university partnership program. We especially wish to thank Steve Lewis of Aerospace for his support and guidance throughout the development of this project. This article is based on the paper “Low Cost RFI Monitor for Continuous Observation and Characterization of Localized Interference Sources” presented at ION ITM 2022, the 2022 International Technical Meeting of the Institute of Navigation, Jan. 25–27, 2022. 

LEILA TALEGHANI recently graduated with her MS degree from Stanford University in aeronautics and astronautics and is now a navigation engineer at Trimble.

FABIAN ROTHMAIER is a navigation research and development engineer at Airbus Defence and Space in Munich, Germany, and a former a Ph.D. student at the Stanford GPS Laboratory. 

YU-HSUAN CHEN is a research associate at the Stanford GPS Laboratory. 

SHERMAN LO is a senior research engineer at the Stanford GPS Laboratory.

TODD WALTER is a research professor in the Department of Aeronautics and Astronautics at Stanford University. 

DENNIS AKOS is a professor with the Aerospace Engineering Sciences Department at the University of Colorado, Boulder.

BENON GRANITE GATTIS is a laboratory assistant and undergraduate student in the Aerospace Engineering Sciences Department at the University of Colorado, Boulder.

<p>The post Innovation: Monitoring GNSS interference and spoofing — a low-cost approach first appeared on GPS World.</p>

Photo: Trimble

The University of Colorado – Denver has received a significant gift from Trimble to establish a state-of-the-art technology lab for the College of Engineering, Design and Computing.

The gift will also support the departments or programs in construction engineering and construction management, geography and environmental sciences, physics, and urban and regional planning. The lab will expand the university’s access and expertise in a customized suite of construction hardware and software products.

Trimble’s broad Connected Construction portfolio enables professionals along the project lifecycle to accelerate project processes — improving productivity, quality, transparency, safety and sustainability, while reducing waste.

The Trimble Technology Lab will provide students enrolled across relevant programs hands-on experience with a wide breadth of Trimble solutions. The lab will expand the university’s access and expertise in project management, architectural and structural analysis, design and engineering, mixed reality, 3D scanning, office-to-field solutions, and GIS data collection and GNSS positioning.

Partnering with Trimble allows the University of Colorado – Denver to integrate the latest technology into its curricula, empowering graduates to rapidly transform how buildings and living environments are designed and constructed.

The lab will include a broad range of Trimble’s technologies.

• Hardware includes the Trimble XR10 HoloLens with hardhat, TX8 3D laser scanner, Trimble SiteVision AR system, R12 GNSS systems, Juno 5D handheld scanner, Geo 7x mobile GNSS data collectors, robotic total stations and field tablets.
• Software solutions include RealWorks scanning software, Trimble Business Center, Tekla Structures, Tekla Structural Designer, Tekla Tedds, Trimble Connect, ProjectSight, Viewpoint, TILOS, Trimble Positions Desktop, TerraSync and TerraFlex, eCognition, and the company’s 3D modeling software, SketchUp Pro.

“CU Denver is right in our backyard, providing an exciting opportunity to integrate our industry-leading technologies into a wide range of educational programs. Their proximity enables us to work closely while ensuring easy access, training and support, and success in all aspects of implementation,” said Allyson McDuffie, director of Education & Outreach at Trimble. “Trimble’s education and outreach programs aim to support the next generation of influencers by actively working with key education institutions to ensure Trimble’s portfolio of solutions are accessible and implemented in higher education curricula and research programs, creating a new workforce equipped and empowered to ‘Transform the Way the World Works.'”

Martin Dunn, dean of the College of Engineering, Design and Computing, said, “I am thrilled with and grateful for this exciting relationship with Trimble. It will accelerate our strategic vision to educate diverse graduates who will not only make an immediate impact in the AEC industry, but will emerge as its future leaders. The generous gift will have broad impact across our campus, nucleating the kind of interdisciplinary collaboration among engineers, architects, construction managers, and scientists that is needed to create and exploit technological innovation to address grand challenges facing the built environment including digital transformation, sustainability, and the future of work and the workforce.”

“Our students and faculty could not be more excited to have access to Trimble technologies. Trimble is a company of international importance, which is also right down the road from our campus. In establishing this new lab, our students will be exposed, either virtually or on-site, to cutting edge products and innovation as well as benefit from direct access to the many professionals in Trimble’s worldwide network. Trimble is exactly the type of company that gets our students excited about pursuing careers in construction and engineering,” said Caroline Clevenger, associate professor and director of Construction Engineering and Management.

<p>The post UC Denver to establish Trimble lab for College of Engineering, Design and Computing first appeared on GPS World.</p>

Morton Honored with Kepler Award

Y. Jade Morton received the Johannes Kepler Award for advances in scientific and navigation receiver technology, automated data collection, robust carrier phase tracking, remote sensing, and profound impact as an educator and author.

Morton is the director of the Colorado Center for Astrodynamics Research at the University of Colorado, Boulder ,where she mentors students, faculty, staff and an ever-expanding international network of collaborators throughout the world. She is a prolific author with more than 270 publications. She was awarded her Ph.D. in Electrical Engineering at Pennsylvania State University. She has also authored articles for GPS World.

Receiver Technology Pioneer. Morton has made pioneering contributions to the advancement of GNSS receiver technology and utilization of these enhanced capabilities for scientific discovery. Her work brings together scientific rigor with state-of-the-art engineering innovations to simultaneously improve PNT, while revealing remarkable new applications for GNSS.

Morton’s lab-developed event-driven GNSS data acquisition systems (EDAS), designed to capture severe space weather and ionosphere disturbances of GNSS signals, which could not be handled by existing GNSS monitoring receivers. Her lab designed and built remotely-configurable, multi-GNSS, multi-band, SDR hardware using off-the-shelf components; and developed software including machine-learning algorithms for automatic event detection to trigger raw data recording during these events.

Network established. Her lab deployed these receivers worldwide. The network has enabled unprecedented studies and forecasting of ionosphere/space weather phenomena, detection of satellite oscillator anomalies, and development of advanced GNSS receivers for navigation and remote sensing under challenging conditions.

Morton’s group has made groundbreaking advances in GNSS carrier-phase processing and established theoretical performance bounds. Her group developed optimal carrier tracking loop architectures and implementations, and successfully applied the techniques to processing signals experiencing strong ionospheric scintillation for ionosphere and space weather research; radio-occultation signals traversing moist lower troposphere for weather and climate modeling; weak coherent reflected signals from ocean, land, and sea ice for precision altimetry applications; and navigation in urban canyons and on high dynamic platforms.

Morton is an expert on space weather and ionosphere monitoring. Her research findings range from climatology and morphology of ionospheric plasma irregularities to spatial, temporal and frequency domain characteristics; cause-effect relationships between solar-geomagnetic activities and GNSS signal disturbances; and radio wave propagation theory and simulation. The studies, based on data from her GNSS networks, magnetometers, radar and satellite-based measurements, cover the globe from the arctic to the equator and span an entire solar cycle.

Volunteer service. Morton has served numerous organizations with thousands of hours of volunteer service including organizing each of the ION’s large technical conferences and leading over 10 student teams participating in ION’s autonomous lawn mower and snowplow competitions, is credited as one of the co-organizing founders of the ION’s Pacific PNT conference, has served as the ION Satellite Division Chair and is the current ION President. Dr. Morton is a past recipient of the IEEE Kershner Award and the ION’s Burka and Thurlow Awards. She is a Fellow of the ION, RIN and the IEEE.

The Johannes Kepler Award recognizes and honors an individual for sustained and significant contributions to the development of satellite navigation. It is the highest honor bestowed by the ION’s Satellite Division.

Kimia Shamaei Honored with Parkinson Award

ION’s Satellite Division presented Kimia Shamaei with its Bradford W. Parkinson Award Sept. 25 for her thesis, “Exploiting Cellular Signals for Navigation: 4G to 5G.”

The Bradford W. Parkinson Award is awarded annually to an outstanding graduate student in GNSS. The award, which honors Dr. Parkinson for his leadership in establishing both the U.S. Global Positioning System and the Satellite Division of the ION, includes a personalized plaque and a $2,500 honorarium.

Any ION member who is a graduate student completing a degree program with an emphasis in GNSS technology, applications, or policy is eligible for the award. ION thanks the altruistic experts who served on this year’s selection committee.

<p>The post Jade Morton honored with ION’s Kepler Award first appeared on GPS World.</p>

Concept3D named the winners of its “Most Innovative Map” awards, which recognize higher education institutions for unique applications of the Concept3D platform.

The five award categories included Best Data Feed Integration, Best Campus Safety Initiative, Best Admissions Integration, Most Digitally Accessible Campus Map and Best Interior Mapping. In addition, honorable mentions were given for the Best Virtual Tour, Best Green Initiative, Most Prepared Initiative and Best Use of Webcams.

Here are the award winners:

• Penn State University won the Best Data Feed Integration Award for its interactive campus maps for the main University Park campus and 21 of the Commonwealth Campuses.

• Boise State University won the Best Campus Safety Initiative Award for its commitment to safety and security with the integration of a new toggle-on Night Map feature, a 3D map overlay that makes it easy for students, staff and visitors to find preferred, well-lit night routes, as well as resources including emergency phones.

• Occidental College won the Best Admissions Implementation Award for integrating a new feature — the “Locate Your Admissions Counselor” option — to the platform to help students around the country find the right point of contact for the Los Angeles-based campus.

• Texas A&M University won the Most Digitally Accessible Campus Map award for integrating a new wheelchair accessible wayfinding feature into its interactive campus map, which helps identify the most efficient and easiest routes for those using a wheelchair.

• Finally, the University of Colorado, Boulder won the Best Interior Mapping Award for adding a new feature that gives map users the ability to view the building interiors, floor by floor.

In addition, the University of Texas, Arlington received a Best Virtual Tour honorable mention; Skidmore University received a Best Green Initiative honorable mention; the University of Denver received a Most Prepared Initiative honorable mention and Michigan Tech University received a Best Use of Webcams honorable mention.

“It was quite a challenge to pare it down to the final award winners because there are so many unique applications happening in the higher education space, where interactive campus maps can be used for a wide variety of uses and by different departments,” said Gordon Boyes, CEO of Concept3D. “It’s really exciting to see how the higher education professionals are able to use the technology to serve their communities and to note how the awards reflect higher education trends that we’re seeing across the board, from greater digital accessibility to IoT.”

<p>The post Concept3D reveals 2019 innovative map award winners first appeared on GPS World.</p>