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Point One Navigation launches real-time INS

Image: Point One Navigation

Image: Point One Navigation

Point One Navigation has introduced the Atlas inertial navigation system (INS) designed for autonomous vehicles, mapping and other applications.

Traditional INS solutions have typically relied on extensive post-processing to reach the high precision levels needed for accurate mapping and observability applications. In contrast, Atlas can provide users with ground-truth level accuracy in real-time, which can streamline engineering workflows, significantly reduce project costs and improve operational efficiency.

Atlas is designed to be used in large fleets. It integrates a highly accurate, low-cost GNSS receiver and IMU with the Polaris RTK corrections network and Sensor Fusion algorithms. The company aims to make it easier for businesses to equip their entire autonomous fleets with high-accuracy INS.

The system features a user-friendly interface, on-device data storage and both ethernet and Wi-Fi connectivity. Field engineers can easily configure and operate Atlas using smartphones, tablets and in-car displays.

Atlas aims to drive innovation across a variety of sectors, including autonomous vehicles, robotics, mapping and photogrammetry. Its real-time capabilities and affordability can enhance the widespread deployment of ground truth-level location in fleet operations.

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Inertial Labs awarded SBIR Phase III contract for CAPSS

Image: U.S. Army logoInertial Labs has been awarded an SBIR Phase III contract by the Army Applications Laboratory of Army Futures Command. This award supports Inertial Labs development, design and fabrication of the Cannon Artillery Pointing and Sighting System (CAPSS) for potential use on the U.S. Army’s Paladin and the extended range cannon artillery (ERCA) vehicles.

The CAPSS aims to dramatically reduce weight on the target vehicle platforms by providing a digital replacement for the vehicle’s current panoramic telescope (PANTEL). The PANTEL is used as a sighting system for the gun when the fire control system is inoperable. The CAPSS prototype is being designed to physically replace the PANTEL.

CAPSS is a collection of cameras, inertial measurement units (IMUs), advanced electronics and an intuitive tablet-based user interface. Designed to digitally mirror the PANTEL, the CAPSS system allows soldiers to emulate all PANTEL functions via the tablet, bypassing the need to physically manage the telescope.

More than 400 lbs in equipment weight is eliminated by replacing the current equipment with CAPSS, which improves the vehicle’s operational efficiency. Additionally, the features integrated within CAPSS eliminate the need for warfighters to leave the vehicle cabin for typical aiming and sighting activities connected to the PANTEL setup, such as working with the auto-collimator. All of the functionalities are inherently embedded in the CAPSS, which simplifies operations.

The CAPSS camera technology is also used for semi-automated ranging capabilities. Warfighters can effortlessly zoom in on specific objects, offer estimated data based on the object’s attributes, such as size estimation, and the system will generate the estimated range to that particular object.

Looking ahead, Inertial Labs plans to continue research on optical/inertial-based GPS-denied navigation designed for land vehicles, integrating both camera systems and inertial sensor data.

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Innovation Insights: Science in paradise

Innovation Insights with Richard Langley

Innovation Insights with Richard Langley

This is an introduction to the November 2023 Innovation article, “Using GNSS Phase Reflectometry on Maui’s Haleakalā”

We’ve all seen the news reports of the terrible devastation and loss of life in the town of Lahaina on the island of Maui by a wildfire this past August. Those terrible reports jarringly contrasted with happy memories of visits to Hawaii and its paradise islands. I recalled my visit some years ago to Maui in particular. My wife and I traveled all around Maui, but we particularly enjoyed the drive up to the top of Mount Haleakalā.

Rising to just over 3,000 meters, Haleakalā is a large, active (though currently dormant) shield volcano that forms about 75% of Maui. Just below its summit there is a visitor center with informative panels describing the geology of the volcano and the flora and fauna to be found on its flanks. On the drive up, for example, you can see endangered nēnē, the Hawaiian Goose, and the threatened silversword plants, which only bloom once in their lifetimes. And the sunrise and sunset views from the summit are quite beautiful.

A few hundred meters away from the visitor center is the Haleakalā High Altitude Observatory Site — a complex informally known as “Science City.” The site accommodates various optical telescopes and other instruments, including among others the 4-meter-aperture Daniel K. Inouye Solar Telescope (the largest solar telescope in the world), a satellite laser ranging station, and the Maui Space Surveillance Complex, which consists of a suite of telescopes operated by the Department of Defense for satellite tracking.

Also at the site is an innovative system observing the ocean surface far below using the phase of GNSS signals. Not only receiving normal line-of-sight signals from satellites, this system also receives signals that are reflected by the ocean surface, a technique called GNSS reflectometry or GNSS-R. GNSS-R can be thought of as a bi-static radar, where the transmitters (the GNSS satellites) and the receiver are separated by a large distance. The receiver can be on Earth’s surface, on an aircraft or on a low-Earth-orbiting satellite. The reflected signals contain information about the surface from which they were reflected. Depending on the receiver’s location and with suitable data processing, parameters such as ground surface elevation and its variation, water level and tide height, sea state (wave height, wind speed and wind direction), soil moisture content, and even snow depth can be deduced.

Over the years, we have had a number of articles on GNSS-R in this column using different receiver platforms (April, 1999; October, 2007; October, 2009; September 2010; September 2014; and October, 2019). In this quarter’s “Innovation” column, we have an article by some members of the team who built and operate the GNSS-R system on the top of Haleakalā. They explain how the system works and some of the preliminary observations and results they have obtained. More science in paradise!

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Using GNSS Phase Reflectometry on Maui’s Haleakalā

Read Richard Langley’s introduction to this article:Innovation Insights: Science in paradise”

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.


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)

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.

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.

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.


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.

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.

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.

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.


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 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.

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 8 Phase processing results for June 21, 2021.

Figure 9 Phase processing results for June 25, 2021.

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


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 10: Phase processing results for July 1, 2021.

Figure 11 Phase processing results for July 5, 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.

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Trimble partners with HALO Trust for landmine clearance in Ukraine

Image: Trimble

Image: Trimble

Trimble has partnered with HALO Trust, a landmine-clearing non-profit organization, to help expand its demining operations across Ukraine.

The grant from the Trimble Foundation Fund will focus on strengthening the HALO Trust’s ability to locate and remove landmines, unexploded ordnance and other explosive hazards from civilian areas to create safer communities. In addition, it will allow HALO to support the Ukrainian national authorities in planning and coordinating landmine clearance activities by streamlining the mapping and data flow from the operational teams in the field to the national database.

The Russian invasion of Ukraine has left areas of the country contaminated with landmines, unexploded ordnance and improvised explosive devices. These hazards block access to farmland, impede reconstruction efforts, prevent displaced persons from returning to their homes and continue to hinder the safety of Ukrainian civilians. The Ukrainian government estimates that 174,000km2 of the country’s land may be contaminated.

More than a thousand HALO staff members are active daily, both to clear explosives in critical priority areas and to recruit and train hundreds of new staff members to help keep communities safe from dangerous weapons left behind.

Surveying and mapping technology has played a significant role in the success of HALO’s operations around the world, including in Ukraine. Over the last six years, Trimble R1 and Trimble R2 GNSS receivers along with Esri ArcGIS Survey123 software have been used by HALO to identify and clear landmines.

Trimble’s Geospatial and Positioning Services businesses provided HALO with a new deployment of 255 high-precision Trimble DA2 GNSS receivers with Trimble Catalyst corrections service, allowing HALO to modernize and transform its landmine clearance operations by providing improved accuracy for more detailed maps, streamlined data flows and increased operational efficiency and safety.

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Kosminis Vytis releases three anti-jamming products

Kosminis Vytis has released two anti-jamming receivers, the KV-AJ3 and KV-AJ3-A, and an 8-channel, controlled reception pattern antenna (CRPA), anti-jamming development kit.

Image: Kosminis Vytis

Image: Kosminis Vytis

The KV-AJ3 tri-band anti-jamming receiver combines a digital antenna control unit (DACU) and a GNSS receiver. KV-AJ3 can be used as a jamming protector for legacy receivers or as a stand-alone GNSS receiver solution.

When used with a Kosminis Vytis multi-band 4-channel antenna array, anti-jamming DACU uses the Null-Steering algorithm based on classic auto-compensator architecture and channel equalization, which eliminates the jamming signals from those processed by the receiver.

The tri-band solution decreases interferences from up to three directions in three frequency bands, including S-band, according to the company. This approach is designed to provide significantly higher protection against interference compared to single-frequency devices.

The receiver has a digital port for navigation data output. Jamming-free RF signals can also be delivered to external non-protected GNSS receivers to obtain position, velocity and time.
KV-AJ3 contains a MEMS inertial sensor, which allows for GNSS-aided INS solutions where coordinates and attitude angles are required.

Image: Kosminis Vytis

Image: Kosminis Vytis

The KV-AJ3-A is designed to provide a stable navigation signal in three frequency bands, including S-band, even in the presence of jamming and other harsh conditions. The technology is MIL-STD compliant and meets the EMI/EMC requirements for avionics.

The direction to interfering signals is determined using a phased array antenna, which can then remove jamming signals from up to three directions. The original signal is either restored and delivered to external GNSS receivers or processed by the internal receiver to obtain position data. The device can be used as a DACU for legacy receivers, or as a stand-alone GNSS receiver solution.

According to Kosminis Vytis, the key components of this anti-jamming device are based on custom ASICs that allow users to achieve high jamming suppression and SWaP. KV-AJ3-A can be used for fixed installations and land, sea and air platforms, including UAVs.

Image: Kosminis Vytis

Image: Kosminis Vytis

The 8-channel, CRPA, anti-jamming development kit is a set of instruments designed to help users add anti-jamming and anti-spoofing capabilities to their receivers.

The main development tool is NT1069x8_FMC — an 8-channel receiver board. The 8 coherent channels are based on NT1069, the RF application-specific integrated circuit (ASIC) that supports a high dynamic range of input signals.

Each channel performs amplification, down-conversion of GNSS signal to intermediate frequency (IF) and subsequent filtering and digitization by 14-bit ADC at 100 MSPS.

The board is compatible with GPS, GLONASS, Galileo, BeiDou, NavIC (IRNSS) and QZSS signals in L1, L2, L3, L5 and S bands. Each RF channel has an individual RF input with the option to feed power to an active antenna.

The board also has an embedded GNSS receiver and an up-converter, or modulator, which can provide connection to an external GNSS receiver.

Under a technology license, Kosminis Vytis provides users interested in developing their solutions based on the NT1069 chip with a reference design of anti-jamming receiver, anti-jamming algorithms and software tools for the development and evaluation of anti-jamming receivers.

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Jamming warfare and air taxis

Russian Pole-21 jammer – (photo: Defense Ministry, Russia)

Russian Pole-21 jammer – (Image: Defense Ministry, Russia)

Other than the tracked unit — a truck that appears to be a power generator and has an overall look of complexity — the thing that jumps out at you about the Russian Pole-21 jammer is that it is brisling with a huge number of antennae. The system apparently can jam almost any known communication channel and everything GNSS. It is a bit lumbering to move around, may be difficult to set up, and must be used judicially, because it may jam everything, including the Russian’s own coms and GLONASS navigation systems. So, it may be useful for disabling the enemy, but only when your own soldiers have already established visual contact with their target.

The Russians just parked one of these contraptions in southern Ukraine’s Zaporizhzhia Oblast (district), which has been the focus of the Ukrainian counteroffensive for the last few months. However, the Ukrainians found it quickly, had time to figure out what to do with it and launched a GPS-guided weapon that destroyed the inoperative Pole-21 jammer. They may have caught the system either before it was fully activated or when it had been intentionally shut down to protect Russian forces. The Pole-21 set up was found during a drone recognizance flight and the system was then taken out by a Ukrainian aircraft — perhaps a Russian Sukhoi Su-27 or Mikoyan MiG-29 — which deployed a U.S. Joint Direct Attack Munition (JDAM) to destroy the system. The UAV, the aircraft, and the JDAM all use GPS for guidance, which is perhaps a little ironic. The Ukrainians have reportedly destroyed more than four dozen other Russian jamming systems since the start of the war.

Just in case there might be reprisals, the Ukrainians have developed their own RF system that forms a 600-ft dome over the area/asset that needs protection from UAV attacks. Additionally, Western countries have supplied several jamming systems to the Ukrainian forces for more elaborate, wider range jamming protection.

Ukraine initially shot down low flying, slow Russian UAVs with rifles and large caliber anti-aircraft defense systems; however, as expensive missile systems have become available from the United States and elsewhere, it has become apparent that it is too expensive to use complex missiles to bring down large numbers of inexpensive commercial UAVs. It just does not make sense — too many UAVs and not enough expensive missiles. Ukraine has thrown large numbers of cheap UAVs — some even made from cardboard — in the other direction, at a significant cost for the missiles shot at them by Moscow’s air-defense system.

Counter-UAV (C-UAV) technology must be an alternative, and several suppliers can readily provide affordable production equipment. Tactics range from firing anti-aircraft gun batteries to intercepting drones using nets, or just crashing UAVs into interlopers, or more prevalently using electronic disruption systems — such as the DroneGun Tactical “arm-held” from DroneShield — which jams the control signals and/or GNSS guidance.

A large proportion of “attack-UAVs” are simply modified off-the-shelf commercial vehicles adapted to carry explosives, even existing conventional ordinance — read “bombs”. The UAVs themselves are built with commercial components, which have little resistance to directed jamming frequencies that overpower the RF section of the receiver. Then, there is loss of lock on the coms and/or the very low power GNSS signal — where signal processing stops, and guidance is lost.

Meanwhile, back in our much safer land of commercial aviation, progress toward the emergence of electric vertical take-off-and-landing (eVTOL) air taxis took a simple step forward, at Tampa International Airport (TPA), Florida.

TPA growth plan of the Tampa airport. (Image: FAA)

TPA growth plan of the Tampa airport. (Image: FAA)

TPA is in the midst of a significant growth spurt that began last year with moving the rental car facility off site to make more space and installing a rail link to restore access for renters to the main terminal. Upcoming is a new Airside D set of 16 additional gates, and future provisions for urban air mobility, enabling eVTOL air taxi traffic services in and around the airport to bring passengers directly to the airport from their local catchment areas. Possible locations have been identified for one or two vertiports on the airport grounds, and an airport integration plan appears to be well underway.

Last week, Volocopter, a German company, showed up at TPA with its VeloCity two-seater prototype air taxi to carry out the very first tests of a passenger-carrying UAM vehicle at a major U.S. airport. The Tampa and St. Petersburg mayors were on hand, as were airport management and others from the aviation and transportation industry.

VeloCity prototype in clear skies at Tampa. (Image: courtesy of courtesy BoxAdmin/Beau Zimmer/Velocopter)

VeloCity prototype in clear skies at Tampa. (Image: courtesy of courtesy BoxAdmin/Beau Zimmer/Velocopter)

Now TPA is a busy place, with 553 flights daily into and out of the facility, but Volocopter was able to fit into the infrastructure and perform two flight tests with the U.S. Federal Aviation Administration (FAA) to investigate aircraft downwash and outwash, along with aircraft performance in TPA’s actual environmental conditions.

Various prototypes of the VoloCity have already flown 2,000 flights. Initial operational flights will be flown by a pilot with only one passenger. However, as operational reliability is proven, a fully autonomous service is planned. Volocopter is currently focusing on certification of the VoloCity, by the European Union Aviation Safety Agency (EASA) in 2024, leading to initial service on three routes in Paris, France. Concurrent validation in cooperation with the FAA, originally submitted in 2020, is also progressing.

In conclusion, GNSS and communications jamming is escalating in and around Ukraine as the war drags on and each side tries to compensate, while efforts to revolutionize shuttle services for passengers to and from major airports in the United States has taken quite positive steps in Tampa, Florida.

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Skydio, Trimble integrate GNSS technologies for construction, utilities, transportation agencies

Skydio X10 UAV. (Image: Skydio)

Skydio’s X10 UAV. (Image: Skydio)

Skydio has entered a strategic collaboration with Trimble to create an integrated workflow of accurate data capture, visualization and analytics. The workflow is designed to address the needs of critical infrastructure industries such as surveying, mapping and inspections.

The collaboration, currently in the developmental stage, aims to offer users centimeter-level accuracy in surveying and mapping projects by integrating Skydio autonomous UAVs with Trimble GNSS receivers and software. The technology can be used by construction and utility companies, as well as state transportation agencies, to streamline workflows for greater precision and project efficiency.

Industry leaders rely on autonomous UAVs with powerful visual and thermal camera sensors, such as Skydio’s X10, for their ability to capture real-time condition reports of critical infrastructure conditions. By capturing images and geospatial data early and frequently throughout construction projects, organizations can easily ensure on-site work matches the design and reduce costly rework.

When bridge or utility site inspections need to scale, the Skydio and Trimble integration can be used to collect comprehensive data and improve the necessary workflow to identify issues early and take action to prevent failures.

According to Skydio, key benefits of the collaboration include:

Automated data integration: An automated, API-based integration enables seamless transfer of aerial imagery and metadata from Skydio Cloud to Trimble Industry Cloud. It accelerates the conversion of reality-capture data into actionable insights to improve efficiency. Further refinement and analysis of the output data can be carried out in professional surveying and mapping environments such as Trimble Business Center (TBC).

Survey-grade accuracy with Skydio X10: The X10 UAV will be fully compatible with Trimble’s GNSS receivers, allowing mutual users to achieve survey-grade accuracy in mapping missions when employing Skydio alongside Trimble’s base stations and GNSS receivers. Beyond RTK, customers will also be able to conduct PPK based corrections post-flight.

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VectorNav releases two INS

VectorNav Technologies has released two products, the VN-210-S and VN-310-S, which expand its tactical series of GNSS-aided inertial navigation systems (INS).

The VN-210-S GNSS/INS combines a tactical-grade inertial measurement unit (IMU) comprised of a 3-axis gyroscope, accelerometer, and magnetometer with a triple-frequency GNSS receiver. The integrated 448-channel GNSS receiver from Septentrio adds several capabilities, including L5 frequencies, moving baseline real-time kinematic with centimeter-level accuracy, support for Galileo OSNMA and robust interference mitigation.

These capabilities and high-quality hardware offer improved positioning performance in radio frequency-congested and GNSS-denied environments.

Image: VectorNav

Image: VectorNav

The VN-310-S dual GNSS/INS leverages VectorNav’s tactical-grade IMU and integrates two 448-channel GNSS receivers to enable GNSS-compassing for accurate heading estimations in stationary and low-dynamic operations. The VN-310-S also gains support for OSNMA and robust interference mitigation, offering reliable position data across a variety of applications and environments. 

The VN-210-S and VN-310-S are packaged in a precision milled, anodized aluminum enclosure designed to MIL standards and are IP68-rated. For ultra-low SWaP applications, VectorNav has introduced L5 capabilities to the VN-210E (embedded) when using an externally integrated L5-band GNSS receiver.  

Image: VectorNav

Image: VectorNav

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Certus Evo GNSS/INS

Image: Advanced Navigation

Image: Advanced Navigation

For demanding applications where precise navigation really matters and where size, weight and power are at a premium, Certus Evo exceeds the accuracy of many fibre optic gyroscopes while providing a very compact, lightweight and low power solution. This makes it ideal for applications such as aerial surveys where high performance is required and where flight times can be extended by minimising component weight and power consumption. The AI-based fusion algorithm takes full advantage of high-accuracy MEMS accelerometers and gyroscopes to deliver dependable performance at an affordable price point. Integration or customisation is made easy with extensive connectivity options and access to world-class technical support.

Click here to learn more.