GPS World

Category: Autonomous

  • Cleared for the dirt: How robotic rovers are revolutionizing military runway assessment

    Cleared for the dirt: How robotic rovers are revolutionizing military runway assessment

    Tactical air-lifters such as the Airbus A400M, Lockheed C-130 and Boeing C-17 require precise runway roughness assessments to operate safely on unpaved surfaces. An autonomous rover system developed at the Royal Military Academy of Belgium uses RTK/PPK GNSS positioning and sensor fusion to deliver centimeter-level height measurements, drastically reducing survey time. The system provides a practical solution for rapid runway certification across military operations and humanitarian response missions.

    Unpaved runway assessment

    The Airbus A400M Atlas, the Lockheed C-130 Hercules and the Boeing C-17 Globemaster III routinely operate from unpaved runways in harsh environments far from established infrastructure. Before these aircraft can safely land, flight crews require accurate runway roughness data to assess whether the surface meets operational limits. This assessment relies on precise, quantitative measurements of the runway’s surface characteristics — a task that traditionally requires specialized survey teams and hours of manual work with GNSS equipment, resources that are often unavailable in high-tempo tactical or emergency response scenarios.

    The challenge is particularly acute because different aircraft have specific roughness tolerances. The A400M uses an equivalent bump height (EBH) methodology, while Boeing employs its Boeing Bump Criteria. The EBH requires vertical measurement precision of ±1 cm over wavelengths ranging from 5 to 100 meters. Meeting these stringent requirements with rapid, field-deployable methods has remained an operational gap — until now.

    At the Royal Military Academy (RMA) of Belgium, we developed a novel solution to this critical challenge. Our system features a rugged, autonomous unmanned ground vehicle that can rapidly perform a centimeter-accurate runway assessment with minimal user intervention. It represents a fusion of robotics, geodesy, and advanced GNSS techniques, designed specifically for ease of use by military teams in the field. The system is called Belgian Navigational Surface Inspector (BENSI).

    FIGURE 1 shows the BENSI system during a mission at a tactical landing zone with the A400Min the background. FIGURE 2 shows the BENSI system being configured by the operator during a landing preparation.

    Figure 1 The autonomous UGV (BENSI) during a mission at a tactical landing zone with the A400M Atlas in the background.
    Figure 1 The autonomous UGV (BENSI) during a mission at a tactical landing zone with the A400M Atlas in the background.
    Figure 2 The BENSI system being configured by the operator during the beach landing preparation at Rømø, Denmark.
    Figure 2 The BENSI system being configured by the operator
    during the beach landing preparation at Rømø, Denmark.

    This article details the system’s architecture, the integration of multiple technologies that enable the stringent precision required achieved by GNSS and sensor fusion, self-driving capabilities and its successful deployment in demanding field tests. We present a military graded solution for ensuring tactical airlift safety, enabled by modern, accessible GNSS technology and robotics.

    Quantifying runway roughness

    Deployable Air Traffic Management (DATM) and Pathfinders are responsible for ensuring the safety of aircraft operations on unpaved runways. They are tasked with assessing the quality of the runway and the Runway Safety Area (RSA) to ensure that the aircraft can land safely. The pilots analyze their assessment and take the final decision to land.

    FIGURE 3 is an example of a landing zone having an unpaved runway that needs to be evaluated for landing. FIGURE 4 overviews the landing zone by mapping and indicating features of the runway that need to be considered by the pilots. An important aspect of the DATM’s assessment is the runway’s roughness, which is quantified by the EBH.

    Figure 3 An example of a tactical landing zone.
    Figure 3 An example of a tactical landing zone.

    For modern military transport aircraft operations, runway roughness assessment is a critical safety parameter. Both major manufacturers — Airbus with its EBH methodology and Boeing with its Boeing Bump Criteria — have developed sophisticated approaches to characterize runway longitudinal roughness profiles. These methods analyze height variations over wavelengths ranging from 5 to 100 meters, requiring vertical measurement precision of ±1 cm. This rigorous assessment is essential to reduce aircraft structural fatigue, minimize maintenance costs, prevent exceedance of design limit loads, and ultimately ensure safe operations. For the A400M specifically, Airbus requires EBH characterization to determine operational limitations of the aircraft’s maximum payload.

    Figure 4 A typical mapping of a landing zone showing a condensed overview of DATM’s assessment.
    Figure 4 A typical mapping of a landing zone showing a
    condensed overview of DATM’s assessment.

    Traditionally, achieving this precision would involve a painstaking survey conducted by specialists using a GNSS survey system mounted on a trolley requiring human guidance along the measurement tracks totaling more than 3 km of length. For military units like the DATM and Pathfinder teams, who often are the first on the ground, this is impractical. They need a system that is rapid, reliable, simple to operate without a surveying background, and robust enough for field conditions.

    A GNSS-Centric design

    Our solution is a two-part system designed for rapid deployment: a portable GNSS base station and autonomous rover. FIGURE 5 shows a schematic overview of the system architecture.

    Figure 5 A schematic overview of the system architecture, showing the data (NMEA) and correction (RTCM) flow between the base station, rover and operator.
    Figure 5 A schematic overview of the system architecture, showing the data (NMEA) and correction (RTCM) flow between the base station, rover and operator.

    The base station: The system’s anchor

    Housed in a compact, portable case, weighing just 2 kg including tripod and radios (as seen in FIGURE 2), it serves as the operational hub. Once set up on its lightweight tripod, it performs an automatic survey to establish its precise coordinates. Its primary role for positioning is to generate and transmit Radio Technical Commission for Maritime Services (RTCM) 3.x correction data to the rover via a robust long-range radio link (operating in the868/900MHz bands).

    Beyond its GNSS duties, the base station acts as a self-contained command center. It hosts a Wi-Fi hotspot and a web server, allowing the operator to connect with any standard tablet, smartphone or laptop. This web interface is used for mission planning, command and control of the rover, and real-time monitoring of survey progress. At the end of the mission, the operator can download the EBH data and additional quality metrics of the runway for analysis such as a summary report of the complete measurement, a gradient analysis, and a runway map highlighting zones with bumps or troughs exceeding the specified criteria.

    An autonomous, all-terrain surveyor

    The UGV is a lightweight but rugged platform chosen for its durability and open-source software architecture, which allows for deep integration of our custom navigation and control algorithms. The rover has been designed to be able to traverse rough terrain and survive in harsh weather conditions. The UGV consists of two parts, the chassis (11 kg) and the processing payload(8 kg). The heart of the rover is the processing payload, which contains a sophisticated sensor suite designed for high-precision localization and navigation.

    ■ Primary GNSS receiver. A high-grade, multi-constellation Septentrio receiver with a Calian/Tallysman GNSS antenna provides the main source of positioning information.

    ■ GNSS heading. A second Calian/Tallysman GNSS antenna, set up in a moving-base configuration, provides degree-accurate true heading, which is critical for maintaining precise track-following.

    ■ Inertial measurement unit (IMU). An industrial-grade Xsens IMU provides high-frequency data on the rover’s orientation and acceleration, bridging any brief GNSS outages, providing the sensor fusion algorithm with high-rate data, and helping to smooth the final trajectory.

    ■  Radio communication. The radio modules provide robust long-range communication with the base station operating in the 868/900MHz bands.

    ■ Wheel odometry. Encoders on the rover’s wheels provide continuous velocity information, acting as a crucial input for the sensor fusion algorithm. All sensor data is fed into an onboard mini-PC running the Robot Operating System, a flexible framework for developing robotic applications.

    Path to precision

    Achieving centimeter-level accuracy on a moving platform in challenging environments requires more than just a good GNSS receiver. Our approach is built on a robust foundation of sensor fusion and a dual processing strategy using real-time kinematic and post-processing kinematic (RTK/PPK). An extended Kalman filter (EKF) is at the core of the rover’s navigation software. The EKF continuously fuses data from the GNSS receivers, IMU and wheel encoders to produce a single, high-integrity “pose” (position and orientation) estimate.

    For runway surveying, we employ two modes of GNSS processing:

    RTK. During the mission, the rover uses the RTCM corrections from the base station to compute a centimeter-accurate position in real-time. This is used for autonomous navigation, allowing the rover to follow its generated mission plan configured by the operator with high precision.

    PPK. While RTK provides excellent real-time results, the most demanding applications benefit from post-processing. Both the rover and the base station log all raw GNSS observables during the mission. After the survey is complete, these raw data files are processed together which allows for more rigorous quality control and can often resolve ambiguities or fix cycle slips that were not solvable in real-time, providing the definitive, highest accuracy trajectory for the EBH analysis.

    A final crucial step is extracting the height profile for each EBH track and subsequently transforming and reformatting this data for Airbus’ AssurTool. The step also is automated and carried out by the software. It takes care of the following:

    ■ The conversion of the geodetic coordinates (latitude, longitude, and height above the World Geodetic System 1984 [WGS84] ellipsoid) to Universal Transverse Mercator plane coordinates and orthometric heights (heights relative to a geoid).

    ■ The extraction of the height profile of each EBH track.

    ■ Quality control of the precision of the height profile flags tracks that do not meet the required accuracy or show inconsistencies.

    ■ The transformation and reformatting of this data for Airbus’ AssurTool.

    Self-driving capabilities

    The rover uses a navigation framework with a custom planner for generating smooth, curved paths that match the rover’s turning capabilities and steers the rover using a controller based on the Regulated Pure Pursuit tracking algorithm. A specialized lane-generation algorithm creates optimal survey patterns from runway corner points, with behavior-tree recovery strategies for robust operation.

    FIGURE 6 shows a typical EBH survey pattern generated from the mission plan and executed by the rover and a depiction of how the rover plans the smooth curved path between the lanes.

    Figure 6 Features of the navigation framework used for planning the EBH tracks. (a) A typical EBH survey pattern generated from the mission plan and executed by the rover. (b) A depiction of how the rover plans the smooth curved path between the lanes.
    Figure 6 Features of the navigation framework used for planning the EBH tracks. (a) A typical EBH survey pattern generated from the mission plan and executed by the rover. (b) A depiction of how the rover plans the smooth curved path between the lanes.

    A streamlined workflow

    The system was designed from the ground up to be operated by non-surveyors. A typical mission workflow is as follows:

    Setup. The operator places the base station on a tripod near the runway and unfolds the rover. The entire hardware setup takes less than 10 minutes.

    Mission planning. Using a ruggedized tablet (or any other device with a web browser), the operator connects to the base station’s WiFi and opens the web interface. They define the runway by entering the coordinates of the runway’s corners. The software automatically calculates the EBH lines based on the required spacing. FIGURE 7a shows the user interface displayed on a tablet, showing the EBH mission configuration page.

    Figure 7a The user interface of the web application.
    Figure 7a The user interface displayed on a tablet, showing the EBH mission configuration.

    Execution. The operator initiates the mission, and the UGV autonomously navigates to the start of the first line and begins the survey. The operator can monitor and control the rover’s progress, position, and GNSS quality status in real-time on the web interface. FIGURE 7b shows the user interface displayed on a tablet, showing the rover control, the real-time status of the UGV and the measurements.

    Figure 7b The tablet showing the rover control and the real-time status of the UGV and the EBH results.
    Figure 7b The tablet showing the rover control and the real-time status of the UGV and the EBH results.

    Data retrieval. Upon completion, the rover returns to the base station. The system automatically processes the data, producing downloadable files formatted for direct import into Airbus’ AssurTool and additional useful quality metrics for the operator. These consist of a summary report of the complete measurement, a gradient analysis, and a runway map highlighting zones with bumps or troughs exceeding the specified criteria.

    Analyzing the data

    Once the rover completes its survey and returns to the base station, the system automatically initiates post-processing of the collected data. This critical step validates the quality of every measurement and generates operator-ready outputs for both Airbus’ AssurTool and field assessment.

    The post-processing pipeline applies rigorous quality criteria to each survey line. Lines failing these criteria are automatically flagged with detailed diagnostics explaining the cause.

    For operational decision-making, the system generates a comprehensive visualization report. The operators receive planimetric maps showing the height profile plots and a detailed gradient analysis identifying critical slope transitions. A key capability is the generation of a 3D interpolated height map of the entire runway surface. This color-coded surface map provides an intuitive view of the runway’s topography, clearly highlighting zones with excessive bumps, depressions, or gradient anomalies that facilitates the assessment of the runway.

    These analysis reports are accessible through the web interface for immediate download to the operator’s tablet. FIGURES 8 shows examples of the visualization report.

    Figure 8a 2D height and gradient contour maps of two surfaces generated by the BENSI system. (a) A height contour map of two landing zone (LZ) surfaces automatically generated by the BENSI system.
    Figure 8a 2D height and gradient contour maps of two surfaces generated by the BENSI system. (a) A height contour map of two landing zone (LZ) surfaces automatically generated by the BENSI system.
    Figure 8b A gradient contour map of two LZ surfaces automatically generated by the BENSI system.
    Figure 8b A gradient contour map of two LZ surfaces automatically generated by the BENSI system.

    Proven performance

    The UGV system is a mature prototype that has been validated in numerous international military exercises. It has successfully surveyed tactical landing zones in varied environments, from the desert strips of Yuma, Arizona, and 29 Palms, California, to the sandy shores of Denmark and fields in France, Portugal and Italy. In all tests, the system has consistently delivered the sub-centimeter height precision required for A400M EBH certification.

    2025 Rømø Head-to-Head Trial. During beach-landing preparations in August 2025, our autonomous rover and a manual system (human-guided trolley) using a professional GNSS survey system ran side-by-side on a 1 000m landing zone on the Rømø beach in Denmark. The BENSI solution matched the manual survey system height profile with a standard deviation of 8mm and demonstrated significantly better lane-tracking consistency (mean deviation: 8,5 cm vs 16 cm and deviation error: 3 cm vs 9 cm). FIGURE 9 shows the height-error distribution between the BENSI system and the manual survey system at Rømø, Denmark.

    Figure 9 Height-error distribution between the BENSI system and the manual survey system at Rømø, Denmark.
    Figure 9 Height-error distribution between the BENSI system and the manual survey system at Rømø, Denmark.

    Rapid humanitarian response

    While BENSI was conceived for tactical airlift operations, its capabilities extend naturally to humanitarian assistance and disaster-relief missions. Belgium’s civil rapid-response unit Belgian First Aid & Support Team (B-FAST) routinely deploys doctors, paramedics, firefighters, and other professionals worldwide following earthquakes, floods, or epidemics. Leveraging the A400M’s ability to land on short, unpaved strips away from congested or contested airfields drastically cuts transit times — but only if the runway’s condition can be certified quickly.

    The BENSI systems enables a DATM team to quickly relay an EBH report and awareness map of the immediate area to the inbound aircrew. This rapid assessment unlocks critical early access for life-saving medical supplies and personnel when every hour counts.

    Conclusion and the Road Ahead

    The fusion of autonomous robotics and high-precision GNSS offers a powerful solution to the critical challenge of certifying unpaved runways. Our system saves valuable time, reduces the burden on specialized personnel, and provides objective, high-quality data that directly enhances the safety of tactical airlift operations.

    Development is ongoing. Our current efforts focus on several key areas:

    ■  Improving navigation in degraded environments. We are exploring tighter coupling between the GNSS and IMU to provide more robust navigation through areas of poor satellite visibility.

    ■ RSA assessment. We are experimenting with integrating a lidar sensor to generate a 3D point cloud of the runway and its surroundings. This will automate obstacle detection and the assessment of the RSA, though we are carefully working to mitigate potential electromagnetic interference from the lidar that can interfere with GNSS reception.

    ■ Handheld corner point device. To further improve absolute accuracy, we are developing a small, handheld device that uses RTK corrections from the base station, allowing operators to mark the runway corners with centimeter-level precision.

    This project demonstrates a clear application of GNSS technology in a demanding military aviation context, with broader implications for any field requiring rapid and precise surface profiling, from civil engineering to disaster response.

    Development Team

    ■ Pieterjan De Meulemeester ([email protected]) is a Ph.D. research engineer at the RMA of Belgium.

    ■ Alain Muls ([email protected]) is professor at the RMA of Belgium. He teaches the courses Military Satellite Based Positioning andMilitary Geodesy.

    ■ Jarno Van Audenhoven ([email protected]) is a Robotics Development and Research Engineer at the RMA of Belgium.

    ■ Pascal De Kimpe is a technician at the RMA of Belgium.

    ■ The BENSI system was developed by the R&D team at the RMA of Belgium in collaboration with Belgian Defense. The system has been successfully field-tested during international military exercises and is being evaluated for operational deployment.

    All photos courtesy of BENSI Development Team of the Royal Military Academy of Belgium

  • Modern Northstar: Starlink LEO PNT across land, air, stratosphere and Arctic Seas

    Modern Northstar: Starlink LEO PNT across land, air, stratosphere and Arctic Seas

    In January 2015, SpaceX publicly announced its plan to launch Starlink: a mega constellation of nearly 12,000 satellites in low-Earth orbit (LEO) to provide global broadband internet service. In May 2019, the first batch of 60 operational satellites were launched.

    In October 2025, Starlink surpassed 10,000 satellites (see Figure 1). This remarkable achievement means that Starlink has more satellites than all other constellations have ever launched into LEO combined.

    SpaceX is redefining global connectivity, delivering high-speed, low-latency internet anywhere on the planet1. Its civilian system, Starlink, is bridging the digital divide by providing reliable broadband in remote and underserved regions, enabling education, telemedicine and economic growth. Its defense and government variant, Starshield, is offering secure, resilient communications and rapid data transfer for military operations.

    Figure 1 The current constellation of Starlink satellites in LEO, as of January 2026.
    Figure 1 The current constellation of Starlink satellites in LEO, as of January 2026.

    In the midst of the COVID pandemic, in a quiet campus building, the ASPIN Laboratory was busy researching Starlink’s mysterious proprietary signals and the satellites’ poorly known orbits. Having demonstrated the first experimental unmanned aerial vehicle (UAV)2 and ground vehicle3 navigation using Orbcomm LEO satellites, the team’s next grand objective was to exploit Starlink’s signals of opportunity for positioning, navigation, and timing (PNT). At the 2021 ION GNSS+ Conference, the team announced a new era of LEO PNT: the first successful exploitation of Starlink for PNT4. The team designed a cognitive software-defined receiver (SDR) capable of tracking the carrier phase 5 and Doppler6 of Starlink’s so-called pilot tones along with ephemerides error correction algorithms7. The SDR and algorithms were put into test to localize a stationary receiver. Starting from an initial estimate nearly 180 km away, listening to six Starlink satellites resulted in localizing the receiver to within 10 m. This led to worldwide research to study Starlink for PNT, from deciphering Starlink’s downlink orthogonal frequency-division multiplexing (OFDM) signals8,9, to analyzing its ephemerides and timing10,11, to studying the achievable PNT performance12,13.

    This article presents the most advanced LEO PNT results to date with Starlink on four mobile platforms at geographically dispersed locations:

    1. Ground vehicle in Pennsylvania

    2. UAV in Ohio

    3. Extremely high-altitude balloon in New Mexico

    4. Maritime vessel in the Arctic near Greenland

    Exploiting Starlink LEO for PNT: The enablers

    SDR and signal analysis

    Unlike GNSS, non-cooperative LEO satellites such as Starlink do not publicly disclose the structure of their downlink signals, so users must build their own “LEO PNT Interface Control Document (ICD)”14. This can be achieved via “reverse-engineering” the signal. A more powerful approach to “reverse-engineering” is via cognitive SDRs, which employ blind signal processing techniques to learn the signals on-the-fly, regardless of the adopted modulation and multiple-access scheme15.

    The most comprehensive characterization to date of Starlink’s downlink signals for PNT was unveiled in16, utilizing the cognitive SDR approach, in which:

    1. The full OFDM beacon was revealed.

    2. Theoretical and experimental description for exploiting Starlink for PNT was provided, showing the maximum achievable carrier-to-noise density ratio (C/N0) under different scenarios: (i) pilot tones versus OFDM-based beacons and (ii) low-gain versus high-gain reception captures.

    3. A Starlink LEO PNT SDR was designed, yielding the first successful extraction of navigation observables (carrier phase, Doppler shift and code phase) from Starlink’s OFDM signals.

    4. A detailed analysis of the quality of Starlink navigation observables, including (i) signal activity and power levels and (ii) timing corrections that contaminate extracted observables along with mitigation strategies.

    Ephemeris and timing error correction

    Unlike GNSS, non-cooperative LEO satellites, such as Starlink, do not broadcast ephemeris and clock data, so users rely on public sources, such as two-line element (TLE) files. However, this data degrades over time due to orbital perturbations, limiting their effectiveness for PNT. Recent research addressed this challenge through five main approaches:

    1. Differential LEO17,18

    2. Machine learning-based orbit prediction19,20

    3. Measurement error correction21,22

    4. Closed-loop ephemeris tracking23,24

    5. Equivalent timing error compensation25,26

    The next sections will showcase experimental LEO PNT results with Starlink signals of opportunity. All experiments utilized the SDR developed in16 and the ephemerides and timing correction methods developed in26-28.

    Ground vehicle navigation in Pennsylvania

    The experiment was conducted in June 2025. The ground vehicle navigated for 3 km in 120 seconds on Interstate 79 by Pittsburgh, Pennsylvania. GNSS signals were available for the first 30 seconds but were virtually cut off for the last 90 seconds, during which the vehicle traversed a 2.25 km trajectory. The vehicle was equipped with a VectorNav VN-310 dual GNSS/INS operating with real-time kinematic (RTK) corrections and a tactical-grade inertial measurement unit (IMU), from which the vehicle’s ground truth was generated. Starlink signals were captured over all eight Ku-band downlink channels using an upward low-noise block with feed-horn (LNBF) and processed at 2.5 MSps via two NI X410 USRPs.

    Figure 2 shows the ground vehicle’s hardware setup.
    Figure 2 shows the ground vehicle’s hardware setup. shows the ground vehicle’s hardware setup.

    The vehicle navigated by fusing Doppler shift measurements from 11 Starlink satellites in a tightly-coupled fashion to aid the IMU, while altimeter measurements were fused in a loosely-coupled fashion. IMU updates were performed at a rate of 200 Hz. Starlink Doppler measurement updates were performed at a rate of 1 Hz with measurement noise variance inversely related to the received C/N0, ranging between 0.05 (m/s)2 and 6.5 (m/s)2, while altimeter updates were performed at a rate of 10 Hz with a measurement noise variance of 3 m2. The vehicle-mounted receiver and LEO satellites’ oscillator qualities were assumed to be that of an oven-controlled crystal oscillator (OCXO). A prior for the vehicle’s position and velocity was obtained from the on-board GNSS system. Starlink LEO satellites’ ephemeris errors were corrected via the equivalent timing error compensation technique in an online fashion as described in28. Each satellite’s equivalent timing error state was initialized with 0, while the relative clock drift state was initialized as the difference between the measured and predicted pseudorange rate.

    An extended Kalman filter (EKF) was used to estimate the state vector, consisting of the vehicle’s orientation, 3D position, 3D velocity and the IMU’s 3D gyroscope and accelerometer biases along with the relative clock drift error between the receiver and each LEO satellite. The Starlink satellites’ orbits were generated by propagating TLE files with SGP4 for the duration of the experiment. The navigation solution was generated using three approaches:

    1. Unaided IMU: The vehicle navigates via open-loop IMU measurements when GNSS measurements are unavailable.

    2. LEO-aided IMU with TLE+SGP4 ephemerides: The vehicle fuses LEO measurements with IMU and altimeter measurements while incorporating TLE+SGP4 ephemerides in the navigation filter.

    3. LEO-aided IMU with online ephemerides corrections: The vehicle fuses LEO measurements with IMU and altimeter measurements. Starting with TLE+SGP4 ephemerides, the navigation filter estimates an equivalent timing error for each satellite as described in28.

    Figure 3 shows the Starlink satellite trajectories, as well as the vehicle’s ground truth and estimated trajectories with the three navigation approaches. The unaided IMU solution drifted to a 3D position root mean squared error (RMSE) of 258 m from the truth trajectory. The LEO-aided IMU solution that incorporated the erroneous TLE+SGP4 ephemerides resulted in a 3D position RMSE of 150 m, while the navigation solution employing the online ephemeris correction method resulted in an RMSE of 8.41 m. Table 1 summarizes the navigation results.
    Figure 3 shows the Starlink satellite trajectories, as well as the vehicle’s ground truth and estimated trajectories with the three navigation approaches. The unaided IMU solution drifted to a 3D position root mean squared error (RMSE) of 258 m from the truth trajectory. The LEO-aided IMU solution that incorporated the erroneous TLE+SGP4 ephemerides resulted in a 3D position RMSE of 150 m, while the navigation solution employing the online ephemeris correction method resulted in an RMSE of 8.41 m.
    Table 1 summarizes the navigation results.
    Table 1 summarizes the navigation results.

    UAV navigation in Ohio

    The experiment was conducted in August 2025. A DJI M600 UAV navigated for 500 m in 75 seconds in Columbus, Ohio. GNSS signals were available for the first 20 seconds of the experiment but were virtually cut off for the last 55 seconds, during which the UAV traversed a 370 m trajectory. The UAV was equipped with a VectorNav VN-310 dual GNSS/INS operating with RTK corrections and a tactical-grade IMU, from which the UAV’s ground truth was generated. Starlink signals were captured from the 4 low-side Ku-band channels using an upward LNBF and processed at 2.5 MSps via an NI 2955 USRP. Figure 4 shows the UAV’s hardware setup.

    Figure 4 UAV’s hardware setup.
    Figure 4 UAV’s hardware setup.

    The UAV navigated by fusing Doppler shift measurements from nine Starlink satellites in a tightly-coupled fashion to aid the IMU, while altimeter measurements were fused in a loosely-coupled fashion. IMU updates were performed at a rate of 200 Hz. Starlink Doppler measurement updates were performed at a rate of 1 Hz with measurement noise variance inversely related to the received C/N0, ranging between 0.09 (m/s)2  and 6.75 (m/s)2, while altimeter updates were performed at a rate of 10 Hz with a measurement noise variance of 3 m2. The UAV-mounted receiver and LEO satellites’ oscillator qualities were assumed to be that of an OCXO. A prior for the UAV position and velocity was obtained from the UAV’s on-board GNSS system. Starlink LEO satellites’ ephemeris errors were corrected via the equivalent timing error compensation technique in an online fashion as described in 28. Each satellite’s equivalent timing error state was initialized with 0, while the relative clock drift state was initialized as the difference between the measured and predicted pseudorange rate.

    An EKF was used to estimate the state vector, consisting of the UAV’s orientation, 3D position, 3D velocity and the IMU’s 3D gyroscope and accelerometer biases, along with the relative clock drift error between the receiver and each LEO satellite. The Starlink satellites’ orbits were generated by propagating TLE files with SGP4 for the duration of the experiment. The navigation solution was generated using the three approaches described in Section II.

    Figure 5 shows the Starlink satellite trajectories, as well as the UAV’s ground truth and estimated trajectories with the three different navigation approaches. The unaided IMU solution drifted to a 3D position RMSE of 46.51 m from the truth trajectory. The LEO-aided IMU solution that incorporated the erroneous TLE+SGP4 ephemerides resulted in a 3D position RMSE of 17.82 m, while the navigation solution employing the online ephemeris correction method resulted in an RMSE of 8.15 m. Table 2 summarizes the navigation results.

    Figure 5 Experimental results of Doppler-based UAV navigation with Starlink: (a) trajectories of the nine Starlink satellites used to navigate the UAV and (b) UAV’s trajectory (blue) and estimated trajectories via the unaided IMU solution (red) and LEO-aided IMU solutions when incorporating the (i) uncorrected TLE+SGP4 ephemerides (orange) and (ii) online ephemeris correction (green).
    Figure 5 Experimental results of Doppler-based UAV navigation with Starlink: (a) trajectories of the nine Starlink satellites used to navigate the UAV and (b) UAV’s trajectory (blue) and estimated trajectories via the unaided IMU solution (red) and LEO-aided IMU solutions when incorporating the (i) uncorrected TLE+SGP4 ephemerides (orange) and (ii) online ephemeris correction (green).
    Table 2 Experimental results: UAV 3D position errors.
    Table 2 Experimental results: UAV 3D position errors.

    High-altitude balloon navigation in New Mexico

    The experiment was conducted in July 202429. The balloon was launched from the Moriarty Municipal Airport in Moriarty, New Mexico, and landed just south of Mountainair, New Mexico, traveling a horizontal distance of about 105 km south with a 3D distance of about 119 km. The balloon reached a peak altitude of about 25.3 km (83,128 ft) above sea level. A specific time period was studied to evaluate utilization of Doppler observables for navigation at an elevation of 82,177 ft. During this period, five different Starlink satellites were tracked over a 50-second period, during which the balloon traveled 948 m.  The balloon was equipped with a VectorNav VN-200 GNSS/INS, from which the ground truth trajectory was generated. Starlink signals were captured over two Ku-band downlink channels using an upward LNBF and processed at 2.5 MSps via two Ettus B205-mini USRPs. Figure 6 shows the balloon’s hardware setup.

    Figure 6 (a)-(c) High-altitude balloon’s hardware setup. (d) OHIO in New Mexico, left to right: Jennifer Sanderson, Zak Kassas, Will Barrett and the Icarus Balloon. (e) Balloon launch.
    Figure 6 (a)-(c) High-altitude balloon’s hardware setup. (d) OHIO in New Mexico, left to right: Jennifer Sanderson, Zak Kassas, Will Barrett and the Icarus Balloon. (e) Balloon launch.

    The balloon navigated by fusing Doppler shift measurements from five Starlink satellites and altimeter measurements via an EKF. The dynamic model of the high-altitude balloon was chosen as a velocity random walk model, with acceleration process noise spectra set to 0.5 m2/s3 in the in the East, North and 0.8 m2/s3 in the in Up directions, respectively. Starlink Doppler measurement updates were performed at a rate of 10 Hz with measurement noise variance inversely related to the received C/N0, ranging between 1.40 (m/s)2 and 7.01 (m/s)2, while altimeter updates were performed at rate of 10 Hz with a measurement noise variance of 1 m2. The process noise covariance for the clock states was constructed according to an OCXO clock quality. A prior for the balloon’s position and velocity was obtained from the on-board GNSS system. Ephemeris data for each satellite was obtained from offline SGP4-propagated TLE, with epoch time corrections made by minimizing the residuals between predicted Doppler and measured Doppler26,27.

    The EKF state vector consisted of the balloon’s 3D position and 3D velocity along with the relative clock drift error between the receiver and each LEO satellite. The navigation solution was generated using (i) an open-loop approach, which simply propagated the states via the dynamical model and (ii) the LEO+altimeter approach.

    Figure 7 shows the balloon’s ground truth and estimated trajectories with the two different navigation approaches. The open-loop solution drifted to a 3D position RMSE of 83.34 m from the truth trajectory, while the LEO-aided solution resulted in an RMSE of 12.28 m. Table 3 summarizes the navigation results.

    Figure 7 Experimental results of Doppler-based high-altitude balloon navigation with Starlink: (a) trajectories of five Starlink satellites used and (b) balloon’s trajectory (blue) and estimated trajectories via the open-loop solution (red) and LEO-aided solution (green).
    Figure 7 Experimental results of Doppler-based high-altitude balloon navigation with Starlink: (a) trajectories of five Starlink satellites used and (b) balloon’s trajectory (blue) and estimated trajectories via the open-loop solution (red) and LEO-aided solution (green).
    Table 3 Experimental results: High-altitude ballon 3D position errors.
    Table 3 Experimental results: High-altitude ballon 3D position errors.

    Maritime navigation in the Arctic

    The experiment was conducted in August 202430. The vessel navigated for 8.5 km in 20 minutes off the shore of Baffin Island, Nunavut, Canada. Starlink signals were captured over the third Ku-band downlink channel using an upward LNBF and processed at 2.5 MSps via a B205-mini USRP and a Raspberry Pi 4. Figure 8 shows the vessel’s hardware setup.

    Figure 8 Vessel’s hardware setup.
    Figure 8 Vessel’s hardware setup.

    The vessel navigated by fusing Doppler shift measurements from 12 Starlink satellites and altimeter data via an EKF. The dynamic model of the vessel was chosen as a velocity random walk model. Starlink Doppler measurement and altimeter data updates were both performed at a rate of 10 Hz with measurement noise variances of 4.5 (m/s)2 and 3 m2, respectively. The vessel-mounted receiver and the LEO satellites’ oscillator qualities were assumed to be that of an OCXO. The vessel’s position states were initialized from the true position obtained from the on-board GNSS system. The velocity was initialized from the true velocity but with a 10˚ clockwise error with respect to the vessel’s direction-of-motion. The Starlink satellites’ orbits were generated by propagating TLE files with SGP4 for the duration of the experiment. Ephemeris errors were corrected by adjusting the TLE epoch time for eachsatellite26,27 to minimize the residuals between predicted Doppler and measured Doppler.

    An EKF was used to estimate the state vector, consisting of the vessel’s 3D position, 3D velocity and the relative clock drift errors between the receiver and each LEO satellite. The navigation solution was generated via two approaches: (i) using only altimeter data and (ii) using LEO Doppler fused with altimeter data.

    Figure 9 shows the Starlink satellite trajectories, as well as the vessel’s ground truth and estimated trajectories with the two navigation approaches. The altimeter-only solution drifted to a 3D position RMSE of 846 m from the truth trajectory. The LEO+altimeter solution resulted in a 3D position RMSE of 123 m. Table 4 summarizes the navigation results.

    Figure 9 Experimental results of Doppler-based vessel navigation with Starlink: (a) trajectories of the 12 Starlink satellites used to navigate the vessel and (b) vessel’s true trajectory (blue) and estimated trajectories using (i) only an altimeter (red) and (ii) using LEO + altimeter (green).
    Figure 9 Experimental results of Doppler-based vessel navigation with Starlink: (a) trajectories of the 12 Starlink satellites used to navigate the vessel and (b) vessel’s true trajectory (blue) and estimated trajectories using (i) only an altimeter (red) and (ii) using LEO + altimeter (green).
    Table 4 Experimental results: Vessel 3D position errors.
    Table 4 Experimental results: Vessel 3D position errors.

    Acknowledgments

    This work was supported in part by the Office of Naval Research (ONR) under Grants N00014-22-1-2242 and N00014-22-1-2115, in part by the Air Force Office of Scientific Research (AFOSR) under Grant FA9550-22-1-0476, in part by the U.S. Department of Transportation under Grant 69A3552348327 for the CARMEN+ University Transportation Center, in part by The Aerospace Corporation under Award 4400000428, and in part by the Laboratory Directed Research and Development program at Sandia National Laboratories under award 2543953. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DENA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

    The authors would like to thank Vasilios Konstantacos, Jackson Morris, Ethan Shaw, Khaled Hamil, Aiden Short and Andrew Ye for constructing the balloon’s payload; Mark Andrews for supervising the payload design; and Prabodh Jhaveri, Danny Bowman, Mike Fleigle and Justin LaPierre for helping with launch and recovery of the balloon. The authors would also like to thank The Explorers Club and Adventure Canada for their help with data collection in the Arcitc. The authors would like to thank VectorNav for supplying the VN-200.

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    2. J. Morales, J. Khalife, A. Abdallah, C. Ardito, and Z. Kassas, “Inertial navigation system aiding with Orbcomm LEO satellite Doppler measurements,” in Proceedings of ION GNSS+ Conference, pp. 2718-2725, 2018.

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    13. Z. Kassas and J. Saroufim, “LEO PNT frameworks for non-cooperative satellites with poorly known ephemerides: open-loop SGP4, tracking, and differential,” IEEE Aerospace and Electronic Systems Magazine, (40)1, pp. 46-71, 2025.

    14. S. Kozhaya, H. Kanj, and Z. Kassas, “Multi-constellation blind beacon estimation, Doppler tracking, and opportunistic positioning with OneWeb, Starlink, Iridium NEXT, and Orbcomm LEO satellites,” in Proceedings of IEEE/ION Position, Location, and Navigation Symposium, pp. 1184-1195, 2023.

    15. S. Kozhaya, S. Hayek, and Z. Kassas, “Cognitive beacon estimation of unknown LEO satellites signals of opportunity for PNT,” IEEE Journal on Selected Areas in Communications, pp. 1-16, 2026, in-press.

    16. S. Kozhaya, J. Saroufim, and Z. Kassas, “Unveiling Starlink for PNT,” NAVIGATION, Journal of the Institute of Navigation, (72)1, pp. 1-35, 2026.

    17. J. Khalife and Z. Kassas, “Performance-driven design of carrier phase differential navigation frameworks with megaconstellation LEO satellites,” IEEE Transactions on Aerospace and Electronic Systems, (59)3, pp. 2947–2966, 2023.

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    23. N. Khairallah and Z. Kassas, “Ephemeris tracking and error propagation analysis of LEO satellites with application to opportunistic navigation,” IEEE Transactions on Aerospace and Electronic Systems, (60)2, pp. 1242–1259, 2024.

    24. S. Kozhaya, J. Saroufim, S. Hayek, P. El-Kouba, and Z. Kassas, “Light will guide you: passive joint DOA/FOA sensing, tracking, and navigation with unknown LEO satellites,” in Proceedings of IEEE/ION Position, Location, and Navigation Symposium, pp. 716–727, 2025.

    25. Y. Du, H. Qin, and C. Zhao, “LEO satellites/INS integrated positioning framework considering orbit errors based on FKF,” IEEE Transactions on Instrumentation and Measurement, (73), pp. 1-14, 2024.

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    28. S. Hayek and Z. Kassas, “A reduced-order model of simultaneous tracking and navigation with LEO satellites”, IEEE Aerospace and Electronic Systems Magazine, in preparation.

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    Authors

    Zaher (Zak) M. Kassas is the TRC Endowed Chair in Intelligent Transportation Systems and Professor of Electrical and Computer Engineering at The Ohio State University (OSU). He is also Director of the Autonomous Systems Perception, Intelligence, & Navigation (ASPIN) Laboratory and Director of the U.S. Department of Transportation Center for Automated Vehicles Research with Multimodal AssurEd Navigation (CARMEN).

    Samer Hayek is a Ph.D. student at OSU and member of the ASPIN Laboratory.

    Will Barrett was a member of the ASPIN Laboratory.

    Sharbel Kozhaya is a Senior Research Associate at the ASPIN Laboratory.

    Paul El-Kouba is a Ph.D. student at OSU and member of the ASPIN Laboratory.

    Faezeh Mooseli is a Ph.D. student at OSU and member of the ASPIN Laboratory.

    Jennifer Sanderson is a Ph.D. student at OSU and member of the ASPIN Laboratory. She is also an R&D Engineer with Sandia National Laboratories.

    Joe Saroufim is a Ph.D. student at OSU and member of the ASPIN Laboratory.

  • AirData expands global access to drone fleet platform with 8 new languages

    AirData expands global access to drone fleet platform with 8 new languages

    The full platform experience is now available in English, Spanish, Portuguese, French, German, Italian, Japanese and Hebrew.

    AirData, a drone fleet data management platform used by organizations worldwide, today announced that its platform is now available in eight languages across both the web application and mobile apps. Supported languages include English, Spanish, Portuguese, French, German, Italian, Japanese and Hebrew.

    AirData is used by a wide range of commercial, public safety and government drone programs, helping operators manage daily operations, reporting and compliance across distributed teams. The addition of platform translation reflects efforts to improve accessibility and usability to meet demand from international customers.

    AirData automatically displays the platform interface in a supported language, with no manual configuration, plugins or external translation tools required. The default language is determined by the preferred language settings of the user’s device or browser.

    The translated interface supports navigation and workflows across daily drone fleet operations and is available across the AirData web application and mobile apps. AirData plans to continue expanding localized support over time, including additional languages, region-specific regulations, compliance requirements, and airspace considerations.

    An AirData wind map aids in drone fleet planning. (Image: AirData)
    An AirData wind map aids in drone fleet planning. (Image: AirData)
  • ARK Electronics launches GNSS magnetometer unit for autonomous applications

    ARK Electronics launches GNSS magnetometer unit for autonomous applications

    ARK Electronics has launched the ARK DAN GPS, a U.S.-built dual-band L1/L5 GNSS and industrial magnetometer unit. The ARK DAN is designed for dependable navigation and orientation in professional drone and autonomous platform applications.

    Incorporating the u-blox DAN-F10N receiver, the system delivers resilient signal acquisition across L1, L5, E5a, and B2a frequency bands. Its integrated SAW-LNA-SAW design ensures robust immunity to interference, while proprietary dual-band multipath mitigation enhances positional reliability even in complex environments.

    An onboard ST IIS2MDC magnetometer provides stable heading data, complemented by a compact 4.4 cm × 4.4 cm × 1.3 cm form factor for flexible installation. The Pixhawk-standard UART/I2C interface and 6-pin JST-GH connector simplify integration into existing flight control architectures.

    With an efficient 5 V power draw averaging just 25 mA and a visual GPS fix indicator, the NDAA-compliant ARK DAN GPS combines performance, precision, and compliance in a lightweight, 25 g package.

    Specifications

    • Dimensions: 4.4 × 4.4 × 1.3 cm
    • Weight: 25 g
    • Power: 5V; 25mA average, 44mA max
    • Frequency Bands: L1/L5/E5a/B2a

  • Jammer suspected as cause of Budva drone show disaster

    Jammer suspected as cause of Budva drone show disaster

    Drones planned for a New Year’s light show in Budva, Montenegro, were deliberately shot down, according to an analysis that Croatian company Mirnovec provided news outlet Vijesti.

    Mirnovec’s owner told Vijesti the culprit used an anti-drone jammer, a gun of the type used by security forces.

    The document “GNSS RF interference analysis: Incident during a drone show,” summarizes technical findings related to a loss of GNSS navigation during the show. About 15 seconds after the launch of 600 drones from the parking lot in front of the Port Authority began, they began to return. Some drones fell into the sea and some collided with drones taking off. After 40 seconds, the operator stopped the launch. The drones fell for another 20 seconds.

    Vijesti provides details of the incident and the data gathered and reported.

  • Trust Automation secures $490M contract from U.S. Air Force for counter-drone tech

    Trust Automation secures $490M contract from U.S. Air Force for counter-drone tech

    The U.S. Air Force has awarded Trust Automation a $490-million indefinite delivery, indefinite quantity (IDIQ) contract for the rapid research, development, prototyping, demonstration, evaluation, production and transition of unmanned and counterunmanned aircraft system (CUAS) capabilities.

    As Trust Automation looks ahead to 2026 and beyond, this program represents a pivotal step in addressing the evolving challenges of modern warfare. Work will be performed at Trust’s facility in San Luis Obispo, California, and is expected to be completed by Aug. 20, 2030.

    Image: Trust Automation
    Image: Trust Automation

    “We’re incredibly proud to have been selected for this critical C-UAS project,” said Ty Safreno, Trust’s chief executive officer. “This contract underscores our commitment to developing cutting-edge anti-drone technologies that address our most pressing security challenges, protect our armed forces and contribute to the safety of our nation.”

    Trust is a field-proven leader in defense and counter-small unmanned aircraft system (C-SUAS) technologies to directly counter UAS activity in C2 and GNSS bands. At the core of its product suite is the Small-Unmanned Air Defense System (SUADS), which delivers fixed-site, such as Air Force base defense operations centers (BDOC), and rapidly deployable protection for key facilities and units in combat zones. These systems provide layered defense with adaptable modular solutions to detect, track and defeat Group 1, 2 and 2+ aircraft threats.

    Complementing SUADS is the weapons-mountable GAT UAS Jammer, which enables personnel to directly counter UAS activity in GNSS bands. Together with a broader suite of radio frequency products, Trust equipes warfighters with scalable options to secure critical operations against evolving UAS threats.

    As Trust Automation rolls into 2026, the company remains dedicated to its mission to deliver innovative, reliable and mission-critical technologies that empower the U.S. Air Force and other defense partners to stay ahead of emerging threats.

  • Communications-PNT integration: A new architectural layer for resilient and ubiquitous navigation

    Communications-PNT integration: A new architectural layer for resilient and ubiquitous navigation

    1. Introduction

    Throughout the past several decades, GNSS has become one of the most significant technologies in modern engineering, supporting transportation, communications, finance, emergency response, and critical infrastructure [1]. Its precision, global reach, and reliability have enabled entire industries to scale in ways that would otherwise have been impossible. Yet as GNSS is used more deeply in autonomy-driven and safety-critical domains, the limitations of relying on a single-layer PNT architecture are becoming increasingly apparent.

    Urban canyons degrade satellite geometry and tracking performance; intentional and unintentional interference is now commonplace [2]; spoofing has shifted from a theoretical concern to an operational reality; and indoor environments, which are essential for robotics, logistics, and emergency services, remain largely outside GNSS’s physical reach. These challenges are not shortcomings of GNSS itself. They reflect what the system was originally designed to provide: a globally available positioning and timing reference, not the entire resilience burden for every PNT-dependent application.

    In parallel, communications technologies have undergone rapid transformation. The evolution from LTE to 5G, and soon to 6G, has introduced wider bandwidths, massive MIMO antenna arrays, improved network synchronization, and dense deployment across urban and indoor environments [3]. At the same time, LEO broadband constellations have matured into powerful satellite infrastructures capable of delivering strong signals, rapid Doppler dynamics, and frequent visibility. Although these systems were built primarily for data connectivity, their physical characteristics naturally lend themselves to positioning and timing.

    Taken together, these developments point toward a new direction for resilient PNT: a multi-layer architecture in which GNSS serves as the global reference layer and is complemented by high-power, high-dynamics LEO satellites, terrestrial 5G/6G networks and Wi-Fi systems, and a suite of onboard sensors that provide short-term stability and dead-reckoning capability. Figure 1 illustrates this emerging architecture and highlights how each layer contributes specific observables, coverage strengths, and levels of robustness. The remainder of this article examines the physical foundations of communications-based PNT, the role of LEO as an augmentation space segment, the engineering challenges inherent in multi-source navigation, and the system-level architecture that is now taking shape to deliver resilient and ubiquitous PNT.

    Figure 1. Multi-layer architecture for resilient PNT. (All figures provided by author)
    Figure 1. Multi-layer architecture for resilient PNT. (All figures provided by the author)

    2. Rationale Behind Communications–PNT Integration

    2. 1 Growing Dependence on PNT and GNSS Vulnerability

    Nearly every sector of modern life depends on GNSS-based positioning and timing. As reliance grows, exposure to GNSS limitations grows with it. Dense urban environments create severe multipath and signal blockage; jamming and spoofing incidents are now regularly reported near conflict zones and busy ports [4]; and autonomy concepts in aviation and ground mobility increasingly assume reliable PNT even when GNSS performance is degraded or unavailable.

    GNSS will remain the global reference layer, but it was never intended to carry the full burden of these mission-critical demands on its own. A complementary set of technologies is needed, systems that continue to function in GNSS-challenged environments and provide redundancy when satellite signals are unavailable, corrupted, or intermittent.

    Error! Reference source not found. illustrates this challenge in a representative urban-canyon environment. Tall buildings restrict line-of-sight to GNSS satellites and generate strong multipath reflections, resulting in weak and unreliable signals (Figure 2a). By contrast, terrestrial networks such as 5G/6G and Wi-Fi maintain strong signal levels and robust geometry because their transmitters are embedded within the built environment, often only tens or hundreds of meters away (Figure 2b). This complementary coverage is a fundamental motivation for integrating communications signals into future PNT architectures.

    Figure 2. Comparison of GNSS and terrestrial network coverage in urban canyons.
    Figure 2. Comparison of GNSS and terrestrial network coverage in urban canyons.
    2.2 Communication Networks Have Quietly Become PNT-Capable

    Modern communication networks have evolved far beyond their original purpose of data transport [5]. Several physical-layer characteristics now make 5G, Wi-Fi 7, and future 6G systems surprisingly well suited to PNT:

    • Wideband signals. Wi-Fi 7 supports 320-MHz channels and 5G FR2 offers up to 400 MHz, with multi-GHz bandwidths anticipated for 6G [6]. Wider bandwidth directly improves time-of-arrival (ToA) precision. The ToA uncertainty can be approximated by:
    • Massive MIMO. Multi-element antenna arrays estimate angle-of-arrival (AoA) and angle-of-departure (AoD), effectively turning base stations into spatial sensors capable of separating line-of-sight from multipath.
    • Dense deployment. Unlike GNSS satellites, orbiting at roughly 20,000 km, terrestrial networks are woven directly into the environment. Small cells and access points provide excellent geometry in exactly the locations where GNSS performance is weakest, including city centers, campuses, factories, and warehouses.
    • High signal power. Terrestrial signals arrive at the receiver tens of decibels stronger than GNSS, improving indoor penetration, acquisition speed, and robustness to interference.

    These features were introduced to enhance connectivity, yet they collectively create an RF landscape that is inherently PNT-capable.

    2.3 The Rise of LEO Constellations as a Complementary Space Layer

    A third major driver behind communications-enabled PNT is the rapid proliferation of LEO satellite constellations. Broadband systems such as Starlink and OneWeb, together with several emerging PNT-dedicated LEO constellations, offer distinct advantages [7]:

    • Stronger received power. LEO satellites operate at altitudes of roughly 500–1,200 km, far closer than GNSS satellites at 20,000 km or higher, resulting in significantly stronger received signals.
    • Rapid Doppler dynamics. The relative motion of LEO satellites produces large, fast-varying Doppler shifts, which improve observability of user velocity and, over short intervals, position.
    • Large constellation sizes. Hundreds or thousands of satellites create rich geometry and frequent visibility, enhancing availability and resilience.

    Although many LEO systems were designed primarily for communications, their signals can already be exploited opportunistically for positioning and timing. Purpose-built LEO-PNT systems extend these capabilities by offering wideband navigation signals, multi-frequency operation, and security features intended specifically for resilient PNT [7].

    These characteristics make LEO a natural augmentation layer, strengthening GNSS performance and providing additional robustness in degraded, obstructed, or contested environments.

    3. Technical Foundations of Communications-Based PNT

    Modern communication and LEO satellite systems provide a diverse set of physical-layer measurements that can be fused with GNSS to create a resilient, multi-layer PNT solution. These observables go well beyond traditional GNSS code and carrier measurements and include Doppler, ranging, time-of-arrival, round-trip time, angle-of-arrival, angle-of-departure, and received signal strength. Figure 3 summarizes this heterogeneous measurement landscape and shows how each layer contributes distinct observables to the fusion engine.

    Figure 3. PNT measurement diversity across GNSS, LEO-PNT, and terrestrial networks.
    Figure 3. PNT measurement diversity across GNSS, LEO-PNT, and terrestrial networks.
    3.1 High-Resolution Ranging from Wideband Waveforms

    Ranging accuracy is fundamentally linked to signal bandwidth. GNSS signals typically occupy 1–20 MHz, whereas modern communication waveforms may span hundreds of megahertz. Wider bandwidth enables finer temporal resolution, allowing receivers to separate closely spaced multipath components and improve time-of-arrival (ToA) precision [6].

    In practice, Wi-Fi 7 and 5G FR2 waveforms can support sub-meter ranging in favorable conditions and substantially enhance relative positioning indoors and in dense urban environments. Techniques such as two-way ranging, cooperative localization, and inertial smoothing can extend performance even further. As shown in Error! Reference source not found., these wideband ToA and RTT observables form an essential input to the PNT measurement fusion layer.

    3.2 Spatial Sensing with Massive MIMO

    Massive MIMO arrays are one of the most powerful enablers of communications-based PNT. By comparing the phase and amplitude across many antenna elements, base stations estimate angles of arrival (AoA) and departure (AoD), turning terrestrial infrastructure into distributed RF sensor arrays [8].

    Angle-based measurements offer several important benefits:

    • Improved localization geometry in 3D urban canyons
    • Ability to distinguish line-of-sight (LOS) from multipath
    • High update rates suitable for UAVs and advanced air mobility (AAM) platforms

    A simplified Cramér–Rao lower bound (CRLB) illustrates how antenna geometry and signal power influence the accuracy of AoA estimation:

    3.3 Infrastructure Density and Geometric Strength

    From a PNT perspective, measurement geometry can be as important as measurement precision. Dense deployments of base stations, small cells, and access points give 5G, 6G, and Wi-Fi networks inherently strong geometric diversity, especially in environments where GNSS geometry collapses.

    In indoor settings or street canyons, a receiver may have ten or more RF sources within a few hundred meters. This density improves dilution of precision (DOP), increases redundancy, and enables fallback positioning even when GNSS availability drops to zero. Within the multi-layer architecture described in Figure 1, terrestrial networks therefore provide crucial observability in GNSS-restricted environments.

    3.4 High Signal Power and Robust Tracking

    Terrestrial and LEO communication signals enjoy a link-budget advantage of roughly 50–100 dB over GNSS. This additional power yields several practical benefits:

    • Better performance with small or non-ideal antennas
    • Increased resilience to interference and jamming
    • Faster acquisition and re-acquisition after outages
    • More reliable tracking under fast dynamics or partial obstruction

    In many scenarios, 5G, Wi-Fi, and LEO signals remain trackable long after GNSS signals fall below usable thresholds, providing essential continuity for navigation filters and multi-sensor fusion engines.

    3.5 Timing and Synchronization in Communication Networks

    Modern wireless networks rely on tight synchronization for scheduling, beamforming, and coordinated MIMO. They obtain timing from GNSS, fiber distribution, and packet-based protocols such as IEEE 1588 Precision Time Protocol (PTP) [9]. As these timing infrastructures mature, communication networks increasingly become timing providers rather than solely timing consumers.

    Although terrestrial networks do not yet match the long-term stability of GNSS-disciplined oscillators, they provide valuable short-term holdover and regional timing continuity. These capabilities play an important role in multi-layer PNT systems, particularly during GNSS outages.

    4. Engineering Challenges and Limitations

    Although communications-based PNT provides powerful complementary capabilities, significant engineering challenges remain. These challenges do not diminish the value of multi-layer PNT; rather, they highlight the technical rigor required to deploy these systems reliably on a scale.

    4.1 Multipath and Non-Line-of-Sight Propagation

    For terrestrial PNT, multipath and non-LOS propagation remain the dominant contributors to ranging and angle errors. Buildings, vehicles, reflective indoor structures, and metallic industrial environments introduce secondary paths that bias ToA, RTT, AoA, and Doppler measurements. A simplified model of multipath-induced ToA bias is:

    Massive MIMO beamforming, high-resolution channel estimation, and machine-learning LOS classifiers can mitigate these errors, but performance is highly environment-dependent and cannot be guaranteed in all cases. Figure 3, introduced earlier, highlights how diversity in measurement types helps reduce susceptibility to any single error mechanism.

    4.2 Synchronization Constraints and Timing Drift

    Communication networks require precise time alignment for scheduling, beamforming, and coordinated MIMO. However, network clocks do not yet match the long-term stability of GNSS-disciplined oscillators. Backhaul delay variability, oscillator drift, and partial GNSS visibility at base stations introduce timing uncertainty that must be explicitly modeled in a PNT fusion engine.

    Figure 4 illustrates timing error growth during a GNSS outage, comparing:

    • GNSS-only timing, which diverges quickly without satellite visibility
    • Network timing holdover, which slows but does not halt drift
    • Multi-layer timing fusion, which maintains the lowest error accumulation

    These behaviors demonstrate why communication-based timing is best used as a complementary layer rather than a standalone reference.

    Figure 4. Timing error comparison during a GNSS timing outage.

    4.3 Waveform and Structural Limitations

    Modern communication waveforms such as OFDM were optimized for throughput and spectral efficiency, not navigation. Several characteristics constrain raw positioning performance:

    • Finite pilot density limits effective ranging bandwidth
    • High peak-to-average power ratio (PAPR) stresses nonlinear receivers
    • Cyclic prefix duration restricts ToA resolution
    • TDD reciprocity assumptions introduce calibration-dependent biases

    4.4 Coverage Variability and Regulatory Constraints

    Terrestrial network density varies sharply by geography. Urban cores, industrial sites, and indoor campuses enjoy strong 5G/6G and Wi-Fi coverage, whereas rural, maritime, and mountainous regions may see limited improvements without LEO-PNT augmentation. Spectrum policy, privacy rules, and operator-controlled access to timing and positioning features further constrain how widely these capabilities can be exposed. Figure 5 summarizes the relative contribution of each PNT layer—GNSS, LEO-PNT, terrestrial networks, and onboard sensors—across open-sky, urban, and indoor environments.

    Figure 5. Relative contribution of PNT layers across operational environments.

    4.5 Security and integrity

    As communication signals begin supporting navigation functions, they must meet higher standards for robustness, integrity, and security. PNT observables are vulnerable to spoofing, replay, meaconing and cyber-attacks on timing sources [10]. GNSS experience demonstrates the value of:

    • Cross-layer consistency checks
    • Cryptographic authentication
    • Fault detection and exclusion (FDE)
    • Monitoring for anomalies in Doppler, timing, or angle domains
    • Redundancy across multiple constellations and layers.

    These functions are visualized in Figure 6, which illustrates how a multi-layer PNT system performs integrity monitoring across heterogeneous measurements.

    5. A Multi-Layer Architecture for Future PNT

    The earlier sections described why GNSS alone cannot meet emerging PNT requirements and how communications and LEO signals provide new sources of observability. Building on those foundations, Figure 1 introduces a multi-layer architecture in which GNSS, LEO-PNT, terrestrial networks, and onboard sensors cooperate to deliver resilient positioning and timing. This section outlines the role of each layer and how they integrate into a unified system.

    5.1 GNSS as the Foundational Global Layer

    GNSS will continue to provide the global reference frame, absolute positioning, and precise timing that anchor the entire architecture. Its worldwide availability, mature error modeling, and extensive user base make it the natural reference for other layers to align with whenever GNSS is available and reliable. In this sense, GNSS remains the “truth model” for time and coordinates, even as additional layers enhance resilience.

    5.2 LEO-PNT as the High-Power, High-Dynamics Space Layer

    LEO satellites provide diversity in orbit, signal power, geometry, and dynamics. Their lower altitude results in significantly stronger signals and rapid Doppler variations that improve motion observability. These characteristics reinforce GNSS performance in interference, urban canyon, and high-dynamics environments. As shown in Figure 3, LEO adds Doppler-based range-rate observables that are particularly valuable for maintaining continuity when GNSS quality fluctuates.

    5.3 Terrestrial Networks as the Urban and Indoor Layer

    5G, Wi-Fi 7, and future 6G networks form the densest PNT-capable infrastructure ever deployed. Their wideband signals, massive MIMO arrays, and strong received power position them as the dominant layer for indoor and urban navigation. Where GNSS geometry collapses, terrestrial networks provide ToA, AoA, AoD, RTT, and coverage exactly where users most often need it. Figure 5 highlights how their contribution becomes primary indoors and highly complementary in urban canyons.

    5.4 Onboard Sensors and Local References

    IMUs, odometry, barometers, cameras, radar, and lidar provide short-term stability and immediate awareness of the immediate environment, independent of external RF conditions. These sensors bridge outages and reduce reliance on any single external signal source. Their role within architecture mirrors their role in autonomy today: providing the continuity needed when GNSS, LEO, or terrestrial signals fluctuate. Together with RF observables, they form a robust solution space consistent with the measurement diversity shown in Figure 3.

    5.5 Fusion, Standards, and System Engineering

    Realizing a multi-layer PNT system is fundamentally a system-engineering effort. Success depends on:

    • Common timing and reference frameworks across GNSS, LEO, and terrestrial layers
    • Standardized quality indicators and integrity metrics
    • Interfaces that expose PNT-relevant observables from communication networks while respecting privacy and operational constraints
    • Cross-layer consistency checks that ensure no single measurement dominates unchecked

    Standards bodies, including 3GPP, IEEE and aviation authorities, are beginning to address these needs, but operationalizing multi-layer PNT at scale will require continued collaboration across industries. Figure 6 illustrates how integrity information from each layer contributes to fault detection, cross-checking, and integrity-bound estimation within the fusion engine.

    Figure 6. Integrity monitoring in multi-layer PNT architecture.
    Figure 6. Integrity monitoring in multi-layer PNT architecture.

    6. Conclusion

    The era of single-layer PNT is coming to an end. As reliance on precise positioning and timing accelerates across aviation, ground autonomy, critical infrastructure, and networked systems, GNSS alone cannot shoulder the growing resilience burden. Fortunately, a rich set of complementary technologies already surrounds us. Dense terrestrial networks, emerging LEO constellations, and increasingly capable onboard sensors provide observables that naturally augment GNSS and extend PNT into environments where satellite signals struggle.

    The opportunity now is to treat communications and PNT not as separate domains but as elements of a unified system. A multi-layer architecture — such as the one outlined in this article — offers stronger availability, improved measurement diversity, and inherent resilience against interference, outages, and environmental constraints. The key challenge ahead lies not in inventing new signals, but in system engineering: establishing shared timing frameworks, standardizing measurement interfaces, ensuring integrity across heterogeneous sources, and building trust in signals not originally designed for navigation.

    Most of the technical ingredients are already in place. The next decade will determine how effectively industry, government, research institutions, and standards bodies can integrate them into certifiable, interoperable, and widely deployable solutions. If successful, multi-layer PNT will become a foundational capability — providing trustworthy positioning and timing wherever future autonomous systems, vehicles, and critical infrastructure require it.

  • RQ-170 stealth drones tied to Venezuela operation as FCC bans foreign UAV imports

    RQ-170 stealth drones tied to Venezuela operation as FCC bans foreign UAV imports

    As the news subsides on the U.S. operation in Venezuela to capture Nicolás Maduro and his wife, attention is now turning to the legal aspects of the prosecution. Nevertheless, this military undertaking was apparently extremely complex and involved very discreet initial persistent surveillance of not only Maduro’s location but also of a large number of military installations and facilities.

    Venezuela has acquired an extensive arsenal of sophisticated Russian air defense capabilities beginning in 2011 and which were apparently recently upgraded in 2024. Heavy damage during the U.S. operation at La Carlota Air Base in Caracas, Fort Tiuna Military Complex, La Guaira Port and El Higuerote Airport appears to have overcome not only surface-to-air anti-aircraft missile systems but also Su-30 Sukhoi Flanker fighter aircraft armed with air-to-air missiles.

    And how was this accomplished? Well, likely with the help of legendary Lockheed RQ-170 Sentinel Stealth Drones. Nothing in the classified operation has been positively confirmed, but it is known that one or two of these surveillance drones were videoed returning to Naval Station Roosevelt Roads in Puerto Rico in the early morning of Jan. 3 following the U.S. attack. And piecing together earlier photos of U.S. Latin American command with an RQ-170 operations operative, pundits now believe confirm RQ-170 involvement.

    Lockheed RQ-170 stealth drone, nicknamed Wraith (Photo: USAF)
    Lockheed RQ-170 stealth drone, nicknamed Wraith (Photo: USAF)

    The 30th and 44th Reconnaissance Squadrons at Wing at Creech Air Force Base in Nevada are the only units the Air Force has confirmed to be operating RQ-170s Wraith low-observable stealth drones.

    It’s therefore quite possible that when President Trump said, “I was able to watch it in real time, and I watched every aspect of it,” that the video link may have been supplied by one or more of the RQ-170 Wraith drones circling over the action on the ground.

    Another aspect of the Venezuelan operation comes from people on the ground in Caracas who reported a number of instances of “flying bombs” which fell on targets during the U.S. operation. Video clips and numerous personal accounts apparently supported the reports that prop-powered attack drones were being crashed into ground targets, followed by big explosions.

    And previously on Dec. 16, the U.S. had a “first” for the U.S. Navy to have launched a one-way attack drone from the deck of the USS Santa Barbara in the Arabian Gulf. These earlier reports indicated that these attack drones could be launched by catapult, using rocket assist and from mobile ground vehicles.

    Therefore, it is not much of a leap to say it’s very likely that other marine and ground launch facilities in and around Venezuela dispatched many one-way, likely semi-autonomous attack-drones to take out targets prior to Delta Force being helicoptered in to capture Madura.

    Meanwhile back in the U.S., well away from military action, the Federal Communications Commission has effectively banned the sale of any new UAS or parts for UAS being imported into the U.S. The ruling was developed after the White House initiated a review aimed at protecting American security which decided “that UAS and UAS critical component parts that are produced in foreign countries pose unacceptable risks to the national security of the United States and to the safety and security of U.S. persons.”

    To somewhat clarify the situation, the FCC just added exemptions for Pentagon-approved “Blue List” drone models and parts thereof from EagleNXT, Parrot, Teledyne FLIR, Neros Technologies, Wingtra, Auterion, ModalAI, Zepher Flight Labs and AeroVironment — imports from these suppliers will be allowed through the end of 2026.

    So with new models of foreign drones being prevented from entering the U.S., the U.S. drone industry has been granted, at least for the moment, an opportunity to develop leading UAS models which will eventually outpace existing foreign drones operating in the U.S. It’s predicted that the FIFA soccer World Cup this summer will need a lot of drone coverage for security purposes — possibly a new U.S. drone home market.

    The Bell-Boeing V-22 Osprey is a complex military tilt-rotor aircraft which overcame many hurdles in its development and initial operations phase, even having some incidents during its mature field operations.

    Bell-Boeing V-22 Tilt-rotor aircraft (Photo: Boeing)
    Bell-Boeing V-22 Tilt-rotor aircraft (Photo: Boeing)

    Nevertheless, the Chinese appear to have adopted a similar design approach for the Lanying R-6000 manned/unmanned tilt-rotor 6-12 passenger eVTOL and a 2-ton-cargo transport version. The promotional video for the Chinese United Aircraft R-6000 seems to interchange shots of the V-22 in hover mode with recent R-6000 prototype system in hover flight. However, Bell-Boeing web statements disclaim any linkage with the Chinese company or its R-6000 development.

    If the name “United Aircraft” seems familiar, it’s because there was such a company in the U.S. in the 1930s, changing its name to United Technologies (Pratt & Whitney) in 1975, now RTX Corp.

    United Aircraft in China has apparently been around since 2012 and has produced a number of vertical lift aircraft, including the TD220 twin-coaxial helicopter (without tail boom). As with most large industrial companies in China, this one also seems heavily engaged with the Chinese military and is now making inroads into the civilian marketplace with a number of UAVs for various applications, leading up to the projected 550 mph Lanying R-6000, which has been depicted in low-level flight mode.

    Promotional image of eVTOL Lanying R-6000. (Photo: United Aircraft)
    Promotional image of eVTOL Lanying R-6000. (Photo: United Aircraft)

    So, a mixed bag of unmanned aircraft reports this month, ranging from drones likely used in the recent U.S. action in Venezuela, FCC rulemaking to restrict imports of foreign UAVs into the U.S., and all the way to a new potential Chinese tilt-rotor eVTOL entrant.

  • Trimble and Volatus improve precision and safety in BVLOS drone deliveries in Canada

    Trimble and Volatus improve precision and safety in BVLOS drone deliveries in Canada

    Volatus Aerospace has integrated the Trimble PX-1 RTX solution into its commercial delivery drone service to achieve accurate and robust positioning and heading.

    The Trimble module provides Volatus’ clients with a turnkey solution for highly-accurate aerial data acquisition and fully-remote drone operations in real-world missions, including beyond visual line of sight (BVLOS).

    The Trimble PX-1 RTX uses Trimble’s CenterPoint RTX corrections along with compact, high-performance GNSS-inertial hardware to deliver real-time, centimeter-level positioning and highly precise inertial-derived true heading measurements. This technology reduces operational risks associated with poor sensor performance or magnetic interference by providing enhanced positioning redundancy.

    Volatus must meet strict guidelines addressing airspace entry and exit, altitude and speed, and communication and remote identification when taking off from and landing at the Edmonton International Airport in Alberta, Canada. The flight corridor approved by Transport Canada and Nav Canada requires them to land and takeoff with precision, while staying at 50-feet altitude when crossing airplane arrival routes.

    Trimble PX-1 RTX’s precise positioning capabilities address crucial accuracy challenges for takeoff and landing, while supporting an exact flight altitude and positioning within the flight corridor. This capability enaables Volatus to remain compliant with the controlled airspace authorization from Nav Canada, a non-profit that operates the country’s civil air navigation system.

    The Trimble PX-1 RTX solution is available through Trimble sales channels.

  • Innoviz Technologies demos InnovizThree lidar at CES 2026

    Innoviz Technologies demos InnovizThree lidar at CES 2026

    Innoviz Technologies, a Tier-1 direct supplier of automotive-grade lidar sensor platforms and software stacks, is demonstrating its fully colored long-range lidar with camera at CES 2026 this week in Las Vegas.

    The InnovizThree creates a compact sensor-fusion module designed to significantly reduce OEM integration complexity. The solution combines lidar and RGB sensing in a single compact perception module, purpose-built for behind-the-windshield installations, drones, micro-robotics and humanoids.

    The consolidation of an RGB camera inside InnovizThree reinforces Innoviz’s commitment to scalable, OEM-friendly sensor-fusion perception solutions designed for series production and long-term deployment with the potential to enable faster deployment and cost saving.

    The RGB sensing capabilities are factory-aligned with the lidar, with an ability to ensure precise and consistent visual-to-lidar geometry across production units. This alignment, combined with hardware-synchronized capture, will enable reliable multi-modal sensor-fusion data correlation while reducing calibration effort during vehicle integration., the company said.

    Delivered through a single integration interface, the solution will minimize wiring, interfaces, and system complexity. This approach will reduce the overall integration burden for OEMs, which is expected to enable simpler validation processes, optimized engineering effort, lower cost and faster time-to-production.

  • Wingcopter drones conduct aerial surveys in Japan

    Wingcopter drones conduct aerial surveys in Japan

    Wingcopter’s authorized partner in Japan, ITOCHU Corporation, has signed a Memorandum of Understanding (MOU) to collaborate on the practical use of Wingcopter’s long-range drones in aerial surveying together with PASCO Corporation and YellowScan Japan.

    The companies initially plan to use the Wingcopter 198 in disaster management where drone-based surveying is playing an increasingly important role,

    • to create hazard maps and monitor ground deformation as part of effective pre-disaster prevention,
    • to gather information and assess damage in the event of a disaster, and
    • to measure terrain changes and develop recovery plans during post-disaster restoration.

    According to Wingcopter, carrying out these tasks is easier and less risky with fixed-wing drones such as the Wingcopter 198 than with traditional human or aircraft-based methods.

    About 70 percent of Japan’s land consists of mountainous and hilly terrain, with steep slopes and short, fast-flowing rivers. Conventional multicopter droneswould not be suitable for such tasks as they are limited in range and coverage compared to the Wingcopter 198.

    Image: Wingcopter
    Image: Wingcopter

    Under the MOU, YellowScan Japan’s advanced lidaer scanner Voyager will be used on the Wingcopter 198. By integrating this technology with PASCO’s extensive expertise in operational quality and safety in aerial surveying, it is possible to carry out long-distance and large-area surveys that were previously difficult to achieve without manned aircraft. 

    In a single 45-minute flight, the Wingcopter 198 can scan 1,000+ hectares, simultaneously capturing lidar and RGB data, allowing the system to generate an exceptionally high point density and precision. This makes it suitable even for demanding applications.

    The collaboration also promotes automation and labor savings in surveying tasks, contributing to sustainable development in the surveying industry and reducing disaster risks.

  • Anello launches Aerial INS at CES 2026

    Anello launches Aerial INS at CES 2026

    Anello Photonics has launched the Anello Aerial inertial navigation system (INS), a compact, high-performance inertial navigation system built around the company’s Silicon Photonics Optical Gyroscope technology and integrated with multi-band GNSS receivers.

    Anello made the announcement at CES 2026, taking place this week in Las Vegas.

    The Anello Aerial INS is built for demanding aerial platforms — including BVLOS UAS, maritime/shipborne VTOL UAS, ISR/special-mission aircraft, heavy-lift and cargo drones, and other autonomous aerial vehicles. The system is powered by an advanced EKF-based sensor fusion engine and ANELLO flight-profile-tuned algorithms, consistently delivering >98% navigation accuracy without the need for cameras or fiber-optic cables.

    The Anello Aerial INS delivers <0.5 deg/hr unaided heading drift, maintaining accurate navigation and control through high-dynamics and GNSS jamming, spoofing, or occlusion. Anello’s navigation solutions are built to deliver assured performance in fully GNSS-denied environments — whether operating over water or desert corridors, in night or low-light missions, or through fog and cloud cover — maintaining precise guidance without GPS and enhancing warfighters’ effectiveness and survivability.

    “Customers flying real missions need resilient navigation when GPS isn’t reliable,” said Mario Paniccia, co-founder and CEO of Anello Photonics. “By combining our SiPhOGs with our airborne-optimized sensor-fusion algorithms and integrated multi-band GNSS, the Anello Aerial INS delivers accurate navigation solutions in a cost-effective SWaP-friendly package. This allows UAVs to hold course through GPS jamming, multipath, spoofing, or outages using only Anello without the need for cameras or fiber-optic cables and allows the warfighter to complete their mission safely and successfully.”

    ANELLO’s full product portfolio has been developed in close collaboration with customers and verified through comprehensive integration and mission-platform testing.

    The Anello Aerial INS is available for evaluation today with production shipments beginning in the second quarter of this year. Evaluation kits include the Anello Aerial INS, cabling, drivers for PX4/ArduPilot, and a quick-start integration guide.