The company positions the QLX3Gx as a market-ready receiver built around its Dragonfly Digital RF architecture, which moves many traditionally analog RF functions into the digital domain to reduce power, size and cost while retaining multi-constellation GNSS performance. The new evaluation kit is intended to let OEMs characterize power consumption and positioning behavior in their own devices before committing to volume designs. 

According to Qualinx, the QLX3Gx can operate in a 1 mW GNSS mode, with the same silicon supporting a range of power-versus-performance configurations through software. The chip is designed to track multiple constellations and bands concurrently, and to keep tracking and navigation computation on the chip rather than offloading to cloud services or host processors. It also supports authenticated Galileo signals via OSNMA to improve resilience against spoofing, with the company highlighting use cases in asset tracking, wearables and other edge devices that need long battery life as well as resistance to malicious interference. 

Qualinx is also emphasizing supply-chain and manufacturing aspects, noting that the GNSS chip is fabricated at GlobalFoundries’ facility in Dresden, Germany, as part of a broader European semiconductor footprint. A recent €20 million funding round is intended to help move the QLX3Gx family into volume production and expand its availability in international markets. 

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Built on the company’s X20 high-precision platform, the module delivers RTK-grade performance while maintaining precise GNSS-based heading even at low speeds or standstill, a key requirement for auto-steering and autonomous operation. Target sectors include precision agriculture, unmanned aerial vehicles, autonomous machinery, marine and robotics navigation. 

All-band on both antennas, with scalable corrections

The ZED-X20D tracks all major GNSS constellations on L1, L2, L5 and L6, and adds L-band reception for PPP correction services, an “all band on both antennas” approach that is intended to maximize heading availability and stability in challenging environments. To meet different accuracy and deployment needs, it works with RTK, PPP-RTK and PPP correction services, including u-blox’s PointPerfect offerings for regional and global coverage. Built-in support for Galileo E6 enables use of the free Galileo High Accuracy Service (HAS), giving equipment makers multiple options to source corrections. 

u-blox is positioning the ZED-X20D as a drop-in upgrade for existing designs by retaining the established ZED form factor and pairing the module with its ANN-MB2 all-band antenna and PointPerfect services as a turnkey high-precision bundle. The company says this combination is aimed at simplifying design, reducing system cost and accelerating mass adoption of automated and autonomous equipment across agriculture, UAVs, construction and other industrial domains. 

Security and interference resilience for trusted heading

The module includes u-blox’s end-to-end hardened security, with secure boot, signed firmware and a hardware root of trust for cryptographic material, as well as support for Galileo OSNMA and encrypted correction data. All-band frequency diversity and interference monitoring are designed to improve resilience against jamming and other RF threats, while access to high-quality GNSS measurements supports reliable post-processing and integrity monitoring—features likely to appeal to developers building safety-critical or highly automated systems on top of the new heading platform.

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The new solution, SimXTRACT, allows engineers to decompose real-world RF recordings into discrete signal components and replay them as fully controllable scenarios on Spirent simulators.

Positioning, navigation and timing (PNT) developers have traditionally been forced to choose between RF record-and-playback on one side and pure lab simulation on the other. Record-and-playback captures all the richness of the real world, but offers limited control and repeatability. Simulation provides precise control over parameters and repeatability for regression and corner-case testing, but can lack the full complexity of live-sky environments. Spirent positions SimXTRACT as a way to fuse these two approaches.

According to the company, SimXTRACT takes signals captured in the field using Spirent record-and-playback devices and performs complex signal decomposition, breaking each received signal into separate line-of-sight and multipath ray paths. Metadata such as Doppler offset, code error, power level, and angle of arrival (AoA) is retained. That decomposed representation is then converted into simulator drive files that can be loaded into Spirent GNSS simulators as fully controllable, repeatable scenarios.

“SimXTRACT revolutionizes how you can test positioning solutions. By combining real-world insights with lab-based control and repeatability, our customers will no longer have to compromise on how they test in this fast-moving technology area,” said Peter Terry-Brown, Divisional CEO of Spirent’s Positioning business, in the announcement. “SimXTRACT ensures customers get the best of both worlds, with enhanced realism delivering more accurate results, quicker issue resolution, and faster time to market.”

By reducing the amount of time and number of trips required for field data collection, Spirent says users can cut the cost and complexity of scenario capture and generation while still working with high-fidelity, real-world conditions. Developers can also analyze signal recordings, search for specific types of events or environments, and then recreate those conditions in the lab to focus troubleshooting or performance characterization.

Spirent expects SimXTRACT to be used across a broad set of sectors where high-precision PNT is critical, including automotive, chipsets, consumer devices, defense and critical infrastructure. Terry-Brown frames the tool as a way to “bring the real-world environment into every stage of your product realization process,” with the goal of improving product quality while saving time and money.

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Agriculture has entered the era of continuous PNT.

Precision agriculture is moving toward full automation. Guidance systems once treated GNSS as the entire solution; today, the industry recognizes that satellite signals are necessary but insufficient. Farms have become complex RF environments. Tree canopy, terrain, outbuildings, seasonal geometry shifts, multipath near grain elevators, interference from adjacent equipment, and the simple reality that tractors roam in and out of open-sky visibility all challenge the idea that GNSS alone can sustain continuity.

OEMs are building guidance systems that must keep machines on path even when GNSS falters. Autonomy depends on uninterrupted perception of position, velocity and attitude. That means pairing GNSS with inertial systems engineered for agricultural machines, not adapted from other domains.

In a recent conversation with Inside GNSS, Tzeno Galchev, Director, Product Marketing and Applications Engineering for Analog Devices, Inc. (ADI), described how their inertial measurement units (IMUs) are being integrated into next-generation tractors, implements, drones and robotics platforms. ADI’s engineers are focused on what really matters in the field: disciplined inertial performance, controlled lifetime drift, rugged packaging and reliable sensor fusion with GNSS. The message was unambiguous: Autonomy in agriculture can scale rapidly when inertial becomes a baseline requirement. 

The Market Reality: Why Inertial Matters Now 

Precision agriculture has matured beyond the first decade of “straight-line” GNSS guidance. Machines now operate in a wider set of field geometries, crop types and environmental constraints. Several forces are converging:

Tractors are evolving from operator-assisted systems to autonomy-ready platforms. Implements are following, including precision planters, high-clearance sprayers, and robotic harvesters. Each requires continuous PNT. A single GNSS dropout during an autonomous end-of-row turn can result in overlap, missed coverage or unsafe behavior.

Agriculture spans open sky areas and GNSS-hostile corridors. Machines pass under tree rows, within orchard canopies, beside barns or silos, or along field edges lined with windbreaks. Modern high-value crops, such as vineyards, orchards and berries, introduce dense canopy that disrupts L-band signals. Even row crops can create directional multi-path in late summer.

OEM Pressure to Deliver “Always-on” Paths

Agricultural OEMs face customer expectations shaped by the automotive sector. The question is no longer whether GNSS can deliver accuracy; it is whether the total system delivers continuity. That continuity is now a competitive differentiator. Dead-reckoning performance, not positional Root Mean Square (RMS) in open sky, shapes the user experience.

Cost Realism and the Mid-Market Explosion

Farm sizes vary globally. Not every user can justify aerospace-tier inertial systems. ADI’s view is that precision agriculture needs inertial performance that respects cost boundaries while still meeting the dynamics of field machinery: vibration, temperature cycling and shock.

“The demand is there because there’s a shortage of workforce, especially in the developed countries, and these machines make a considerable difference in the cost and efficiency of farming operations,” Galchev said. “They are replacing and reducing the number of workers needed as well as putting workers out of harm’s way.” 

The Shift to Autonomy-Grade Attitude Estimation

GNSS provides position and velocity; but many operations require continuous knowledge of roll, pitch and yaw. Sprayers use boom leveling. Planters need implement attitude to maintain depth accuracy. Drones require stable orientation in low-signal environments. INS establishes those states even when GNSS is degraded.

THE RESULT: GNSS remains the reference, but inertial is now the mechanism that closes the reliability gap.

Inertial Basics for Agricultural Platforms 

Agricultural operators rarely see inertial systems directly. They see better lines, fewer skips, improved boom stability, and smoother turns. Under the hood:

• IMUs measure angular rate and acceleration along orthogonal axes.

• Sensor fusion in an inertial navigation system (INS) uses those measurements to propagate position, velocity and attitude during GNSS gaps.

• Drift is inherent, but it can be minimized, modeled and constrained with well-tuned sensor fusion.

• GNSS resets the INS, bounding cumulative error.

• Agricultural use-cases emphasize short-to-medium duration bridging, not long-haul independent navigation.

Modern MEMS technology has reduced noise, bias instability, and temperature sensitivity to levels appropriate for automotive-grade and robotic applications. ADI’s work has focused on improving consistency across production units, strengthening environmental robustness, and integrating compensation routines at the firmware level.

Agricultural machinery introduces several complicating factors that inertial systems must handle cleanly:

• High vibration environments from diesel engines, tillage tools, and PTO-driven implements.

• Complex motion during headland turns, uneven terrain and differential traction events.

• Thermal swings, from dawn cold starts to midday heat.

• Mechanical shock, especially on implements.

• Long duty cycles, including 14 to 18 hour days in planting or harvest season.

This environment is less deterministic than automotive and more dynamic than many robotics platforms. The IMU/INS must treat vibration as a feature of the mission, not a source of error.

ADI’s Technical Approach

ADI designs inertial solutions with a focus on predictable error behavior, rugged packaging and stable sensor fusion. The company emphasizes several technical principles:

VIBRATION TOLERANCE. Farm machinery produces persistent broadband vibration. ADI considers how vibration intrinsically disturbs the sensors and ADI engineers design mechanical structures that better suppress, cancel and otherwise reduce the effect of vibration directly into the MEMS structures themselves because once vibration is allowed to pollute the sensor signal, it is too late for the INS system to do anything about it. This ensures the INS maintains the correct angular-rate and acceleration signatures even when implements shake violently.

BIAS REPEATABILITY. This is the lifetime bias drift expectation that intends to capture all unmodeled error sources and is not commonly specified in MEMS IMU datasheets. It provides a single error window that will determine the convergence times for critical estimation/filter loops. For systems that need to turn and deploy quickly, failure to anticipate and quantify these errors can limit deployment time and degrade initial heading accuracy. In their latest products, ADI has expanded their Bias Repeatability definition to include turn-on drift/settling, drift from package stress relief, electronic drift and thermal hysteresis. In parallel with expanding the coverage of this specification, ADI has reduced this metric by an order of magnitude in recently-released devices, such as the ADIS16545 and ADIS16576. 

AXIS-TO-AXIS ALIGNMENT. With tight axis-to-axis alignment out of the box and calibrated through an extensive inertial routine over multiple temperature set-points, tight alignment can be achieved only using mechanical alignment features. For tighter alignment than 0.25° one could leverage the tight axis-to-axis alignment (along with excellent bias repeatability in the accelerometer) to greatly simplify the frame alignment process. 

LINEAR, TEMPERATURE-CONTROLLED BEHAVIOR. Temperature gradients on tractors and implements are large. ADI incorporates temperature compensation models enforced at both the sensor and system level. The goal is not perfect thermal invariance, which is unrealistic in cost-sensitive segments, but predictable behavior that fusion algorithms can model accurately.

FUSION-FIRST PHILOSOPHY. ADI treats the IMU as one component of a larger PNT solution. Their systems are designed for tight integration with GNSS receivers, wheel speed sensors, magnetometers, and vehicle CAN data. Robust synchronization and time-based alignment of the inertial output simplifies system coupling. This architecture enables robust attitude estimation and velocity smoothing, especially during headlands or canopy exposure.

PREDICTABLE LIFECYCLE PERFORMANCE. Agricultural platforms must last. ADI designs for multi-season reliability and bounded long-term drift. The objective is to ensure a machine equipped with an ADI IMU behaves the same in year four as it did in year one.

“You can’t calibrate a sensor’s inherent noise performance, its stability, or its response to vibration,” Galchev said. “These unmodeled error sources directly produce error at the output, and that’s where ADI focuses on innovating at the chip level.”

This technical discipline supports the system-level view: Inertial is not a premium feature; it is a foundation for reliable GNSS-enabled autonomy.

Integration in the Field: What Engineers Face

Engineers integrating inertial systems into agricultural machines confront real-world constraints that differ from lab conditions. ADI’s field experience highlights specific patterns.

Booms flex. Toolbars vibrate. Tractor frames twist. Sensor placement often becomes a compromise. An INS may be exposed to off-axis motion uncorrelated with actual vehicle trajectory. ADI mitigates this through calibration routines, filtering strategies, and noise modeling that treat flex and vibration as signal partitions.

Implements as Independent Dynamic Systems

The implement behind a tractor behaves differently from the tractor itself. For operations like variable-rate spraying or multi-row harvesting, implement attitude, even when decoupled from tractor motion, must be sensed accurately. IMUs can be mounted on booms or frames to track these dynamics.

Agricultural systems rely on multiple data streams: GNSS, wheel speed, steering angle, hydraulic cylinder positions, and sometimes LiDAR or camera inputs. INS integration requires precise timing alignment. ADI designs its systems for deterministic latency and reliable time stamping, which improves fusion accuracy.

The transition from row guidance to headland turns stresses both GNSS and INS. Machines accelerate, decelerate, rotate sharply, and pass through GNSS-obstructed corners. ADI’s inertial fusion helps maintain attitude and velocity states during these high-dynamic transitions.

Agricultural drones operate close to trees and terrain. Ground robots operate beneath canopy. INS solutions provide roll/pitch stability, altitude smoothing, and fallback motion propagation when GNSS is degraded.

Economics: Performance Within Reach

Precision agriculture is expanding beyond large, capital-intensive farms. The next wave of adoption will come from mid-market operations and mixed-crop geographies.

• Cost matters. Expensive IMUs are non-starters. ADI designs MEMS-based solutions that offer robust performance within an accessible cost envelope.

• Scalability drives OEM decisions. Manufacturers want sensors available in volume, with predictable lead times and long lifecycle commitments.

• Global adoption requires price/performance balancing. Emerging markets need PNT reliability but cannot bear aerospace-grade costs. Scalable, rugged MEMS solutions fill this gap.

• Autonomy ROI depends on continuity. If a machine can maintain guidance through GNSS disruptions, it can operate longer hours and at higher speeds, improving economics for both OEMs and end-users.

“Just because you go from a big tractor to a smaller tractor, the conditions don’t change that much,” Galchev said. “If you want to achieve the same mission profile, you still need the same performance level.”

As ADI brings cost-efficient inertial capability into mainstream ag equipment, the performance gap between high-end and mid-tier platforms narrows.

The Road Ahead: Multi-Sensor Fusion and Autonomy

Agriculture is evolving toward heterogeneous fleets: autonomous tractors, robotic harvesters, terrain-following sprayers, orchard drones, and edge-connected implements. All require resilient PNT.

End-of-row autonomy

Low-speed, high-precision maneuvers demand stable attitude estimation. INS ensures smooth transitions even in partial GNSS shadows.

Terrain-following and boom dynamics

Sprayers rely on roll/pitch estimates for boom control. IMU data supports rapid damping of boom oscillation, improving chemical placement, reducing drift, and lowering input costs.

Cooperative ground-air systems

Drones performing scouting missions must integrate with guidance systems on the ground. Consistent inertial performance across platforms enables better data fusion and farm-level coordination.

Resilience as a design requirement

Interference, accidental or intentional, is increasingly common. INS helps maintain continuity of operation when GNSS performance degrades. It stabilizes machine behavior during uncertainty and helps diagnostic systems detect anomalies.

Regulatory evolution

As autonomy expands, functional-safety requirements will increase. INS adds a measurable layer of redundancy and validation, supporting safety cases for next-generation machines.

“We have sensors that we released more than 20 years ago still being produced,” Galchev said, “because our customers’ systems have long lifespans and once something works, it can be very difficult and expensive to re-qualify and swap it out.”

As autonomy accelerates, the next decade of agriculture will be shaped by platforms that assume GNSS variability and engineer around it from day one. That shift elevates inertial from an add-on to a core requirement. ADI, with its long record of sensor innovation and system-level discipline, is positioned to anchor that transition. Their approach: predictable drift behavior, calibration at the silicon level, ruggedized packaging, and tight GNSS-INS fusion, gives OEMs a stable foundation to build autonomy across tractors, implements, drones, and emerging agricultural robots. The path forward is clear: Resilient PNT will define productivity, and ADI’s inertial technology will increasingly sit at the center of the autonomy stack, enabling machines that navigate, adapt and operate with confidence in the real conditions of the farm.

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Launched in 2021, Robotic Autonomy in Complex Environments with Resiliency (RACER) was conceived not as a single robotic platform but as an autonomy “stack”—a collection of algorithms, datasets and neural network models that can be ported to multiple vehicles equipped with appropriate sensors. According to DARPA, that stack now allows users to “apply the RACER stack to any vehicle … turning it into an autonomous machine capable of operating in challenging off-road environments, independent of GPS or pre-mapped routes, and at mission-relevant speeds.” 

From Grand Challenge to GPS-independent ground autonomy

DARPA casts RACER as an heir to its 2004–2005 Grand Challenge, which helped kick-start the modern era of autonomous vehicles. Two decades later, the focus has shifted from proving that a single vehicle can complete a desert course to fielding a reusable autonomy layer that can be adapted quickly to new platforms and operational environments. 

“RACER isn’t just about replicating existing military capabilities,” RACER program manager Stuart Young said in the agency’s announcement. “It’s about fundamentally reimagining how missions are executed.” 

For the PNT community, the notable point is that RACER explicitly assumes unreliable or unavailable GPS. DARPA’s description emphasizes that the stack is intended to operate “off the grid,” with reduced reliance on GPS and pre-programmed paths, allowing robotic systems to handle missions such as reconnaissance and breaching at standoff distance from friendly forces. 

Army use cases: breaching and long-range reconnaissance

RACER’s final phase centered on operationally realistic scenarios with Army units. In October 2025, the program partnered with the Army’s III Armored Corps 36th Engineer Brigade during a combat breaching demonstration at Fort Hood, Texas, under the Machine Assisted Rugged Soldier effort. Using the RACER Heavy Platform—a Carnegie Robotics system built on a Textron M5 chassis—the Army paired the robotic vehicle with an M58 mine-clearing line charge to autonomously open a lane through a minefield. 

DARPA highlighted that event as a proof point for using heavy uncrewed platforms in high-risk tasks where GPS may be degraded or denied and where keeping human crews farther from the breach is a priority.

In November 2025, soldiers from the 11th Armored Cavalry Regiment employed RACER-equipped Polaris RZR–based “RACER Fleet Vehicles” as part of an opposition force during a live force-on-force rotation at the National Training Center, Fort Irwin, California. With integrated ISR payloads, those platforms were tasked to conduct autonomous long-range reconnaissance—traditionally a mission for manned scout teams—again with reduced reliance on GPS and no detailed pre-mapping of the route. 

“By decreasing reliance on GPS and pre-programmed paths, RACER ensures warfighters can deploy autonomous assets in any environment, even when operating off the grid,” Young said. “Instead of human scouts going 12 or 15 km into enemy territory, that dangerous work can be handled by a robot while humans are safe, and the risk is minimized.” 

Perception and fast adaptation as “soft” alternative PNT

DARPA describes RACER’s perception architecture as the program’s most significant technical advance. Earlier autonomous ground systems often needed weeks of retraining when moved to a new environment. RACER, by contrast, is said to adapt a new model in roughly a day, which DARPA calls “invaluable for warfighters who need to deploy robotic assets rapidly to unfamiliar terrains.” 

The agency likens the behavior to a human driver with “a priori insight” about how roads normally behave: the autonomy stack predicts what lies ahead based on prior experience and sensor evidence, then adjusts its speed and path when cues—such as an oddly parked vehicle or construction cones—indicate elevated uncertainty. That predictive capability allows higher speeds and safer operation in unstructured terrain, without the crutch of detailed maps or continuous GPS. 

While RACER does not introduce a new radio-frequency PNT source, it effectively functions as a local, perception-driven navigation solution—a form of “soft” alternative navigation that depends on machine-learned terrain understanding rather than external timing or ranging. For PNT practitioners tracking how DoD plans to fight through GPS disruption, RACER sits alongside inertial, visual-odometry, and terrain-referenced navigation efforts as part of a broader shift toward GPS-independent autonomy.

DARPA’s final RACER experiment at Fort Irwin, California, was used to validate this perception architecture and its rapid retraining process, demonstrating that models could be adapted quickly to new terrain conditions while maintaining autonomous mobility. 

Transition paths and dual-use autonomy

With the experimentation campaign ending, DARPA is now emphasizing transition to both military programs and commercial sectors such as agriculture, construction, mining and off-road logistics, where vehicles face similar perception and navigation challenges. Multiple companies have spun out of RACER, including Field AI and Overland AI, which trace their origins to NASA Jet Propulsion Laboratory and University of Washington research respectively. 

“Now that the RACER program is ending, there is a lot of commercial opportunity for private equity,” Young said. “It’s time for both military users and private investors to recognize the transformative potential of RACER and embrace a future where autonomous systems are not just a possibility, but a reliable and integral part of our world.” 

RACER is another signal that DoD is planning for operations where GPS cannot be assumed. Even without a new RF PNT layer, programs like RACER are pushing the services toward autonomy stacks that treat GNSS as one input among many—and that are explicitly designed to keep vehicles moving when that input disappears.

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The Defense Innovation Unit (DIU) has opened a new Commercial Solutions Opening (CSO) for the Geomagnetic Airborne Unmanned Survey System (GAUSS), a multi-year, multi-phase effort to build the magnetic reference maps needed to make magnetic navigation (MagNav) an operational alternative to GPS over the world’s oceans. Solution briefs are due 22 January 2026. 

The GAUSS project sits squarely in the DoD’s broader push for resilient position, navigation, and timing (PNT) in the face of persistent GPS jamming and spoofing on the modern battlefield. DIU describes GAUSS as a program to demonstrate “magnetic data collection platforms that address warfighter needs for precision navigation capabilities beyond GPS,” calling it a key step toward operationalizing MagNav for national security assets. 

Why magnetic navigation needs better maps

MagNav exploits spatial “fingerprints” in the Earth’s crustal magnetic field: by comparing real-time magnetometer readings against a precise magnetic map, a vehicle can estimate its position even when GNSS is denied. Over land, geophysical survey and resource-exploration campaigns have generated reasonably dense data. Over open ocean, however, high-precision geomagnetic mapping is sparse and expensive, leaving large gaps in the reference models needed for operational navigation. 

GAUSS is explicitly designed to attack that bottleneck. The RFP notes that current commercial airborne platforms for magnetic data collection are optimized for terrestrial geology, not long-range oceanic campaigns. As a result, they lack the range and cost profile to support million-kilometer-scale surveys at the altitudes and geometries MagNav requires. The DoD is therefore looking for concepts that can be deployed in bulk, flown repeatedly, and scaled rapidly—both at high altitude and near the sea surface—without bespoke, one-off integration work each time.

Data layer first, navigation products later

Unlike many navigation-focused programs, GAUSS is not asking industry to deliver a complete MagNav system. It is asking for the pieces that make MagNav possible:

• Platforms that can be deployed in numbers, from higher-endurance aircraft that can cross oceans to more attritable vehicles suited for high-risk areas.
• Integration approaches that allow COTS and GOTS magnetometers to be added with minimal non-recurring engineering, and with careful control of platform-induced magnetic noise.
• Processing techniques that can turn raw magnetic line data into “navigable products” with tight repeatability requirements.

There is also a clear emphasis on correcting the field itself. GAUSS explicitly calls for techniques to handle diurnal variations and other time-varying effects more than 1,000 miles offshore, whether through space-weather modeling, loitering monitoring assets, or novel approaches. That places space-weather, modeling and signal-processing expertise on equal footing with airframe design.

In other words, GAUSS treats geomagnetic mapping as a layered problem: aircraft, sensors, calibration, environmental correction, and data products that future navigation systems can consume. It is a data infrastructure program that sits underneath whatever MagNav receivers eventually reach the field.

For the PNT community, GAUSS is notable because it shifts the conversation away from narrow “anti-jam” solutions and toward a portfolio strategy. Magnetic navigation does not replace GNSS, but it offers a fundamentally different signal source that cannot be jammed in the same way and does not depend on a satellite broadcast. To make that option real, the government now appears willing to underwrite the most expensive part: building the maps.

A visible commitment to alternative PNT

The GAUSS documentation opens by naming “precision navigation capabilities beyond GPS” as the core need and frames the effort as a multi-year initiative culminating in “mature technology demonstrations” that enable operational MagNav over trans-oceanic distances. Flight testing is expected throughout prototyping, including navigation flight tests using newly collected data.

Taken together, those elements send a clear message:

• The DoD expects GNSS denial and degradation to be a long-term reality, not a temporary aberration.
• Magnetic navigation has progressed far enough that the main barrier is data availability, not basic feasibility.
• Unmanned systems, rather than traditional crewed survey aircraft alone, are viewed as the scalable way to build the required data sets.

As other alternative PNT concepts—from celestial navigation to signals-of-opportunity—compete for attention and investment, GAUSS suggests that magnetic navigation has secured a place in the emerging portfolio, with the data layer now moving to the forefront.

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This Belgian Defense-commissioned research program is aimed at enabling fully autonomous unmanned aerial system (UAS) approach and landing on moving vessels under conditions where conventional satellite navigation can be unreliable or unavailable.

As 2026 begins, heightened GNSS vulnerability concerns make Agilica’s hybrid PNT work very pertinent. The company’s ground-based localization (AGL) positioning system is an alternative PNT solution that blends ultra-wideband (UWB) terrestrial signals with seamless GNSS integration, including the Galileo High Accuracy Service, to extend precision navigation into GNSS-challenged environments.

The underlying architecture operates much like a terrestrial ‘mini constellation’; fixed UWB anchors with known coordinates broadcast ranging signals to mobile tags on UAVs, enabling centimeter-level positioning accuracy even during multipath or signal obstruction that would typically plague GNSS alone.

This hybrid approach was validated earlier in 2025 when Agilica successfully completed a European Space Agency (ESA)-funded feasibility study that confirmed the technical and commercial viability of the AGL system for precision navigation and landing tasks in unfavorable environments, including indoor spaces, offshore platforms and moving vessels at sea. The study demonstrated the system’s ability to augment GNSS with UWB-based local positioning and achieve sub-20 cm accuracy.

High-level coordinated effort

SAILS, funded under the Belgian Defense DEFRA program, with a €1.6 M budget and running from 2025 through 2028, brings together Sabena Engineering, the Belgian Navy, the Royal Military Academy, and Agilica. The consortium aims not just to enable autonomous drone landing but to extend UAV operational envelopes in high seas, harsh weather, and GNSS-challenged environments, essential for military, offshore energy, and search-and-rescue missions.

“Landing a drone on a moving ship is among the toughest navigation challenges in maritime autonomy,” Bart Scheers, COO of Agilica, told the press in late 2025. “With SAILS, we’re moving from concept to operational demonstration, bridging maritime robotics and safe flight operations.”

As GNSS vulnerabilities, from interference to signal blockage, increasingly constrain autonomous systems, Agilica’s work reflects a broader shift toward resilient PNT architectures. By combining multi-sensor fusion with terrestrial augmentation, the SAILS project demonstrates how GNSS-centric autonomy can be strengthened for operations in environments where satellite navigation alone cannot meet performance or integrity demands.

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The ANELLO Aerial INS is purpose-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 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 the warfighter’s effectiveness and survivability.

“Customers flying real missions need resilient navigation when GPS isn’t reliable,” said Dr. 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.

Key features of the ANELLO Aerial INS include:

• High-precision 3-Axis SiPhOG™ optical gyros with <0.5º/hr unaided heading drift for reliable dead-reckoning during GNSS outages
  
• Dual triple-frequency all-constellation GNSS receivers with static heading capability; ready for RTK/PPP corrections
  
• ANELLO Advanced Sensor Fusion Engine with GNSS spoofing detection; resilient holdover in GPS-denied or spoofed conditions
  
• Flight-stack integration: PX4 and ArduPilot drivers; standard interfaces (Ethernet, RS-232, RS-422, CAN) and timing (PPS Out/PPS Sync In)
  
• NMEA-Compliant GNSS Interface outputs NMEA navigation packets for seamless integration as a drop-in replacement for conventional GNSS receivers
  
• Flight-Profile Optimization; Algorithms calibrated for BVLOS, ISR, VTOL, and other autonomous aerial vehicles for accurate navigation
  
• Rugged, Compact, and Lightweight; Small footprint, low power consumption, vibration-tolerant design for multirotor, fixed-wing, and VTOL platforms.

“The ANELLO SiPhOG™ technology is a game changer for our warfighters. The ability to navigate in GPS-denied or spoofed environments without cameras or fiber-optic cables—in small, lightweight systems—are essential for future combat missions,” said Dan Magy, CEO at Firestorm. “The ANELLO team has developed an Aerial solution that seamlessly integrates into existing avionics with minimal effort. This type of capability is essential in today’s conflict areas where our adversaries actively disrupt GPS, making ANELLO a powerful upgrade for all modern aerial platforms.”

The launch of the ANELLO Aerial INS reinforces ANELLO’s focus on assured navigation in contested environments. By introducing a purpose-built airborne solution alongside its already proven ground and maritime inertial navigation systems, ANELLO Photonics extends its lead in GPS-denied navigation across land, air, and sea. ANELLO products have been validated through multiple U.S. DoW operational test events and have been shipping to a vast array of customers.

The ANELLO Aerial INS is available for evaluation today with production shipments beginning Q2/2026. Evaluation kits include the ANELLO Aerial INS, cabling, drivers for PX4/ArduPilot, and a quick-start integration guide. For pricing, demo data, and evaluation units, contact in**@*************cs.com.

The post ANELLO Photonics Launches Aerial INS at CES 2026, Featuring SiPhOG™ Technology and Integrated GNSS appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Swift Navigation, a leader in high-precision GNSS technology today announced that it is collaborating with NVIDIA to enable a more scalable, cost-effective approach to autonomous driving by integrating the NVIDIA DRIVE AGX platform with Swift’s globally referenced, centimeter-accurate GNSS positioning.

The Challenge: High-Cost Localization Stalls Autonomy at Scale

Mass-market autonomy has been hindered by reliance on expensive, compute-heavy optical sensors for absolute localization. This traditional approach drives up hardware costs and creates reliability failure points, particularly in the absence of clear lane markings or when rain, snow, or direct sunlight, blind optical sensors.

The Solution: Precise GNSS Offloads the Positioning Burden

Swift Navigation solves this by offloading absolute localization to the Global Navigation Satellite System (GNSS) — such as GPS — sensor stack using the Swift Automotive Suite™. The Suite is a complete, modular software solution for safe, high-integrity precise vehicle localization that combines:

1. Skylark™ Precise Positioning Service: A cloud-based, ASIL-certified service that improves GNSS accuracy from several meters to centimeter level.
2. Starling® Positioning Engine: Software that fuses raw GNSS data and corrections with inertial sensors (IMU) and wheel odometry to deliver high-integrity, centimeter-accurate positioning (PVT).

By entrusting lane-level positioning to Swift’s high-precision stack, the vehicle’s optical sensors are relieved of the absolute positioning burden. This allows the perception stack to be optimized for obstacle detection and immediate safety, significantly reducing overall system cost and complexity.

Native Integration with NVIDIA DRIVE AGX platform Accelerates Production

The integration is delivered through the new Starling SAL Plugin for NVIDIA DriveWorks. NVIDIA DRIVE AGX platform is the industry-standard, end-to-end platform for software-defined vehicles, scaling from assisted to fully autonomous operation. DriveWorks, its comprehensive SDK, provides a unified Sensor Abstraction Layer (SAL) for seamless ingestion of data from all sensor types.

Swift’s new plugin acts as a “drop-in” component within this architecture. Sitting between the vehicle’s raw GNSS sensors and higher-layer software, such as that for localization, the plugin invisibly handles the complex mathematics of GNSS corrections and sensor fusion, outputting a clean, corrected position stream directly into the standard DriveWorks interface.

“We are removing the single biggest hurdle to widespread autonomy: the complexity and cost of localization,” said Holger Ippach, EVP of Product and Marketing at Swift Navigation. “By delivering Starling’s natively integrated, high-integrity GNSS to NVIDIA DriveWorks, we are giving OEMs a direct path to globally referenced, lane-level positioning that is simple, scalable, and affordable.”

Key Differentiators 

The collaboration and the Starling SAL Plugin unlock several key advantages for automotive OEMs leveraging the NVIDIA DRIVE platform:

• Cloud-Native ASIL Safety: Skylark is the first ASIL-certified positioning service built entirely in the cloud, offering unmatched scalability and reliability at a lower cost than solutions reliant on physical data centers.
• Comprehensive Sensor Fusion: Starling Positioning Engine delivers robust, high-integrity positioning by fusing precise GNSS with IMU and wheel odometry, ensuring continuous, lane-level accuracy even in signal-challenged environments.

• Plug-and-Play Precision: Developers no longer need to build localization stacks from scratch. High precision is toggled on simply by adding the Starling plugin to the DriveWorks configuration.
  
• Hardware Independence: Because Starling is software-defined, NVIDIA customers can achieve high performance using a wide variety of mass-market GNSS receivers, rather than being locked into expensive, proprietary navigation units.
  
• Pre-Validated Integration: The Starling plugin has been rigorously tested and validated within the DriveWorks environment. This eliminates the complex, months-long burden of validating custom sensor drivers and fusion algorithms, allowing engineering teams to focus immediately on high-level path planning and control.

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SEBASTIAN CIUBAN, PETER J.G. TEUNISSEN, CHRISTIAN C.J.M. TIBERIUS, DELFT UNIVERSITY OF TECHNOLOGY

Positioning via Global Navigation Satellite Systems (GNSS) and Terrestrial Networked Positioning Systems (TNPS), along with measurements from other sensors (e.g., inertial measurement units, cameras, LiDAR), is widely used or of growing interest in several safety-critical applications, such as automotive, aviation, rail and maritime [1-3]. With these measurements, or observables, a position estimator x̅∈Rⁿ can be formulated, where n≥1 represents the dimension (e.g., n=1 for vertical component, n=2 for horizontal components). Given access to the probability density function (PDF) of x̅∈Rⁿ, denoted f_x̅(x), and an application specific safety-region B⊂Rⁿ (e.g., interval when n=1, ellipse when n=2), one can pose the following question: “What is the probability that the position estimator falls outside the designated safety-region?”

Quantifying this probability enables its comparison against application-specific requirements, or guidelines, to determine whether they are satisfied. This probability can be viewed as a positioning “safety indicator” for the application of interest.

We formulate the aforementioned probability based on the event of positioning failure F=(x̅∉B) [4], and express it as follows

where B^c=Rⁿ\B is the complement of the safety-region (i.e., the Rⁿ space without the safety-region B). The computation of P_F is a challenging task, primarily because the PDF f_x̅(x) may have multiple modes. These modes arise from the position estimator, which, as we assume here, usually results from a combined parameter estimation and statistical hypothesis testing procedure to accommodate for model misspecifications (e.g., faults or outliers in observables). This intricacy is captured in the theoretical framework introduced in [5].

To account for such model misspecifications, one can assume a positioning model that is valid under nominal conditions (the null hypothesis H₀) and design alternative positioning models under multiple alternative hypotheses H_i≠0 with i∈{1,…,k}. For example, in the case of GNSS-based positioning, one can define positioning models under the H_i≠0’s to account for the presence of one or multiple outliers (or faults) in the code-based observables, cycle slips in the phase-based observables, satellite failures, unmodelled atmospheric delays, etc. Then the objective is to select the most likely hypothesis and use the corresponding positioning model to provide the position estimator x̅_i for i∈{0,…,k} (Figure 1). As shown in Figure 1, we highlight that the position estimator x̅∈Rⁿ is a function of the individual estimators x̅_i and the statistical testing procedure used to select these estimators (e.g., a Detection Identification and Adaptation-DIA procedure [6-8] or a Fault Detection and Exclusion-FDE procedure [9-11]).

The expression of f_x̅(x) provides additional insights into its dependencies [5]

where f_x̅i,t̅(x,t) is the joint PDF of the individual estimators x̅_i∈Rⁿ and the vector of misclosures t̅∈R^r used to construct statistical tests for selecting the most likely hypothesis H_i, and p_i(t̅) is an indicator function that equals 1 if H_i is selected and 0 otherwise. The vector of misclosures has the dimension of the measurement redundancy, denoted r, and it contains all the available information useful for testing the validity of the positioning model under H₀. From the expression of f_x̅(x) in (2) we emphasize that P_F from (1) depends on the number of k+1 hypotheses, on the dimension n of the to-be-estimated parameter vector, and also on the dimension r of the misclosure vector. Furthermore, x̅_i∈Rⁿ and t̅∈R^r are dependent for i≠0, which means the joint PDF cannot be expressed as a product of the marginal PDFs (i.e., f_x̅i,t̅(x,t) ≠ f_x̅i(x) f_t̅(t̅), for i≠0). Ignoring or failing to account for the dependence between x̅_i∈Rⁿ and t̅∈R^r, for i≠0, may result in overly-optimistic results when computing P_F, potentially leading to incorrect conclusions that positioning safety requirements are met when they are not [12-15]. The multimodal structure of f_x̅(x) (Figure 2) and its aforementioned dependencies render analytical methods for computing P_F unfeasible.

Another aspect to take into account when computing P_F is related to the requirement for the event of positioning failure F=(x̅∉B)= (x̅∈B^c) to be rare (e.g., P_F<10^-7 [17]). Because analytical methods for computing P_F are unfeasible, numerical integration methods are reasonable candidates. However, as the rarity requirement for F=(x̅∈B^c) becomes more stringent, the computational effort required to compute such probabilities increases significantly. In recent work, we tackled these challenges by introducing a method grounded in rare event simulation principles and proposed a possible approach to address them [18]. The method is intended for use during the design stage of positioning algorithms, where decisions are required regarding (i) measurement models associated with selected positioning technologies and sensors, (ii) parameter estimation techniques for the position vector, (iii) statistical hypothesis testing procedures to accommodate for model misspecifications, and (iv) positioning scenarios of interest (e.g., vehicle driving on a highway, or in an urban area), among other considerations. This methodology is consistent with the scenario-based safety assessment framework widely adopted for studies on automated and autonomous vehicles [19-21].

In this contribution, we outline the principles underlying the approach proposed in our recent work [18]. As an example, we present a simulation-based analysis of positioning safety for an automated vehicle using a real dual-constellation GPS and Galileo satellite geometry [13].

Standard Monte Carlo and Importance Sampling

A reasonable first step is to use numerical integration methods, such as standard Monte Carlo (MC) simulation, to compute P_F. It is possible to re-express (1) as an expected value with respect to the PDF f_x̅(x), denoted E_fx̅(.),

where 1_B^c(x̅) is an indicator function that is 1 if x̅∈B^c, and 0 otherwise. By generating N_x̅ independent and identically distributed (i.i.d.) pseudo-random samples from f_x̅(x) one can approximate (3) as follows (based on counting how many pseudo-random samples fall in B^c=Rⁿ\B)

with its simulation variance, or dispersion, expressed as D(P̅_F^MC)= P_F(1-P_F)/N_x̅. However, the standard Monte Carlo approximation in (4) has limitations when computing probabilities of rare events. Specifically, in the case of rare events a substantial number of pseudo-random samples N_x̅ is needed to be able to compute (4) with a low simulation variance D(P̅_F^MC). For example, if the objective is to compute a target value P_F=10^-9 with √(D(P̅_F^MC))=10^-10, then the required number of pseudo-random samples would be N_x̅≈10¹¹, which involves an excessively large computational effort.

To tackle the limitations of the standard Monte Carlo method, a different approach is needed. Importance Sampling (IS) is a reasonable candidate to be considered as it can achieve simulation variance reduction without a significant increase of the required pseudo-random samples to be generated [22]. The principles of IS have found applicability across a wide area of safety-critical applications, such as safety analyses of structures, nuclear power plants, and for computations of probabilities of collision events in aviation [23-25]. On the basis of IS, we can express P_F from (3), with respect to a newly introduced PDF f̃(x), as follows

where f̃(x) is also called IS density, auxiliar density, or proposal PDF [23, 26]. The main idea is to choose, or find, a proposal PDF that has a larger probability density over the region B^c=Rⁿ\B than f_x̅(x) and 1_B^c(x̅) f̃(x)≠0 whenever 1_B^c(x̅) f_x̅(x)≠0 [27]. If such proposal PDF f̃(x) is chosen, then by generating Ñ_x i.i.d. pseudo-random samples from it allows for the approximation of (5) as follows

The performance of P̅_F^IS in reducing simulation variance depends on the choice of the proposal PDF f̃(x), which in turn drives the quality of the probability estimates. It is possible to find a proposal PDF f̃(x), within a family of PDFs (e.g., exponential family) that minimizes the Kullback-Leibler (KL) divergence, or equivalently the Cross-Entropy, with respect to the optimal theoretical PDF f̃^*(x)=[1_B^c(x̅) f_x̅(x)]/P_F [22]. The theoretical PDF f̃^*(x) is optimal in the sense that it minimizes simulation variance. In practice, f̃^*(x) cannot be used because it depends on the unknown P_F. The proposal PDF f̃(x) that minimizes the KL divergence w.r.t. f̃^*(x) can be determined using the Cross-Entropy method [28].

Probability of Positioning Failure and its Components

We recently proposed a method [18] that can compute the probability of positioning failure from its conditional components (Figure 3). A component-wise computation of P_F enables the determination of the conditional components that contribute most, or least, to its value. Having designed a statistical hypothesis testing procedure to address model misspecifications with a null hypothesis H₀ (comprising of the positioning model believed to be valid under nominal conditions) and k alternative hypotheses H_i≠0 (e.g., comprising of positioning models that account for the presence of outliers, or faults, in the observables), it is possible to decompose P_F into its conditional components based on the statistical testing decisions: Correct Acceptance (CA) when H₀ is accepted and H₀ is valid; False Alarm (FA_i) when H_i≠0 is accepted and H₀ is valid; Missed Detection (MD_i) when H₀ is accepted and H_i≠0 is valid; Correct Identification (CI_i) when H_i≠0 is accepted and H_i≠0 is valid; Wrong Identification (WI_j) when H_j is accepted and H_i is valid for j∉{0,i} (see also the example in Figure 1). This decomposition has been presented and elaborated on in [13]. A graphical representation of the “failure-tree” is shown in Figure 3, where the notation of the probability of positioning failure changed to P_F(b̅) with b̅={b̅₁,…,b̅_k} to account for b̅_i, with i∈{1,…,k}, under all the k alternative hypotheses. Note: Figure 3 goes here The equations that describe the branches and connections in Figure 3 are the following:

where P(H₀) and P(H_i), for i∈{1,…,k}, are the apriori probabilities of the hypotheses H₀ and H_i≠0, P_F|E is the probability of positioning failure conditioned on the statistical testing decision E∈{CA,FA_i,MD_i,CI_i,WI_j}, and P_E is the probability of the event of the statistical decision E. Once the conditional components from (7) are computed, then the total probability of positioning failure is obtained as follows [18],

With (7) and (8) one can obtain the entire characteristic of P_F(b̅) as a function of b̅={b̅₁,…,b̅_k}, thus providing all the information required to perform a rigorous sensitivity analysis for the design purposes of the positioning system. The positioning safety-analysis in the next section is based on constructing the “failure-tree” in Figure 3 via the computations of (7) and (8). Because the size of the outliers in (8) is not known a priori, the maximum value of (8) is reported to provide insights into the worst-case scenario, aiding in the assessment of whether safety requirements or guidelines are met.

Decimeter Level GNSS-Based Positioning Safety Analysis

We consider the GNSS-based positioning safety analysis presented in [13], which is also similar to the one in [18]. The simulation scenario involving an automated vehicle which coordinates are determined in a local East-North-Up (ENU) coordinate system using single-frequency, code-based pseudorange observables in a Differential (DGNSS) setup. The GNSS constellations we consider are GPS (G) and Galileo (E), at L1/E1 frequency. At the considered snapshot of time (epoch), eight GPS and seven Galileo satellites are visible after applying an elevation mask of 10°. Figure 4(a) shows the skyplot of the observed GPS and Galileo satellites by the automated vehicle. Additionally, an elevation depending weighting is applied to the observables.

The horizontal positioning precision is about 0.5 meters (95% circular probability radius).

In the setup of the statistical hypothesis testing procedure to accomodate for outliers or faults in measurements, in this case the DIA procedure, we account for individual outliers, assuming only one occurs at a time (there are k=15 alternative hypotheses). At the output of this procedure the DIA-estimator x̅∈Rⁿ and its PDF f_x̅(x) are obtained. When determining, or choosing, the shape and size of the safety-region B⊂R² several factors should be considered, such as: (i) vehicle’s dimensions, (ii) road geometry to ensure the vehicle is within its lane, (iii) minimum required braking distance as a function of the vehicle’s speed, (iv) proximity with regard to other traffic participants, among other considerations. Several approaches have been proposed in terms of shapes of the safety-region that bound the vehicle (e.g., elliptical, rectangular) in several studies [17, 29, 30]. For our scenario, and for consistency with existing approaches in the literature, we choose an ellipse to inscribe the vehicle, which has a length of 4.5 meters, a width of 1.8 meters, and an orientation of 0° relative to the North axis (Figure 4(b)).

With the DIA-estimator x̅∈Rⁿ, its PDF f_x̅(x), and the ellipsoidal safety-region B⊂R² defined, the method in [18] can be applied for positioning-safety analysis. This yields the computed conditional components of P̅_F(b̅) from equation (8). Figure 5(a) shows the component P̅_F|H0 computed over 50 simulation runs to observe the variability in the results, and Figures 5(b) and 5(c) show the P̅_F|Hi(b̅_i) as a function of the outlier size b̅_i for i∈{1,…,15}. It is noticable that P̅_F|H4(b̅₄), P̅_F|H8(b̅₈), P̅_F|H10(b̅₁₀), and P̅_F|H14(b̅₁₄) are dominating when the size of their respective outlier is larger than 1.60 meters. This can be explained based on the rover receiver-satellite geometry in Figure 5(a), which shows that satellites corresponding to the hypotheses 4, 8, 10, and 14 have a large influence on the horizontal-axis (east-component) of the 2D position solution. Conversely, satellites at low-elevations have a reduced contribution to the 2D position solution (e.g., 6, 9, and 13), which leads to low probabilities of positioning failure under the respective alternative hypotheses.

To compute the maximum P̅_F(b̅), assumptions are needed for the a-priori P(H_i) for i∈{0,…,15}. Because the alternative hypotheses account for outliers in the pseudoranges at the rover-receiver (automated vehicle), it is assumed they primarily occur due to different signal reflections caused by the surrounding environment (e.g., nearby infrastructure). For this analysis, we consider three sets of assumptions ranging from conservative to optimistic cases: (1) P(H₀)=0.98500 and P(H_i)=10^-3 for i∈{1,…,15}; (2) P(H₀)=0.99850 and P(H_i)=10^-4 for i∈{1,…,15}; (3) P(H₀)=0.99985 and P(H_i)=10^-5 for i∈{1,…,15}. The obtained results for the maximum P̅_F(b̅) in the three cases are shown in the Table 1. Note these results correspond to the rover receiver-satellite geometry in Figure 4(a) and the fixed safety-region B⊂R² in Figure 4(b).

In practice, a vehicle will change its orientation while moving (e.g., when making a U-turn, exiting a highway, taking a left/right turn), and consequently, the maximum P̅_F(b̅) will also change. For a short time window (e.g., few minutes), it can be assumed the rover receiver-satellite geometry from Figure 4(a) is constant, allowing us to base our next analysis on the vehicle’s change in the orientation angle θ (measured, in degrees, clockwise with respect to the North axis). Therefore, the safety region B_θ⊂R² (note the change in notation) will also depend on the vehicle’s orientation angle θ. The objective is to compute the maximum P̅_F(b̅) as a function of θ from its components P̅_F|H0 and max_{b̅1,…,b̅15} ∑_i=1¹⁵ P̅_F|Hi(b̅_i). Figure 6(a) shows the results of P̅_F|H0 as a function of θ and that the maximum values is 3.33⋅10^-7±0.0216⋅10^-7 for the vehicle’s orientation angle of θ=110°. In the case of the component max_{b̅1,…,b̅15} ∑_i=1¹⁵ P̅_F|Hi(b̅_i), as shown in Figure 6(b), the maximum value is reached at 1.81⋅10^-2 ±0.00462⋅10^-2 for θ=110°. By combining the results from Figure 6(a) and Figure 6(b) with the assumptions regarding the a-priori probabilities P(H₀) and P(H_i) for i∈{1,…,15} as discussed in the three cases, the results in Figure 6(c) are obtained. Note: Figures 6a, b and c go here In the most conservative case (Case 1), the maximum P̅_F(b̅) at θ=110° is 1.84⋅10^-5 ±0.000216⋅10^-5 while for the most optimistic case (Case 3) the maximum P̅_F(b̅) is 4.94⋅10^-7 ±0.0216⋅10^-7. These results help determine whether the target requirements or guidelines for positioning safety are met. If the requirements or guidelines are not satisfied, it may be necessary to make appropriate changes to the positioning algorithm design choices. These could include aspects such as the measurement setup (e.g., functional and stochastic models), the safety-region, or the combined parameter estimation and statistical hypothesis testing procedure. For instance, the new theoretical framework introduced in [31] shows how fit-for-purpose statistical hypothesis testing improves the performance of DIA-estimators.

Summary and Conclusions

In this contribution, we have presented a new perspective on positioning safety analyses by addressing key ideas and challenges associated with the computation of the probability of positioning failure, such as: (i) the multimodal PDF of the position estimator f_x̅(x), which accounts for the dependence between parameter estimation and statistical hypothesis testing to accommodate potential faults or outliers in the measurement model; (ii) to account for the dependencies on the number of k+1 hypotheses, on the dimension n of the to-be-estimated parameter vector, and also on the dimension r of the misclosure vector when constructing the PDF f_x̅(x) and performing its integration over the region B^c= Rⁿ\B (the Rⁿ space without the safety-region B⊂Rⁿ), which renders analytical methods overly complex or impractical; and (iii) the rarity of positioning failure events in the context of safety-critical applications, which requires more advanced numerical integration methods than standard Monte Carlo.

To address these challenges, we presented an approach based on our recent work in [18], which relies on techniques from rare event simulation, specifically Importance Sampling and the Cross-Entropy Method [22, 28]. The computation and analysis of the probability of positioning failure are intended to be performed during the design stage of positioning algorithms, where key decisions are made regarding (i) measurement models, (ii) parameter estimation methods for the position vector, (iii) statistical hypothesis testing procedures to handle model misspecifications (e.g., outliers or faults in measurements), and (iv) positioning scenarios for vehicles, among other factors. This approach aligns with scenario-based safety assessment frameworks, which are widely used or proposed in studies on automated and autonomous vehicles [19-21].

As an example, we applied the proposed method to perform a single-epoch positioning safety analysis for an automated vehicle, focusing on decimeter-level precision GNSS-based positioning. The method facilitated an analysis of a worst-case scenario aimed at determining the maximum probability of positioning failure. Such analyses can guide decisions on whether positioning safety targets or requirements are satisfied. Once compliance with application-specific requirements is demonstrated based on the probability of positioning failure in the relevant scenarios, the corresponding parameter estimation and statistical hypothesis testing procedure can be implemented for real-time positioning.

While the chosen positioning scenario was centered on the automotive domain, the proposed approach for computing and analyzing the probability of positioning failure is also applicable to other safety-critical fields, including civil aviation, shipping and rail.

Acknowledgements

This research was funded by the Dutch Research Council (NWO) under Grant 18305, titled ‘I-GNSS Positioning for Assisted and Automated Driving.’ The support is gratefully acknowledged.

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Authors

Sebastian Ciuban received the M.Sc. in Aerospace Systems: Navigation and Telecommunications from École Nationale de l’Aviation Civile (ÉNAC), Toulouse, France, in 2017. Post-graduation, he joined the European Space Research and Technology Centre (ESTEC) as a Young Graduate Trainee (YGT) in the Directorate of Navigation. From 2019 to 2021, he worked as a GNSS Engineer at CGI Nederland B.V. In 2021, he started a Ph.D at Delft University of Technology, Delft, The Netherlands, in the field of PNT safety for automated and autonomous vehicles (to be defended in December 2025). As of June 2025, he joined the Defense and Security Unit of Science and Technology (S[&]T) B.V, Delft, The Netherlands.

Peter J.G. Teunissen is Professor of Geodesy and Satellite Navigation at Delft University of Technology, the Netherlands, and a member of the Royal Netherlands Academy of Arts and Sciences. His past academic positions include Head of the Delft Earth Observation Institute, Science Director of the Australian Centre for Spatial Information, and Federation Fellow of the Australian Research Council. He has been research-active in various fields of Earth Observation, with current research focused on the development of theory, models and algorithms for high-accuracy applications of satellite navigation and remote sensing systems.

Christian C.J.M. Tiberius received a Ph.D. on recursive data processing for kinematic GPS surveying from Delft University of Technology, Delft, The Netherlands. He is an Associate Professor at the Geoscience and Remote Sensing (GRS) Department, Delft University of Technology. His research interests include navigation with GNSS and terrestrial radio positioning, primarily for automotive applications.

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