The AsteRx EB is aimed at industrial robots, port logistics, marine platforms, and scalable automation systems — markets where accuracy requirements are demanding but where the volume economics of a high-end OEM module may not be practical. The IP67-rated housing protects against weather and dust, and the compact enclosure is designed to reduce installation time and simplify integration.

On the positioning side, the receiver incorporates Septentrio’s GNSS+ algorithms for performance in environments that challenge standard GNSS — foliage, urban multipath, proximity to interference sources. In a dual-antenna configuration, it delivers sub-degree heading alongside RTK-level positioning, covering applications that require both location and orientation. The AIM+ anti-jamming and anti-spoofing technology is built in, addressing the growing priority of interference resilience in industrial and autonomous systems.

“AsteRx EB is an ideal boxed receiver for customers who need reliable, resilient, and highly accurate positioning in a compact form factor and at a price point that makes rapid scale-up possible,” said Danilo Sabbatini, Product Manager at Septentrio.

The AsteRx EB slots into Septentrio’s enclosed receiver lineup between the mosaic-go evaluation platform and the AsteRx RB3, which is positioned for applications requiring the highest level of mechanical and environmental protection. Septentrio notes the EB can also serve as an evaluation platform for integrators assessing its positioning technology before committing to a production architecture.

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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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Across Europe and the United States, thousands of bridges and other elevated roadways are ageing, with many approaching or exceeding their design lifespans. In Italy alone, over 3,600 such structures span the 5,700 km Autostrade network, many in urgent need of inspection and maintenance, but traditional structural health assessment remains expensive and logistically complex.

PONTI builds on S4L’s GAMBE project, which used data from drivers’ smartphones to detect bridge vibrations. PONTI furthers this concept with dedicated hardware and cloud-based analytics to create a low-cost, scalable monitoring system.

Data are transmitted, either directly or via the PONTI app, to a cloud-based storage and processing centre, which integrates acquisition, filtering, and analytic functions. The system architecture, built around virtualized modules (firewall, broker, server, storage, and web server), supports secure data flows and web-based dashboards for users, operators, and administrators.

Affordable and scalable

At a recent ESA-hosted event, S4L’s Fabio Gerace presented the results of the PONTI research program, which involved laboratory validation and field demonstrations. During one testing campaign, 21 buses equipped with PONTI Boxes recorded over 62,000 crossings on Italian overpasses.

Multi-level processing transformed raw accelerometer and GNSS data into frequency spectra and spectrograms that reveal each structure’s dynamic response. While the system correctly acquired and processed data, results showed that the 1 kHz sampling rate limited frequency resolution; modal frequencies below 15 Hz could not be reliably detected. GNSS connection losses in tunnels and on certain types of overpasses also affected data quality.

Despite these challenges, the demonstrations confirmed the feasibility of vehicle-based, satellite-referenced structural monitoring. Gerace said results point towards a PONTI Box Plus, which would involve 15 kHz sampling and deployment on a dedicated, AI-assisted mobile laboratory van. The team also intend to extend the technology to assessment of road surface conditions.

Tragic overpass collapses like that which occurred at Genoa’s Ponte Morandi have made clear the human and economic cost of delayed monitoring. Gerace said PONTI exemplifies how space-derived position, navigation and timing (PNT) technologies can transform civil infrastructure management, offering road authorities and concessionaires an affordable, continuous, and scalable solution to safeguard public safety and optimize maintenance investments.

Ponti (Position Navigation Timing for Viaducts Monitoring) was co-funded under ESA’s NAVISP Element 2, which aims to increase European competitiveness in PNT.

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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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For decades, road resurfacing has depended on constant-depth milling, a road resurfacing method in which the asphalt layer is removed to a uniform depth across the entire surface, regardless of variations in the underlying pavement profile or drainage needs. This legacy method locks in surface distortions and limits the lifespan of the repaired road.

Developed under ESA’s NAVISP framework, Exact Control System’s GreenAsph 4.0 concept introduces 3D differential milling capable of removing each asphalt layer according to a precise geometric model.

The technology combines dual-GNSS receivers, cross-slope sensors, and edge-mounted antennas with a data-driven control algorithm that continuously adjusts the milling depth and drum orientation. Each pass not only improves smoothness but also captures data for the next layer’s digital design.

The system comprises a scalable microservice architecture hosted on Microsoft Azure. Kubernetes-orchestrated modules process data from multiple milling machines in parallel, synchronizing design updates via secure cloud links. Operators view live corrections through a simplified interface, reducing the need for extensive pre-survey and post processing.

At a recent ESA-hosted event, Exact Control System CTO Vítězslav Obr presented the final results of the GreenAsph 4.0 project, which involved extensive testing of the new system. Field demonstrations in cooperation with Prague’s road authority (TSK) confirmed measurable benefits: the international roughness index improved from 5 m/km to 0.6 m/km, 98 percent of cross-slopes were corrected, and service life projections rose by 30 to 50 percent.

Quantifiable improvement

Traditional mobile LiDAR surveys can deviate by ±50 mm, large enough to distort drainage gradients. Exact Street’s simulation environment and iterative on-site validation reduce mean GNSS trajectory error from 20 mm to between 2 and 4 mm, a five- to ten-fold improvement. That precision allows true layer-selective recycling, separating binder and surface materials for reuse instead of mixing them into waste.

Building on these results, Obr highlighted key lessons learned, such as the importance of accurate cross-slope calibration and the inclusion of 3D selective milling specifications in project tenders, which are now being applied in new pilot programs across Europe, Canada, and the U.S. With GreenAsph 4.0, GNSS moves beyond mapping into active control, closing the loop between design, construction, and long-term sustainability. “We are not only smoothing roads,” Obr said. “We are digitizing the resurfacing process itself.”

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The system addresses a persistent problem in civil engineering, obtaining timely and integrated information on ground and structural movement from multiple sensor types in challenging environments.

“GEOvis is a monitoring platform for visualization,” said Amar Camo, Project Manager at Amberg Infra 7D, speaking to Inside GNSS at InterGEO 2025 in Frankfurt. “You can use it to visualize your monitoring data, but we can also collect data ourselves or host clients that have their own total stations or any sensors.”

The platform centralizes data from tunnels, railways, bridges, and urban construction sites, supporting real-time analysis and automated alerts. In tunneling and excavation, it tracks ground deformation and settlement. For rail projects, GEOvis provides millimeter-level precision to detect movement along tracks, while in dense urban environments it monitors potential displacement affecting nearby buildings or utilities.

At its core, GEOvis is a flexible web-based system that scales from manual campaigns to fully automated monitoring networks. It supports a broad sensor range, including total stations, inclinometers, crackmeters, vibration sensors, fiber-optic instruments, and GNSS receivers. The integration of GNSS data enhances the system’s capability to detect three-dimensional displacements over time, particularly in locations where optical instruments face line-of-sight limitations.

GNSS among a range of sensors

GNSS delivers absolute positioning and long-term stability, allowing GEOvis to import, process, and visualize satellite-derived data alongside terrestrial measurements. The platform’s architecture supports custom integration, a key differentiator according to Camo: “If the user comes and says, ‘Hey, we have this sensor’ we can integrate it into our software. It’s not a problem for us to adapt to what the user needs; our engineers are on it straight away.”

Founded in 2024, Amberg Infra 7D builds on more than 60 years of Amberg Group expertise in surveying, rail, and tunnel inspection. “The Amberg Group started as a normal survey company,” Camo said. “Then they created products that were very rail-oriented; Switzerland has a lot of tunnels and rail work, and they’re very careful about upkeep and inspection.”

Complementing GEOvis, the company offers Amberg Navigator, a field application that connects directly to total stations, laser scanners, or GNSS receivers for fast and guided measurements. “Navigator allows for very simple and easy measurements, say, in a tunnel,” Camo said. “You just point the instrument, it actually figures out where it is.”

Together, GEOvis and Navigator illustrate Amberg Infra 7D’s integrated approach to digital infrastructure monitoring, combining GNSS precision, real-time visualization, and adaptable field technology within a single ecosystem.

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The toll will apply to all motorways and select provincial and municipal main roads across the Netherlands. Vehicles affected include both domestic and foreign HGVs – vehicle categories N2 and N3. Notably, emission-free vehicles up to 4,250 kg are exempt from the charge.

Current truck transport in the Netherlands relies on flat or weight-based charges, lacking precise distance measurement. This limits fairness, efficiency, and environmental accountability, while growing freight volumes strain infrastructure and increase emissions.

The new toll rates will vary based on the vehicle’s Euro emission class, with lower rates for vehicles emitting less CO2, encouraging the adoption of cleaner technologies within the freight sector. To facilitate accurate tolling, trucks will be required to install certified OBUs that utilize GNSS to track distance traveled. Truck operators will be responsible for contracting with authorized service providers to obtain and maintain the necessary equipment.

The Dutch vehicle authority, RDW, has appointed Triangle, a consortium including Via Verde Portugal, Ascendi O&M, and Yunex GmbH, as the primary service provider for the new system. The net proceeds from the toll will be reinvested into the transport sector, focusing on sustainability and innovation. This includes subsidies for the acquisition of zero-emission vehicles and the development of charging infrastructure, aligning with the Netherlands’ broader environmental objectives.

Implementation timeline

The official start is set for 1 July 2026. By then, all affected vehicles will need to have a functioning OBU and a contract with an accredited service provider.

Dutch officials say the initiative represents a significant step towards modernizing road user charging in the Netherlands, leveraging GNSS to create a more efficient and environmentally friendly freight transport system.

GNSS-based tolling generates high-resolution, anonymous traffic data that can help authorities optimize infrastructure planning, anticipate congestion, improve road safety, and coordinate maintenance more efficiently than conventional schemes.

The initiative also aligns with broader European trends toward GNSS-based road pricing, reflecting similar initiatives in Germany, Austria, and France that integrate GNSS for smart, sustainable freight management.

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The software solution is designed to enhance receiver reliability and accuracy in autonomous and connected vehicles. By boosting line-of-sight signals and rejecting non-line-of-sight signals, S-GNSS Auto aims to help vehicles maintain accuracy in challenging environments like urban canyons and forest roads. Delivered as a simple firmware upgrade, it transforms GNSS into a more reliable, powerful component of the ADAS stack in areas where traditional solutions fall short.

The companies are due to showcase the latest test results from the collaboration, including data from various signal-challenged environments such as Tokyo, Frankfurt and the Black Forest in Germany.

“As Asia’s leading advanced automotive technology show, Automotive World 2025 is the perfect place to showcase our S-GNSS Auto software and how it helps automotive manufacturers use GNSS to its full potential, especially for ADAS and V2X applications,” said Manuel Del Castillo, VP business development at FocalPoint. “Our technology, embedded in STMicroelectronics’ Teseo GNSS chips, enhances the end users’ trust in the automotive systems where STMicroelectronics is used. This can only trigger a positive impact on systems designed to improve end users’ safety and convenience.”

The solution will be on show at the Automotive World show 2025, which is taking place on September 17-19 at Makuhari Messe, Japan.

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Founded by Jeff Sanders, a well-known SME in the SIGINT community, UHU Technologies brings decades of expertise to one of today’s most pressing challenges: assuring trusted positioning navigation and timing (PNT) and providing unmatched PNT Situational Awareness (PNT SA) in contested environments.

Inside GNSS sat down with Sanders to discuss UHU’s two flagship products—the UHU 1000 and the Northstar—and the company’s mission to enhance real-time awareness, threat detection, and operational confidence for all GPS-enabled operations.

THE UHU 1000: DEFINING NEW STANDARDS IN PNT SITUATIONAL AWARENESS

At the core of UHU Technologies’ offering is the UHU 1000, a 7-channel system specifically designed to combat advanced GPS attacks while simultaneously locating their source. “Our goal with the UHU 1000,” Sanders explained, “was to create a system that delivers comprehensive PNT situational awareness—not just defending against attacks but enabling users to understand, visualize and respond to them in real time.” 

Key features of the UHU 1000 include:

• Impervious to Spoofing: Spatial validation confirms the authenticity of each satellite by validating angle-of-arrival.

• Adaptive Nulling Anti-Jam: Real-time elimination of interfering signals, preserving operational continuity.

• Non-Adaptive Nulling Anti-Spoof: Separates spoofed signals for continuous situational clarity.

• Integrated Timing Receiver: Maintains precise timing even under GPS denial conditions.

• I/Q Recording: Captures detailed signal environments for forensic PNT analysis.

• Situational Awareness GUI: Multiple operational views—Sky, Map, Chart and Spectral—offering full visualization of the GNSS environment.

Housed in a compact 1U 19-inch rackmount chassis, the UHU 1000 empowers users to maintain trusted PNT while achieving continuous, actionable awareness of the threat landscape.

“We designed it to ensure that operators always know not just that there’s a problem, but exactly where it is and how it’s affecting their mission,” Sanders said.

THE NORTHSTAR: EXPANDING PNT SA TO CRITICAL INFRASTRUCTURE

While the UHU 1000 is geared for military and high-security governmental operations, the Northstar system brings robust PNT Situational Awareness to civil infrastructure—a rapidly growing vulnerability as ports, airports and financial systems depend more heavily on GPS.

“Northstar was built to give operators the ability to see—in real time—what’s happening in their GPS environment, with the power to react immediately,” Sanders said.

Highlights of the Northstar system include:

• 4-Channel Antenna System: Differentiates authentic GNSS signals from threats via spatial processing.

• Rapid Threat Detection: Provides immediate alerts and visualizations when spoofing or jamming occurs.

• Automatic Timing Holdover: Maintains operational timing continuity during attacks.

• Angle-of-Arrival Threat Localization: Displays the direction and magnitude of threats to enhance operational decision-making.

• Remote RF Disconnect: Automatically protects external GPS receivers by isolating corrupted signals.

• Event-Based I/Q Recorder: Enables in-depth analysis for sustained PNT SA improvements.

With a user interface designed around Sky View, Map View and Time Machine replays, Northstar transforms GPS-dependent facilities into PNT-aware operations hubs.

“Northstar isn’t just monitoring the sky,” Sanders emphasized. “It’s giving operators a window into the PNT battlespace.”

REAL-WORLD IMPACT: ENHANCING PNT SA AT PORTS, BORDERS AND AIRPORTS

Sanders recounted real-world deployments where enhanced PNT situational awareness made a critical difference. At a major U.S. port, a rogue refrigeration unit was emitting interference, crippling GPS container tracking. A UHU rapid deployment kit quickly located and isolated the source, restoring operational stability.

“PNT SA is about understanding disruptions before they become operational crises,” Sanders explained.

“Knowing when, where and how GPS is being attacked along a sensitive border isn’t just a bonus—it’s critical for mission success and international safety,” Sanders said.

RAPID DEPLOYMENT: DELIVERING IMMEDIATE PNT AWARENESS

Both the UHU 1000 and Northstar were designed from the ground up to provide rapid, on-the-move situational awareness:

• Installation: Fully operational within one hour.

• Mobility: Easily mounted on vehicles using magnetic mounts and stowed in low-profile duffel bags.

• Real-Time Data Streaming: Secure transmission over VPN-enhanced cellular networks.

“Fast, reliable PNT SA shouldn’t require a full engineering team on site,” Sanders said. “Our systems bring clarity to the field immediately.”

A VISION FOR BROADER PNT RESILIENCE: AVIATION, ENERGY AND MARITIME

With civil aviation, maritime and power grid operators now confronting growing GPS vulnerabilities, UHU Technologies is expanding the reach of its PNT Situational Awareness solutions.

Sanders cited a recent incident at a major airport where GPS interference disrupted navigation, forcing pilots to revert to Instrument Landing Systems (ILS) and causing operational delays.

“Without real-time PNT SA, critical infrastructure becomes reactive rather than proactive,” Sanders said. “Our mission is to change that.”

Upcoming demonstrations, including at NAVFEST, will showcase how UHU’s systems can be networked across facilities for distributed, collaborative PNT threat detection.

INTEGRATING PNT SA INTO DOD PROGRAMS OF RECORD

As GPS jamming and spoofing incidents escalate worldwide, the future of military navigation will depend heavily on integrated PNT Situational Awareness. Key Department of Defense (DoD) Programs of Record, particularly in areas such as resilient communications, aviation command and control, autonomous systems, and expeditionary logistics, are increasingly recognizing the need to incorporate field-proven technologies like UHU’s. Real-time detection, localization and mitigation of PNT threats must become intrinsic to platform and mission system designs. 

By integrating UHU’s advanced capabilities, these programs can move from a model of after-the-fact vulnerability assessment to a proactive, resilient posture—ensuring continuous mission assurance even in PNT contested or denied environments.

PNT SA AS A CORE OPERATIONAL CAPABILITY

Reflecting on UHU Technologies’ strategy, Sanders summarized the new reality:

“You can’t defend GPS today without superior PNT Situational Awareness. It’s no longer about responding to disruption—it’s about knowing disruption is happening before it impacts your mission.”

With the UHU 1000 and Northstar, UHU Technologies is redefining the standard for resilient, real-time awareness—transforming PNT from a passive dependency into an active operational advantage.

As the global GNSS landscape grows more contested, UHU Technologies ensures operators across sectors will not only survive—but thrive—with trusted PNT.

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Using accelerometers, inertial measurement units (IMUs) and GNSS positioning, the Road Sounder system transforms vehicle fleets into road surveyors capable of detecting cracks, surface roughness, potholes, and other defects in real time. The technology aggregates data into a centralized system for effective pavement management, enabling municipalities and service providers to optimize maintenance operations.

The network consists of multiple Road Sounder Cells (RSCs), small, lightweight devices equipped with sensors and GNSS receivers. The devices are deployed in vehicles, where they collect data such as road profiles and roughness, which is then transmitted to the cloud for analysis. Real-time data mining enables the prediction of pavement degradation, so public administrations can plan maintenance interventions more efficiently. The result is a reduction in repair costs and improved road quality and driving experience

With the cost of road reconstruction now often reaching €100,000 per kilometer, poor maintenance can hit hard in terms of a city’s expenses. In Rome, officials recently allocated €10 million to catalog and repair potholes on its 800 kilometers of roads.

Smart integration

A key advantage of the new system is its ability to analyze road roughness, estimate profiles, and detect defects with high accuracy, allowing precise forecasting of road maintenance needs. Road Sounder employs GNSS-aided inertial navigation system (INS) algorithms to ensure precise localization, even during poor GNSS conditions and signal outages. IMU integration adds robustness, ensuring reliable performance in tunnels, extreme urban canyons and other challenging environments.

Data from the new devices is processed in real time using MQTT, a lightweight messaging protocol tailored for IoT applications with limited bandwidth, ensuring efficient cloud-based data aggregation and road condition visualization. New and innovative, data-driven insights are also possible, which could lead to smarter, more timely interventions, further extending the lifetime of road infrastructure.

Project partners believe the market for Road Sounder services is large and growing. An agreement with a potential customer has been reached for the implementation of the new system in a fleet of vehicles already being used in maintenance services by the public administration in Rome.

The Road Sounder team is also planning new market actions targeting mid-sized cities with 1,000-20,000 inhabitants and covering road networks spanning 50-150 kilometers. Future developments could include the integration of artificial intelligence for improved, automated defect detection.

Road Sounder is funded under ESA’s NAVISP program, aimed at driving European industrial competitiveness and innovation in the positioning, navigation, and timing sector.

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