With the increase of jamming and spoofing attacks targeting global navigation satellite systems (GNSS) in the Baltic Sea region, the demand is growing, to establish an alternative navigation system which can provide a reliable source of position, navigation and timing (PNT) information. The most promising alternative PNT system for the Baltic Sea region is known as R-Mode. R-Mode makes use of very high frequency (VHF) and medium frequency (MF) signals transmitted from maritime ground infrastructure to provide positioning and timing services in their overlapping coverage areas. Currently supported by several countries of the Baltic Sea region and under the umbrella of the project ORMOBASS (pre-Operational R-Mode Baltic Sea System), the number of available transmitters is rapidly increasing, with the system foreseen to be declared pre-operational by the end of 2026.

Several measurement campaigns have shown that MF R-Mode can provide horizontal positioning accuracies approaching 10 m (95%) under optimal propagation conditions. Less favorable conditions occur in case one or several transmitters provide unstable signals with jumps within a range of several meters or the propagation of the groundwave from the MF R-Mode transmitter to the receiver is affected by changes in the environmental conditions in between. These effects can cause significantly increased positioning errors.

This work presents how the authors tackled this challenge with a differential R-Mode approach. The R-Mode reference station is placed at a known location. It receives MF R-Mode signals from different transmitters. The role of the reference station is to assess the quality of the received signals and compute corrections which can then be transmitted to the users to increase the R-Mode ranging accuracy. The approach is explained in details and results from real measurement campaign are presented to demonstrate the validity of the developed technique.

The continuing growth of Low Earth Orbit (LEO) missions intensifies the need for autonomous, on-board precise orbit determination (POD) capable of ensuring the accuracy, continuity and reliability required by next-generation LEO platforms operating in a highly dynamic environment. This work compares the achievable performance and computational load of two different GNSS-based real-time POD architectures. Both solutions are evaluated using public ESA LEO GNSS datasets from Sentinel-6A (S6A) and Swarm-B (SWB).

The first architecture consists of the sequential combination of GMV’s Gsharp® PPP plus a dynamic least-squares-based filter for the Orbit Determination and Prediction (ODP). Gsharp has been evolved into a 1 Hz real-time, on-board precise orbit and clock determination algorithm based on an Extended Kalman Filter (EKF) processing uncombined, multi-frequency, multi-constellation GNSS measurements, including Galileo’s High Accuracy Service (HAS). The resulting PPP coordinates are accumulated by the ODP filter, which refines the orbit and predicts the satellite trajectory by estimating key dynamical parameters (state vector, solar radiation pressure coefficients, atmospheric drag and nine empirical acceleration parameters), providing a dynamically consistent orbit solution that enhances the initial PPP positions. With S6A data (GPS+Galileo+HAS), the PPP solution achieves <25 cm 3D RMS and <1 ns clock RMS, improved by ODP to <20 cm. For SWB (8 GPS channels only), errors are <60 cm for PPP and <30 cm for ODP. ODP performs the forward-propagation of the estimated orbit achieving 3D RMS of ~16 cm at 10 min, 18 cm at 30 min and 19 cm at 50 min for S6A and corresponding values of ~34 cm, 52 cm, and 60 cm for SWB.

The second architecture presents a unified on-board solution that integrates GNSS measurement processing and dynamic orbit estimation within a single EKF. By estimating the relevant orbit parameters in real time with an advanced orbit determination model, the filter provides a physically consistent navigation solution and eliminates the need for a separate ODP stage. This configuration achieves an RMS clock a synchronization error <0.5 ns for S6A. The position 3D RMS is <10 cm for S6A and <15 cm for SWB in estimation, while forward-propagation results confirm the robustness of the approach, with S6A position RMS of ~10 cm at 10 min, 15 cm at 30 min and 20 cm at 50 min, and corresponding SWB values of ~15 cm, 30 cm and 40 cm.

These results highlight GMV’s ability to offer complementary on-board navigation architectures tailored to different mission and platform constraints. The modular Gsharp+ODP chain provides maximum flexibility, enabling independent deployment of PPP or ODP capabilities when required. Conversely, the unified dynamic Gsharp filter consolidates both functions into a single, high-performance algorithm optimized for minimal CPU and memory consumption, making it especially attractive for resource-constrained LEO spacecraft.

Overall, the study provides a consolidated view of GMV’s advances toward autonomous, high-precision, on-board navigation for LEO platforms, demonstrating that both architectures are technically mature and ready to support future constellation missions, resilient Positioning, Navigation and Timing (PNT) services and spacecraft requiring dependable real-time orbit knowledge.

The use of Global Navigation Satellite System (GNSS) carrier phase measurement is essential to obtain high accuracy GNSS positioning solutions. However, it requires us to deal with the carrier phase ambiguity, an unknown constant parameter affecting the carrier phase measurements. Usual algorithms, such as Real-Time Kinematics (RTK) or Precise Point Positioning (PPP), estimate the ambiguities as new variables included in the state vector. Another way is to perform difference of carrier phase measurements from the same satellite at two epochs, the so called Time-Differenced Carrier Phase (TDCP), which gives an observation no longer depending on the ambiguity, but on the positions at the two epochs. Using TDCPs simplifies the estimation problem by reducing the number of estimated states, and provides interesting accuracy gain, thanks to their high accuracy.

Analytical modeling of the errors affecting TDCP is difficult due to the limited knowledge of the time correlation of the various error terms affecting the carrier phase measurement. When performing the time-difference, time-correlated errors will be reduced, while uncorrelated errors will increase. In addition, some time-varying errors may also change with some unknown evolution.

To face these difficulties, this paper presents a characterization of the error affecting TDCP observations based on real data analysis. A residual analysis is performed on data from IGS stations. Special attention has been given to the cycle slip occurrence and ephemeris change, to stay in the hypothesis of a constant ambiguity.

The results provide useful information to better tune positioning algorithms using TDCP. In particular, we observe that the TDCP noise model does not depend on the elevation of the satellites. We also investigated the dependence of the noise model standard deviation on the time interval and found an affine trend.
To demonstrate the validity of the proposed model, an FGO-based solution will be computed using code and TDCP observations. The results will then be compared under different noise models applied to the TDCP observations.

Galileo has pioneered the global introduction of a data authentication service, named Open Service Navigation Message Authentication (OSNMA), allowing end-user receivers the capability to verify the Galileo navigation messages source authenticity and data integrity.
The OSNMA is provided to the end-users over the Galileo E1 I/NAV 20bps capacity, via the Timed-Efficient Stream Loss-Tolerant Authentication (TESLA) protocol.
Following an intense set of verification and E2E validation activities, started on the 18th November 2020 with the first transmission, OSNMA has entered officially in its initial Service Phase as of the 24th July 2025.
OSNMA has been successfully penetrating the GNSS receivers’ market, with major players integrating OSNMA functionality into their products well before the official service declaration. This early adoption enabled experimentation with receiver implementations, supporting EC/EUSPA and ESA in consolidating and validating the publicly available receiver guidelines and the OSNMA ICD.
Over the last few years, several receiver models in the market have officially incorporated OSNMA and, as of today, the number of OSNMA-enabled receivers are counted by millions.
This work aims at providing a methodology to characterise and compare OSNMA performance across different receiver technologies available in the market, under challenging operational conditions. By “challenging operational environment”, we refer to scenario predominantly affected by signal fading and blockage, as typically experienced in deep urban environments.
This work will address:
• The Test Setup tools and methodologies used by the ESA receiver team for the execution of mobile testing activities in urban areas, such as in Rotterdam downtown
• The OSNMA-Enabled Mass-Market Receivers test bench available at ESA/ESTEC
Note: Receiver manufacturers will be kept anonymous throughout this abstract and the associated paper.
• The relevant Key Performance Indicators (KPIs)
• The measured OSNMA performance across the different receiver technology and derivation of relevant implemented optimisation to maximize OSNMA data extraction from SiS
Performance assessment on field will be carried out over three different levels:
1. Positioning Level:
o PVT availability: Percentage of time, within the observation window, during which the receivers produce a valid 3D position, regardless of their accuracy.
o PVT accuracy: 95% percentile of horizontal and vertical accuracy, measured only over valid PVT epochs.
2. OSNMA PVT level:
o Number of visible satellites per epoch from which the receiver is collecting OSNMA data.
o Number of Galileo SVIDs for which Clock and Ephemeris data are successfully authenticated at each MACK Epoch (currently every 30s as per current OSNMA SIS configuration).
o Number of Galileo SVIDs with timing parameters (GST to UTC and GGTO) successfully authenticated within the corresponding tag type dissemination epoch (every 60 seconds as per current OSNMA SIS configuration).
o Time between Consecutive TESLA Key Authentications.
o Time between Consecutive Authentications of at least 4 different satellites.
Additionally, receiver’s raw bits are analysed and processed offline to derive preliminary assessment of Time to First Authenticated Data, measured across different receiver OSNMA start-up assumptions and different OSNMA processing optimisations exploiting an open-source python based OSNMA library.