Reliance on GNSS continues to grow across the application space: from critical infrastructure to the burgeoning autonomy sector. At the same time, instances of radio frequency interference (RFI) affecting GNSS are becoming more widespread and can endanger lives with such dependence on GNSS. Currently, GNSS jamming and spoofing has become widespread around areas of conflict, especially surrounding Russia and Ukraine. But this has also spread more broadly and numerous reports, using Automatic Dependent Surveillance-Broadcast (ADS-B) aviation-based sensors, have come out of regions around the south Baltic Sea, significantly away from the current conflict regions. To explore the extent of the RFI, a low size, weight, power and cost (SWaP-C) GNSS monitor was deployed in Gdynia, Poland, which confirmed the presence of the RFI at the Gulf of Gdansk on the Polish coast. This initial effort was extended to deploy networked GNSS intermediate-frequency (IF) samplers around the Gulf of Gdansk on the Polish Baltic coast to further understand and attempt to localize the source of the interference using time-difference-of-arrival (TDOA). TDOA results are presented from the period of April-June 2025, where a circular spoofer was detected and localized, along with a separate jammer. The IF data allowed for the tracking of the circular spoofing signal, giving valuable insight into this emerging threat.

In precise point positioning (PPP) solutions, stochastic modeling of the receiver clock parameter affects solution stability, especially the station up component. This study analyzes GPS + Galileo static and kinematic PPP solutions for 14 GNSS stations equipped with ultra-stable hydrogen masers, utilizing real-time orbit and clock products, such as HAS, IGS, and CNES, as well as final products from CODE. The solution with modeling of the receiver clock parameter enhances stability and precision compared to the reference solution, in which the receiver clocks are estimated independently for each epoch. In static PPP solutions, receiver clock modeling reduces spikes in the clock parameter and improves short-term stability. The most notable improvement of the station up component reaches up to 59, 21, 25, and 38% for HAS, IGS, CNES, and CODE, respectively, in terms of the interquartile range (IQR) of the estimated parameter during the first three processing hours. For kinematic PPP solutions, the maximum reduction of the up component standard deviation is 38, 50, 18, and 26% for HAS, IGS, CNES, and CODE, respectively across all analyzed GNSS stations. Additionally, the IQR analysis of epoch-wise differences in the station up component indicates even greater enhancements, with reductions reaching up to 63, 70, 52, and 83% for HAS, IGS, CNES, and CODE, respectively. Epoch-wise differences indicate short-term stability. The proposed clock modeling improves both the common clock parameter and the vertical component, enhancing PPP solution by increasing stability (reduced variability) and precision (reduced dispersion) across real-time and post-processed GNSS products.

Urban areas are challenging environments for satellite navigation because of large multipath errors and frequent satellite signal loss. Urban air mobility (UAM) has a concept for operating aircraft in urban environments, where it is important to have accurate and integrity-assured positions. In response, this paper proposes a receiver autonomous integrity monitoring (RAIM) method using the time-differenced carrier phase (TDCP). TDCP measurements are robust to multipath and do not require the resolution of integer ambiguity because time differences eliminate any integer ambiguity. TDCP RAIM differentiates measurements at fixed time intervals and accumulates the estimated displacement vector. In this study, a general formulation for positioning and protection level of TDCP RAIM is introduced. The performance of TDCP RAIM was evaluated through simulation and validated using real urban measurement data, and was compared with that of Relative RAIM. The results indicate that TDCP RAIM is robust to changes in visible satellites and provides continuous positioning and protection level.

Global Navigation Satellite Systems (GNSS) tomography is a modern technique that leverages GNSS satellites and ground-based network stations for meteorological applications, with a particular focus on studying the dynamics of water vapor. Over the past two decades, various approaches have been proposed, but assessing the accuracy of these methods remains challenging. In this study, we introduce an empirical stochastic model for GNSS tomography. This model is formulated as a fully populated covariance-variance matrix that incorporates the variance of the GNSS-Integrated Water Vapor (IWV) solution, the spherical distance between station-satellite signal paths, and an empirical covariance-variance function. Our findings indicate that combining the functional model with the stochastic model significantly improves the estimation of unknown parameters, suggesting a method for enhancing the accuracy and reliability of GNSS tomographic solutions. We compared the standard tomography solution, both with and without stochastic information, with data from radiosondes, ERA5 reanalysis, and the WRF model. The results demonstrate that incorporating the empirical stochastic model can improve the solution accuracy by 10%. More importantly, it provides a valuable tool for evaluating the trustworthiness of tomographic solutions.