• Jammertest’s dataset Norway 2024 compaign, data collected through an antenna array • PRN-noise like jammer with increasing power over time • Detection, localization and mitigation granted during the processing • Beamforming techniques such as MUSIC, MPDR are used for DoA estimation and mitigation • Winning solution of the 2025 EUSIPCO student challenge Global Navigation Satellite Systems rely on estimating the signal propagation delay and Doppler shift to a set of visible satellites, which in turn allows to determine the receiver position, velocity and timing. However, the presence of interfering signals degrades the estimation of such synchronization parameters, reason why robust solutions must be accounted for. One specific kind of interference is jamming, where a powerful signal is emitted in the same bandwidth as the signal of interest. One possible way to mitigate jamming is to resort to an antenna array. Doing so, spatial diversity can help to estimate the most powerful signal, allegedly the interference, and perform detection, localization and mitigation. In our solution, we propose two methods: the first is an offline processing, which uses snapshots where the interference is the most powerful to allow precise detection and localization of the interferer. The second is an online processing, allowing to perform detection, localization and mitigation in real time of the interfering signal.

In the context of GNSS signal processing, the carrier-to-noise density ratio ( C / N 0 ) is a powerful metric for evaluating GNSS performance, analyzing interference effects, and monitoring reception quality. Although the modeling of C / N 0 in the presence of interference has been extensively discussed in the literature, the specific impact of spoofing remains unexplored. In fact, due to the similar structure between authentic and spoofed signals, the latter directly interferes with the true signal and cannot be considered as an equivalent additional and independent noise source. This paper investigates the impact of spoofing on both true and estimated C / N 0 and proposes analytical expressions for their biases in the presence of spoofing. It reveals situations where the correlator output becomes non-ergodic, inducing divergence between the true and estimated values, as well as significant C / N 0 degradation. Additionally, there is a high dependence on the receiver architecture (estimation method) and spoofing geometry. Finally, beyond GNSS spoofing applications, this study highlights the effect of non-ergodicity on estimation and underscores the importance of studying estimation under non-ergodic conditions.

In modern GNSS receivers, the Automatic Gain Control (AGC) monitors the received signal level to optimize quantization and mitigate interference.This paper characterizes the jamming and spoofing impact on AGC and received signal.It first expresses the AGC gain as a function of the received signal level.Under nominal conditions, the AGC leverages the ergodic properties of the received signal to estimate its level over time.Two physical quantities, namely time-based power and signal distribution, are typically considered.However, in the presence of interference, these ergodic properties are no longer guaranteed, posing challenges in modeling the behavior of these quantities.This paper proposes a probabilistic framework for interpreting temporal estimation and computing time-based power and distribution in order to characterize AGC gain under jamming and spoofing.First, this study models the spoofing impact for both unique and multiple emitted spoofing signals as a function of the re-radiated noise power and the spoofing signals' characteristics (e.g., number of emitted signals, amplitudes, modulation).Furthermore, it reveals the non-uniformity of jamming chirp phase, which introduces distortions in power and signal distribution, consequently affecting AGC gain, and demonstrates the convergence of the jamming signal toward a continuous wave signal at high frequencies.

In the context of Global Navigation Satellite Systems (GNSS), the measure of the Carrier-to-Noise Density Ratio (C/N0) plays a critical role in evaluating received signal quality, particularly in the presence of Radio-Frequency Interferences such as from a meaconer. This paper presents a dual-step strategy to characterize C/N0under meaconer influence. First, the theoretical expression of the real C/N0is computed. Second, a model to predict meaconer-induced distortion on the Moment Method (MM) and the Narrowband-Wideband Power Ratio (NWPR) estimators is derived. The comparison between real and estimated C/N0reveals a deviation between NWPR, MM and real C/N0. In essence, this work enhances comprehension of meaconing-induced C/N0distortion.

This letter studies a new expectation maximization (EM) algorithm to solve the problem of circle, sphere and more generally hypersphere fitting. This algorithm relies on the introduction of random latent vectors having a priori independent von Mises-Fisher distributions defined on the hypersphere. This statistical model leads to a complete data likelihood whose expected value, conditioned on the observed data, has a Von Mises-Fisher distribution. As a result, the inference problem can be solved with a simple EM algorithm. The performance of the resulting hypersphere fitting algorithm is evaluated for circle and sphere fitting.

Multipath (MP) remains the main source of error when using global navigation satellite systems (GNSS) in a constrained environment, leading to biased measurements and thus to inaccurate estimated positions. This paper formulates the GNSS navigation problem as the resolution of an overdetermined system whose unknowns are the receiver position and speed, clock bias and clock drift, and the potential biases affecting GNSS measurements. We assume that only a part of the satellites are affected by MP, i.e., that the unknown bias vector has several zero components, which allows sparse estimation theory to be exploited. The natural way of enforcing this sparsity is to introduce an ℓ1regularization associated with the bias vector. This leads to a least absolute shrinkage and selection operator problem that is solved using a reweighted-ℓ1algorithm. The weighting matrix of this algorithm is designed carefully as functions of the satellite carrier-to-noise density ratio (C/N0) and the satellite elevations. Experimental validation conducted with real GPS data show the effectiveness of the proposed method as long as the sparsity assumption is respected.

Multipath remains the main source of error when using global navigation satellite systems (GNSS) in constrained environment, leading to biased measurements and thus to inaccurate estimated positions. This paper formulates the GNSS navigation problem as the resolution of an overdetermined system, which depends nonlinearly on the receiver position and linearly on the clock bias and drift, and possible biases affecting GNSS measurements. The extended Kalman filter is used to linearize the navigation problem whereas sparse estimation is considered to estimate multipath biases. We assume that only a part of the satellites are affected by multipath, i.e., that the unknown bias vector is sparse in the sense that several of its components are equal to zero. The natural way of enforcing sparsity is to introduce an I1regularization associated with the bias vector. This leads to a least absolute shrinkage and selection operator (LASSO) problem that is solved using a reweighted I1algorithm. The weighting matrix of this algorithm is designed carefully as functions of the satellite carrier to noise density ratio and the satellite elevations. The smooth variations of multipath biases versus time are enforced using a regularization based on total variation. An experiment conducted on real data allows the performance of the proposed method to be appreciated.