Bayesian detection of interference in satellite navigation systems

In this paper, we propose a novel algorithm to detect/compensate on line interference effects when integrating Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). The GNSS/INS coupling is usually performed by an Extended Kalman Filter (EKF) which yields an accurate and robust localization. How ever, interference cause the GNSS measurement noise to increase unexpectedly, hence degrade file positioning accuracy. In this context, our contribution is twofold. We first study the impact of the GNSS noise inflation on the covariance of the EKF outputs so as to compute a least square estimate of the potential variance jumps. Then, this estimation is used in a Bayesian test which decides whether interference are corrupting the GNSS signal or not. It al lows us to estimate their times of occurrence as well. In this way, the impaired measurements can be discarded while their impact on the navigation solution can be compensated. The results show the performance of the proposed approach on simulated data.