Joint Jammer Detection And Localization For Dependable Gnss

Nowadays a considerable number of applications and services rely on global navigation satellite systems (GNSS) for navigation, positioning and time synchronization. Unfortunately, GNSS signals are usually weak in terms of power and can easily be overwhelmed by unintentional or malicious interference. Indeed, interference severely affects the accuracy of position, velocity, and timing estimates, so much that it may completely disrupt a receiver's operation. For this reason, there is a strong demand for interference detection, localization and mitigation techniques aimed at ensuring integrity, accuracy, reliability, and continuity of GNSS solutions.In this paper, we propose a novel algorithm capable of detecting and localizing an interfering source, by extracting data from a network of sensing nodes and fusing them through an Extended Kalman Filter (EKF). For this purpose, we exploit the concept of system observability as applied to Kalman filtering, to define an eigenvalue-based test on jammer presence to aid subsequent localization. In addition, the proposed algorithm may be applied to completely different contexts, whenever detection and estimation problems must be solved simultaneously, as long as the estimation problem can be solved by a Kalman filter. Numerical simulations of the proposed algorithm, show outstanding detection performance and down to sub-meter localization accuracy.