GNSS Intentional Interference Mitigation via Average KF Innovation and Pseudo Track Updation

摘要

GNSS based navigation is vulnerable to intentional interferences like jamming, meaconing, and spoofing. In the jamming attack, there is no position estimate from the navigation filter. Whereas, in the case of meaconing and spoofing attacks, the navigation track is misguided by projecting the false measurements onto the GNSS receiver. In the above two cases, the measurements are either corrupted by jamming or spoofed. Therefore, to address this problem, this paper presents a novel pseudo-state update and pseudo-covariance update equations for the Kalman filter (KF) at a given epoch to improve the performance of GNSS track. Simulation results reveals that proposed method enhanced the navigation accuracy (by including two adders, two flip flops and a control unit) compared to other traditional methods. Further, it is evident from the results that the proposed approach provided improved performance in track continuity and position root mean square error(PRMSE) compared to existing techniques.