Navigation in GPS spoofed environment using m-best positioning algorithm and data association

Abstract

Intentionally misguiding a global positioning system (GPS) receiver has become a potential threat to almost all civilian GPS receivers in recent years. GPS spoofing is among the types of intentional interference, in which a spoofing device transmits spoofed signals towards the GPS receiver to alter the GPS positioning information. This paper presents a robust positioning algorithm, followed by a track filter, to mitigate the effects of spoofing. It is proposed to accept the authentic GPS signals and spoofed GPS signals into the positioning algorithm and perform the robust positioning with all possible combinations of authentic and spoofed pseudorange measurements. The pseudorange positioning algorithm is accomplished using an iterative least squares (ILS). Further, to efficiently represent the robust algorithm, the M-best position algorithm is proposed, in which a likelihood-based cost function optimizes the positions and only provides M-best positions at a given epoch. However, during robust positioning, the positions evolved due to spoofed pseudorange measurements are removed to overcome GPS spoofing. In order to remove the fake positions being evolved owing to wrong measurement associations in the ILS, a gating technique is applied within the Kalman filter (KF) framework. The navigation filter is a three-dimensional KF with a constant velocity (CV) model, all the position estimates evolved at a specific epoch are observations. Besides, to enhance this technique's performance, the track to position association is performed by using two data association algorithms: nearest neighbor (NN) and probabilistic data association (PDA). Simulations are carried out for GPS receiver positioning by injecting different combinations of spoofed signals into the receiver. The proposed algorithm's efficiency is given by a success rate metric (defined as the navigation track to follow the true trajectory rather than spoofing trajectory) and position root mean square error (PRMSE). © 2013 IEEE.