Impact of Target Tracking Module in GPS Spoofer Design for Stealthy GPS Spoofing

This paper presents six different filtering algorithms (Kalman Filter (KF), Reduced State filter (RSF), Separate Covariance filter (SCF), Autonomous Multiple Model (AMM), Generalized Pseudo Bayesian (GPB1 and GPB2), Interactive Multiple Model (IMM)) and its performances to evaluate the impact of target tracking module on GPS spoofing. GPS spoofers are famous for misleading the GPS receivers by imposing mimic GPS signals. The spoofer incorporates the external delays to the captured authentic satellite signals and retransmits towards the GPS receiver to accomplish spoofing. The external delay is a function of the distance between the spoofer and GPS receiver. Therefore, there is a need to estimate the time-varying dynamics of the target by employing a target tracking module to enhance the spoofer performance. In this paper, a comprehensive Simulation study has been carried out to evaluate the performance of various target tracking filters. Simulation results reveals that the multiple model based filters giving good performance for the linear and maneuvering target compared to single model filters. Further, it is evident that the IMM filter gives superior performance in tracking a target and computationally efficient compared to AMM, GPB1, and GPB2 filters. The suggested IMM with KF and EKF configuration provides improved performance (in terms of computational complexity by 43.5%, in tracking position root mean square error (PRMSE) by 26%, and in spoofing PRMSE by 29.6%) compared to two KF filters GPB2 technique. Furthermore, the results also reveals the deployment of IMM based filters into the spoofer module substantially enriches the spoofing efficiency.