Novel SREKF-based recurrent neural predictor for narrowband/FM interference rejection in GPS

The GPS provides accurate positioning and timing information that is useful in various applications. A new adaptive neural predictor for GPS jamming suppression applications is proposed. The effective and computationally efficient square-root extended Kalman filter (SREKF) algorithm is adopted to adjust the synaptic weights in the nonlinear recurrent architecture and thereby estimate the stationary and non-stationary narrowband/FM waveforms. Cholesky factorization is employed in Riccati recursion to improve numerical stability because of the propagation of round-off errors in conventional KF equations. The main characteristics of the proposed SREKF-based canceller are their rapid convergence and favorable tracking performance. Simulation results reveal that its SNR improvement factor exceeds the factors of conventional LMS, RLS, ENA and TLFN filters in single-tone CWI, multi-tone CWI, pulse CWI and FM jamming environments, respectively.