The UKF-based RNN predictor for GPS narrowband interference suppression

Abstract

The global positioning system (GPS) provides accurate positioning and timing information useful in many applications. Although DS-SS inherently can cope with low power narrowband and wideband obstacles by its near 43-dB processing gain, it cannot cope with high power obstacles. The approaches of system performances that can be further enhanced by preprocessing to reject the intentional or unintentional jamming will be investigated in this paper. A recurrent neural network (RNN) predictor for the GPS anti-jamming applications will be proposed. The adaptive RNN predictor is utilized to accurately predict the narrowband waveform based on an unscented Kalman filter (UKF)-based algorithm. The UKF is adopted to achieve better performance in terms of convergence rate and quality of solution. Two types of narrowband interference, i.e. continuous wave interference (CWI) and auto regressive interference (ARI), are considered to emulate realistic circumstances. The signal-to-noise ratio (SNR) is varied from -20 to -5 dB. The anti-jamming performances are evaluated via extensive simulation by computing mean squared prediction error (MSPE) and signal-to-noise ratio (SNR) improvements.