ML-based beamforming for follower jamming rejection in slow FH/MFSK systems

Abstract

Follower partial band jamming is recognized as an efficient strategy to degrade the performance of frequency hopping (FH) systems with M-ary frequency shift keying (MFSK) modulation. In this paper, a maximum likelihood based beamforming (MLBB) algorithm that uses a two-element array is proposed to reject a follower jamming signal and carry out symbol detection in slow FH/MFSK systems over quasi-static flat fading channels. Deploying a received signal model that takes care of flat fading, the proposed scheme first uses a ML-based approach to obtain a ML estimate of the ratio of jamming fading gains. Based on this ML estimate, a simple beamforming structure is employed to place a null toward the follower jamming source and the symbol detection is then performed by the ML technique. Analytical and simulated results show the effectiveness of the proposed scheme in combating follower jamming over a wide range of signal and jammer power ratios.