A nonlinear adaptive filter for narrowband interference mitigation in spread spectrum systems

This paper presents a novel approach based on the estimation of the jammer instantaneous frequency for the excision of narrowband interferences in spread spectrum systems. Using a three-coefficient finite impulse response (FIR) model for the interference, an augmented state-space representation of the received signal (spread spectrum signal+noise+interference signal) is developed. Based on the state-space representation developed, a Kalman-type nonlinear adaptive filter, namely, augmented-state approximate-conditional-mean (ASACM) filter, is formulated to estimate the unknown jammer instantaneous frequency from the received signal. The receiver performance and the problem of stability of the proposed filter are addressed. The improvement in the performance achieved with the proposed nonlinear Kalman-type filter is quantified in comparison with the linear one based on three-coefficient FIR excision filter, and an adaptive nonlinear prediction filter. The efficacy of the proposed filter is corroborated with simulation examples for interference suppression in spread spectrum systems. Simulation results show that the proposed filter is effective in suppressing powerful narrowband interference in spread spectrum systems with good receiver output SNRs.