Single channel time-varying amplitude LFM interference blind separation based on improved particle filter

Two methods are proposed for single channel blind separation problem of communication signal and interference (include Linear frequency modulation interference and Single-tone interference) based on genetic algorithm and particle swarm optimized particle filtering. Proposed algorithms aim to obtain the maximum a posterior (MAP) estimate of communication code and the unknown parameters using particle filtering by establishing the state space model for the interfered signal, Specially, in order to overcome the sample impoverishment problem, genetic algorithm and particle swarm optimized is introduced to the re-sampling process in particle filtering. In such a way, the number of needed particles is reduced and the variety of particles is retained during the sequential estimation process, moreover, the proposed algorithm has superior performance under time-varying amplitude LFM interference and Single-tone interference. Simulation results show that particle swarm optimized resample method is effective to separate communication signal and interference when the ISR is less than 15dB and SNR is more than 14dB.