A bidirectional iterative equalization algorithm for underwater acoustic communication based on sparse adaptive filtering

This paper proposes a parameter-adaptive adjustment sparse recursive least-square (PAA-S-RLS) adaptive algorithm for the sparse underwater acoustic channel. Firstly, the proposed algorithm can achieve adaptive matching of sparse channels with different structures by jointly optimizing the forgetting factor and sparse penalty parameters, which enables the equalization algorithm converge to the steady-state faster. Secondly, a robust sparse adaptive RLS algorithm combined with bidirectional iterative Turbo decision feedback equalizer is designed to address the issue of error propagation caused by the conventional equalization algorithms and conventional Turbo decision feedback equalizer, which can improve the convergence rate and the performance of adaptive equalizers in sparse underwater acoustic channels. Simulation and experimental results show that the bidirectional iterative equalization algorithm based on adaptive adjustment sparse RLS has faster convergence rate, lower bit error rate, and better equalization performance in the sparse underwater acoustic channel.