A joint estimation algorithm for single-input multiple-output underwater acoustic communications

Abstract

In single-input multiple-output (SIMO) underwater acoustic (UWA) communications, the receiver based on passive time reversal (PTR) combined with decision feedback equalizer (DFE) is widely used but has a limited performance. A multi-channel joint estimation algorithm based on sparse Bayesian learning (MJSBL) is proposed in this paper to exploit the diverse gain from multi-channels, where reasonable prior distribution functions are selected for parameters in the probabilistic model. Afterwards, the algorithm is derived by the mean-field variational inference (VI), iteratively updating the estimation of symbols, channels and noise variation. As a result, the maximum likelihood estimation of the dictionary matrix, as well as the maximum posterior estimation of the channel vectors and noise variance, can be approximated. Simulation and experimental results verify that compared to typical single-channel and multi-channel algorithms, the systems always have lower bit error rates (BERs) and symbol error rates (SERs) with the MJSBL algorithm for different communications distances and symbol block lengths.