Electric chain hoist

Abstract

Channel estimation is a challenging task for underwater acoustic (UWA) communications. In this paper, we investigate the channel estimation for OFDM based UWA communications by utilizing the channel characteristics as auxiliary information. Firstly, the channel estimation problem is formulated as a sparse signal recovery problem using block-wise single measurement vector (SMV) model and multiple measurement vector (MMV) model. Then we develop a novel channel estimators under the temporal sparse Bayesian learning (TSBL) framework by jointly exploiting the temporal correlation and sparsity based on the block-wise SMV model. For the MMV model, a variational Bayesian inference based TSBL (VBI-TSBL) method is introduced that considers the inter-block correlations and exploits the row-sparsity by a linear temporal correlation model. The two Bayesian inference methods realize the posterior and parameter estimation of channel via expectation maximization (EM) and variational EM algorithm, respectively. Simulation and sea-trial experiments show the proposed channel estimators outperform the benchmarks in terms of bit error rate, mean square error of channel estimation and the robustness.