Underwater Acoustic Channel Estimation Based on Sparse Bayesian Learning Algorithm

The channel estimation algorithm based on sparse Bayesian learning proposed in recent years shows better performance than the traditional by effectively reducing convergence error process. However, expectation maximization (EM-SBL) is difficult to meet practical applications with low complexity and power consumption. In order guarantee long-term stable communication of underwater devices, this paper proposes fast Fast Marginal Likelihood Maximization (FM-SBL) estimate acoustic channels consumption high performance. Simulation sea trial results show output BER after FM-SBL similar that EM-SBL, LS, MP OMP, it has good robustness slow time-varying channels. terms running speed, 16.7% EM-SBL algorithm, which greatly reduces time.