Title: Underwater acoustic multipath sparse channel estimation via gridless relevance vector machine method
Author: LIN Geping; MA Xiaochuan; YAN Shefeng; JIANG Li
Affiliation: Institute of Acoustics, Chinese Academy of Sciences
Abstract: In the scenario of underwater acoustic sparse channel estimation with training sequences, grid points in the measuring matrix are caused by discretizing procedure. Estimated accuracy might not be guaranteed with the state-of-the-art methods when multipath delays don't exactly locate on the grid points. In this paper, we construct a gridless measuring matrix for sparse channel estimation which contains an off-grid adjusting factor. The Relevance Vector Machine (RVM) algorithm is employed to estimate this factor. The numerical experiments for two different underwater channels are performed to testify the newly proposed method. The results demonstrate that this method outperforms conventional ones in terms of estimating error and bit error rate, especially when the grid gets coarser.