Broadband horizontal wavenumber estimation using block sparse Bayesian learning

Abstract

The horizontal wavenumbers are estimated by block sparse Bayesian learning for broadband signals received by a vertical line array in ocean waveguides. The dictionary matrix consists of multi-frequency modal depth functions derived from WKB approximation, which are associated with horizontal wavenumbers. The dispersion relation for multi-frequency horizontal wavenumbers is also taken into account to generate the dictionary. With the constraint of block sparsity, the sparse Bayesian learning approach is shown to extract the horizontal wavenumbers and corresponding modal depth functions with a high precision, while the prior of sea bottom information, moving source, and source locations is not needed. The performance is demonstrated by simulations and experimental data.