A Sparse Bayesian Learning Method for Moving Target Detection and Reconstruction

Moving target detection in a disturbing environment has been significantly active and challenging for underwater acoustic array signal processing. One of the most effective approaches is based on low-rank matrix estimation, which divides the reconstructed imaging frames into low-rank and sparse matrix components, with the low-rank structure representing the interference and the sparse component corresponding to the moving target. However, the array model has not been well explored, taking the reconstruction and moving target separation as independent steps. In this article, the sparse Bayesian learning (SBL) approach is employed to recover the stable interference and the moving target simultaneously and separately, retaining the high reconstruction quality pertaining to SBL. Specifically, in each frame, a shared prior distribution is assigned to the stable interference, while the time-varying component (the moving target) is modeled with a unique prior distribution. The hierarchical model is solved by the variational Bayesian inference. The superior performance of the proposed algorithm is validated by simulation and experimental data in an underwater acoustic test tank and Songhua Lake in China, compared with state-of-the-art low-rank matrix estimation algorithms.