Sparsity-Based Processing to Enhance the Reverberation Suppression for FDA-MIMO Sonars

Multiple-input-multiple-output (MIMO) system with frequency diverse array (FDA) offers interesting perspectives for target detection and estimation in several fields, such as sonar. In this context, reverberation in shallow water environments is highly heterogeneous and, as a consequence, impairs the performance of methods that assume statistical homogeneity. To deal with this drawback, we propose a weighted sparse algorithm to enhance the performance of FDA-MIMO sonars in the context of space–time adaptive processing (STAP). Specifically, we exploit the structure of the FDA-MIMO sonar signal model to construct an optimization problem that allows for high-resolution estimation of the angle–Doppler spectrum. In order to solve such a problem, we assume that the weight vector is sparse and conceive a sparse STAP method for the related estimation. In addition, we also estimate a regularization parameter that controls the sparsity level of data by using the constant-false-alarm-rate (CFAR) detector and the maximum likelihood (ML) estimator. As a consequence, the negative effect related to the amount of training data or accurate prior knowledge of the reverberation statistics is no longer present. Both target parameter estimation accuracy and reverberation suppression performance are superior over the conventional sparse-STAP and sparse direct data domain methods. Simulation results show the effectiveness of the proposed method.