High-resolution reconstruction of multi-highlight bearings of underwater target with L1 norm sparsity measure

Abstract

Aiming at the problem that multi-highlight bearing resolution of underwater target, a high-resolution reconstruction of multi-highlight bearings resolution of underwater target with L1 norm sparsity measure is proposed. The L1 norm singular value decomposition (L1-SVD) method proposed by Malioutov et al. is used to reconstruct the multi-highlight bearings structure, which breaks through the Rayleigh limit of bearings resolution of conventional beamforming and solves the problem that MUSIC method cannot distinguish coherent sources. At the same time, two off-grid processing methods, the first-order Taylor expansion processing and the total variation norm (TVN) constrained meshless processing, are used to solve the off-grid problem caused by the array manifold discretization, which improve the performance of the methods. In this paper, a sparse representation model of array signal with L1 norm as the sparsity measure is established, and a high-resolution reconstruction method for multi-highlight bearings of underwater target is given. The computer simulation results show that the proposed method has high-resolution and robust performance. This method can resolve multi-highlight coherent signal sources, and the off-grid processing algorithms improve the accuracy of bearing estimation.