Redundancy-Reduced Sparsity-Based Adaptive Beamforming for Polarization-Sensitive Arrays

A sparse reconstruction approach for adaptive beamforming (ABF) with polarization-sensitive arrays (PSA) is introduced in this letter. It first represents the spatial sparsity of incoming signals as the row sparsity of a power-scaled polarization matrix, which arises from the matrization of the redundancy-reduced covariance vector. Then the row sparsity issue is relaxed to an l(2,1) norm minimization form and solved in a gridless way via a compact formulation, where a dimension reduction method is introduced to reduce the problem size. Compared to existing techniques, the proposed method processes the polarization information holistically and derives each signal parameter in the continuous domain. Simulation results substantiate the advantages of the proposed method over competing methods.