An angle super-resolution approach of sparse acoustic vector array based on compressed sensing

In order to improve the Direction of Arrival (DOA) estimation performance of Acoustic Vector Sensor (AVS), an angle super-resolution approach based on Compressed Sensing (CS) is proposed. First, the CS theory is introduced into this field by exploiting the sparsity of target scene. Then, a joint sparse model along with Singular Value Decomposition (SVD) is utilized to accumulate signal power and reduce dimension. Finally, RMFOCUSS is utilized as a performance guaranteed recovery algorithms with available practicability for estimating targets angle. Compared with traditional methods, the proposed approach achieves better angle resolution in coping with both incoherent and coherent signals, also mitigates the high sidelobe problem caused by sparse array effectively.