A Multiple Measurement Vector Approach for DOA Estimation

Background: Compressed Sensing (CS) is an emerging signal processing technique for signal acquisition and reconstruction which recently finds applications in array processing. Direction of Arrival (DOA) is a well-known problem in array signal processing which can be treated with methods based on compressed sensing. Methods: In this work, a novel algorithm has been developed based on sparse multiple measurement vector model (MMV) to estimate DOAs of far-field and narrowband sources in linear arrays scenarios. The proposed algorithm exploits singular value decomposition denoising to enhance the reconstruction process. Conclusion: Several simulations have been carried out to show the superior performance of proposed method in comparison to simultaneous orthogonal matching pursuit (S-OMP), Ι2,1 minimization and root-MUISC in both uniform linear array (ULA) and nonuniform linear array (NLA) scenarios.