Direction-of-Arrival Estimation Using a Mixed $\ell _{2,0}$ Norm Approximation

摘要: A set of vectors is called jointly sparse when its elements share a common sparsity pattern. We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as recovering joint-sparse representation. consider both narrowband and broadband scenarios. propose to minimize mixed l2,0 norm approximation deal with recovery problem. Our algorithm resolve closely spaced highly correlated sources using small number noisy snapshots. Furthermore, need not known priori. In addition, our handle more than other state-of-the-art algorithms. For DOA problem, allows relaxing half-wavelength spacing restriction, which leads significant improvement in resolution limit.