SUPER-RESOLUTION DOA ESTIMATION VIA CONTINUOUS GROUP SPARSITY IN THE COVARIANCE DOMAIN

Estimation of directions-of-arrival (DoA) in the spatial covariance model is studied. Unlike the compressed sensing methods which discretize the search domain into possible directions on a grid, the theory of super resolution is applied to estimate DoAs in the continuous domain. We reformulate the spatial spectral covariance model into a Multiple Measurement Vector (MMV)-like model, and propose a block total variation norm minimization approach, which is the analog of Group Lasso in the super-resolution framework and that promotes the group-sparsity. The DoAs can be estimated by solving its dual problem via semidefinite programming. This gridless recovery approach is verified by simulation results for both uncorrelated and correlated source signals.