A novel off-grid DOA estimation via weighted subspace fitting

Abstract

In this paper, a novel off-grid direction-of-arrival (DOA) estimation algorithm involving sparse recovery is proposed based on weighted subspace fitting, in which multiple snapshots are used and effects of off-grid DOA are taken into account. The DOA estimation problem is formulated as a binary cost function, then an iterative sparse recovery algorithm alternating resolved the unknown variables with weighted linorm approximation method is developed to estimate DOA accurately. The proposed algorithm obtains improved accuracy compared with the existing methods. Simulation results demonstrate that the proposed algorithm can estimate the DOA with high accuracy for correlated signals while maintaining a relatively low computational cost.