DOA Estimation of Coherently Distributed Sources Based on Block-Sparse Constraint

摘要

In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.