DOA estimation for nonuniform linear arrays using Root-MUSIC with Sparse recovery method

Abstract

Direction-of-arrival (DOA) estimation with nonuniform linear arrays (NLA) using the sparse data model is considered. Different with the usually used sparse data model, we introduce a linear interpolation operator which can transform the data of the NLA to the data of a virtual uniform linear array (VULA). We first reduce the dimension of the model using the singular value decomposition technique, next recover the solution of the reduced MMV using a compressed sensing (CS) algorithm, then get the data of the VULA using the recovery result and the linear interpolation operator, and lastly use root-MUSIC to estimating DOA. The method is called CS-RMUSIC. The experiments illustrate the good efficiency of the CS-RMUSIC algorithm. © Springer-Verlag Berlin Heidelberg 2013.