Novel methods of DOA estimation based on compressed sensing

Making use of the sparsity of targets, three novel direction of arrival (DOA) models based on compressed sensing (CS) theory are proposed. Covariance matrix CS, interpolated array CS and beam space CS carry out compressive sampling on covariance matrix, interpolated array and beam space, respectively. High-resolution DOA estimations are obtained through reconstruction of sparse signal by convex optimization problem resolution. The proposed methods are conceptually different from subspace-based methods and provide high resolution using a uniform linear array without restricting requirements on the spatial and temporal stationary and correlation properties of the sources and the noise. Results of both simulated data and measured data show that these methods are superior to conventional DOA methods in angular estimation performance.