Direction-of-arrival estimation using laplace prior based on bayes compressive sensing

Based on the multi-task Bayes Compressive Sensing (BCS), a Direction-Of-Arrival (DOA) estimation strategy using Laplace prior is proposed. The DOA estimation is formulated as the reconstruction of sparse signal constrained by the Laplace prior through the BCS framework. The outputs of array sensors are directly employed as the observations, and the exploiting of Laplace prior leads to better spare property than the conventional BCS method. The proposed method needs not the prior information of the number of sources, needs not the eigenvalue decomposition and can work in the coherent signal scenario. The numerical experiments show that the proposed method has the better performance than the conventional BCS and MUSIC algorithm on the DOA estimation.