Doa estimation using a sparse linear model based on eigenvectors

Research paper by Libin Wang, Chen Cui, Pengfei Li

Indexed on: 08 Mar '12Published on: 08 Mar '12Published in: Journal of Electronics (China)


To reduce high computational cost of existing Direction-Of-Arrival (DOA) estimation techniques within a sparse representation framework, a novel method with low computational complexity is proposed. Firstly, a sparse linear model constructed from the eigenvectors of covariance matrix of array received signals is built. Then based on the FOCal Underdetermined System Solver (FOCUSS) algorithm, a sparse solution finding algorithm to solve the model is developed. Compared with other state-of-the-art methods using a sparse representation, our approach also can resolve closely and highly correlated sources without a priori knowledge of the number of sources. However, our method has lower computational complexity and performs better in low Signal-to-Noise Ratio (SNR). Lastly, the performance of the proposed method is illustrated by computer simulations.