A Low Complexity Direction of Arrival Estimation Algorithm by Reinvestigating the Sparse Structure of Uniform Linear Arrays

In this paper, we present a new computationally efficient method for direction-of-arrival (DOA) estimation in uniform linear arrays (ULAs). A sparse uniform linear array (SULA) structure is firstly extracted from the conventional ULA to exploit its advantage in high resolution. By performing the multiple signal classification (MUSIC), the noise subspace of the SULA is simultaneously orthogonal to the steering vectors corresponding to the true DOAs and several virtual DOAs, where all the true and virtual DOAs for each source are uniformly distributed in the sine domain. Then we divide the total angular field into several small sectors and search over an arbitrary sector. Finally, the true DOAs can be distinguished by the noise subspace of the original ULA. Since the proposed method involves a limited spectral search and a reduced-dimension noise subspace, hence it is quite computationally efficient. Simulation results are provided to verify the effectiveness of the proposed method in terms of computational complexity, estimation accuracy, and resolution performance.