A novel beamspace algorithm for direction of arrival based on compressive sensing

In this paper, the narrowband direction-of-arrival (DOA) estimation problem is studied in compressive sensing (CS) perspective. A novel approach to DOA estimation in beamspace for a uniform linear array is proposed. A compression perception model is established making use of the spatial sparsity. Then compressive received data is mapped from element-space to beamspace. The MIP statistics property is analyzed to ensure the proposed method possessing better reconstruction property. Simulation results demonstrate that, compared to the Multiple Signal Classification (MUSIC) algorithms and beamspace MUSIC algorithm, the novel method possesses higher resolution and better estimate performance; compared to element-space Dantzig Selector reconstruction algorithm, the proposed method owns higher distinguish probability and lightens computational burden in the condition of low SNR scenario.