A Beamspace-Based Orthogonal Matching Pursuit DOA Estimation Algorithm
Zhongchuan Sun, Shuo Wang, Kexin Jia, Shuaishuai Pan · 2024
In order to solve the problem that the traditional Direction of Arrival (DOA) estimation algorithm is seriously affected by the multipath effect and has high computational complexity when tracking at low elevation angles, this study innovatively proposes an Orthogonal Matching Pursuit (OMP) algorithm based on beam space. This algorithm first performs multibeam coverage of the target, maps the received signal from the array element domain to the beam domain, and then utilizes the theory of compressed perception to compressively sample the airspace signals in the beam space, so as to efficiently extract the data information. Compared with the traditional DOA estimation algorithms, such as the Capon Beamforming (CAPON) algorithm and the Multiple Signal Classification (MUSIC) algorithm, the algorithm proposed in this paper has stronger resistance to multipath interference. Compared with the array-element level OMP algorithm and the L1-Norm Singular Value Decomposition (L1-SVD) algorithm, the algorithm computation is significantly reduced and the real-time tracking capability of the system is improved.