Angle Separation Learning for Coherent DOA Estimation With Deep Sparse Prior
Houhong Xiang, Baixiao Chen, Minglei Yang, Saiqin Xu
Abstract
In this letter, we propose two angle separation learning schemes (ASLs) to address the coherent DOA estimation problem. We first show that the columns of the array covariance matrix can be formulated as under-sampled linear measurements of the spatial spectrum. Secondly, the computational load of coherent DOA estimation contains two-dimensional searching process, which is reduced through introducing angle separation. Correspondingly, two classification learning models integrating the angle separation are proposed to solve the problem of coherent DOA estimation. Compared to classic sparsity-inducing methods with complex computational interactions, the proposed ASLs can efficiently obtain DOA estimates in near real time. Moreover, the proposed ASLs improve the DOA estimation performance compared to existing deep-learning based methods. Numerical experiments prove the effectiveness and superiority of the presented ASLs. Simulation results show that the new techniques have a better performance in term of estimation errors and generalization ability than the classic physics-driven and state-of-the-art deep-learning based DOA estimation methods, especially in demanding scenarios with low SNR and limited snapshots.