DOA Estimation Using Beamspace-Based Deep Neural Network
Yuanjie Ji, Cai Wen, Yan Huang, Jinye Peng · 2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT) · 2022
Direction-of-arrival (DOA) estimation is to analyze the received sensor array data to determine the direction of the signal, so as to better perform beamforming or determine the target position. In the field of DOA estimation, traditional methods are parametric, while machine learning methods rely heavily on the consistency of test and training samples, and when there is array imperfection, their performance is extremely degraded. Therefore, this paper uses deep learning to solve the DOA estimation problem, and makes DOA estimation in the virtual array beam space to adapt to the array imperfections, and a training method robust to various array imperfections is proposed, that is, a spherical model is used to simulate the distribution of different array imperfections, and training sets are generated in the distribution. Simulation results show that the proposed method has good adaptability to array imperfections.