Deep learning techniques for direction of arrival estimation
Zhangmeng Liu, Liuli Wu, Philip S. Yu · Institution of Engineering and Technology eBooks · 2022
This chapter presents an overview of how deep learning (DL) techniques can be exploited to solve the problem of direction-of-arrival (DOA) estimation, and also provides a solution to this problem using a feasible and efficient hierarchical deep neural network (DNN). The chapter begins with a general introduction to existing DOA estimation and DL techniques in Section 1.1, then formulates the DOA estimation problem mathematically under different conditions in Section 1.2, and summarizes the most common DL frameworks that have been applied to DOA estimation in Section 1.3, including mainly their neural network configurations and the most widely used strategies for algorithm implementation. Section 1.4 presents a hierarchical DNN framework to solve the DOA estimation problem, and carries out simulations to demonstrate its predominance in generalization over previous machine learning (ML)-based methods, and in array-imperfection adaptation over conventional parametric methods. Finally, this chapter ends in Section 1.5 by providing some clues on several future research trends of this area.