Deep Learning-Based DOA Estimation

被引:16
作者
Zheng, Shilian [1 ]
Yang, Zhuang [2 ]
Shen, Weiguo [1 ]
Zhang, Luxin [1 ]
Zhu, Jiawei [1 ]
Zhao, Zhijin [2 ]
Yang, Xiaoniu [1 ]
机构
[1] Natl Key Lab Electromagnet Space Secur, Innovat Studio Academician Yang, Jiaxing 314033, Peoples R China
[2] Hangzhou Dianzi Univ, Telecommun Sch, Hangzhou 310018, Peoples R China
基金
中国国家自然科学基金;
关键词
Direction-of-arrival estimation; Estimation; Covariance matrices; Adaptation models; Training; Convolutional neural networks; Antenna arrays; Direction-of-arrival (DOA) estimation; deep learning; residual neural network; classification; regression; OF-ARRIVAL ESTIMATION; SPARSE; ESPRIT;
D O I
10.1109/TCCN.2024.3360527
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Direction-of-arrival (DOA) estimation is a vital research topic in array signal processing, with extensive applications in many fields. In recent years, deep learning has been applied to DOA estimation to improve the performance. However, most existing deep learning-based DOA estimation methods extract DOA information from the covariance matrix (CM) input. In this paper, we introduce a novel deep learning-based DOA estimation scheme that utilizes the raw in-phase (I) and quadrature (Q) components of the signal as the input. We formulate the problem as single-label classification and multi-label classification based on the number of signal sources. We design a convolutional neural network to solve the problems and to adapt to different number of snapshots. We also propose a deep learning regression-based method to overcome the limitations of classify-based methods in dealing with off-grid angles. We conduct extensive experiments with simulations and over-the-air collected signals to analyze the performance of the proposed method in various scenarios including different SNRs, additive generalized Gaussian noise (AGGN) and extreme multi-source DOA estimation. Results demonstrate that our proposed method outperforms the existing deep learning-based DOA estimation methods.
引用
收藏
页码:819 / 835
页数:17
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