YOLO-DoA: A New Data-Driven Method of DoA Estimation Based on YOLO Neural Network Framework

• Published in: IEEE sensors letters Vol. 7; no. 2; pp. 1 - 4
• Main Authors: Fan, Rong, Si, Chengke, Yi, Wenchuan, Wan, Qun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array signal processing; convolutional neural network (CNN); Convolutional neural networks; deep learning; direction -of-arrival (DoA) estimation; Direction of arrival; Direction-of-arrival estimation; Estimation; Feature extraction; Neck; Neural networks; Object recognition; Signal processing; Target detection; Tensors; Uninterruptible power systems
• ISSN: 2475-1472; 2475-1472
• DOI: 10.1109/LSENS.2023.3241080

Direction-of-arrival (DoA) estimation is one of the most promising technologies in array signal processing. Existing data-driven methods for DoA estimation are usually implemented by classification networks, which suffer from insufficient utilization about features of sources and require spectral peak-search stage. In this paper, we reframe DoA estimation as a target detection problem and propose a novel DoA estimation approach on the basis of YOLOv3 framework, namely YOLO-DoA. DoAs of sources with confidence scores are directly predicted from the spectrum proxy with YOLO-DoA and an end-to-end estimation is realized. By combining squeeze-and-excitation operation, cross stage partial connections, and an improved loss function for bounding box regression, the performance of YOLO-DoA is enhanced. Simulation results demonstrate that the proposed approach outperforms several state-of-the-art methods in terms of network size, computational cost, prediction time and accuracy of DoA estimation.