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

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 pe...