(Peer-Reviewed) Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu 谷华鑫 ¹, Jingming Zhang 张靖明 ², Xingwei Chen 陈星炜 ², Feihong Yu 余飞宏 ², Deyu Xu 许德宇 ¹, Shuaiqi Liu 刘帅旗 ³, Weihao Lin 林伟浩 ⁴, Xiaobing Shi 施晓兵 ², Zixing Huang 黄子星 ², Xiongji Yang 杨雄基 ², Qingchang Hu 胡清畅 ², Liyang Shao 邵理阳 ¹ ² ⁵
¹ School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China
中国 深圳 南方科技大学创新创业学院
² Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055 China
中国 深圳 南方科技大学电子与电气工程系
³ Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China
中国 郑州 哈尔滨工业大学郑州研究院
⁴ Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province; School of Mechanical Electrical and Information Engineering, Xiamen Institute of Technology, Xiamen 361021, China
中国 厦门 厦门工学院机械电气与信息工程学院 柔性制造装备集成福建省高校重点实验室
⁵ Peng Cheng Laboratory, Shenzhen 518055, China
中国 深圳 鹏城实验室
Opto-Electronic Advances, 2025-11-25
Abstract
With the application of Distributed Acoustic Sensors (DAS) across various infrastructures, it will play a pivotal role in shaping smart cities in the future. However, the current single-source detection and identification technology might struggle to meet the high precision needs in the intricate environmental conditions of mixed multi-source interference. We propose a new deep neural network-based multi-source signal separation method for DAS and accomplish the separation performance of this method under practical applications.
In addition, a new evaluation metric for the separation method is proposed in conjunction with the separation and identification of DAS mixed signals. For mixed signals with different source numbers, the recognizable rate of separated signals can reach 98.33% on average. This study provides a promising solution to the multi-source mixed interference problem faced by DAS in complex environments.
A review on optical torques: from engineered light fields to objects
Tao He, Jingyao Zhang, Din Ping Tsai, Junxiao Zhou, Haiyang Huang, Weicheng Yi, Zeyong Wei Yan Zu, Qinghua Song, Zhanshan Wang, Cheng-Wei Qiu, Yuzhi Shi, Xinbin Cheng
Opto-Electronic Science
2025-11-25
