Frequency Domain Separation of DAS Multi-Source Aliased Signals

Huaxin Gu1; Shuaiqi Liu2; Feihong Yu2; Deyu Xu1; Xingwei Chen2; Liyang Shao2; Yu Wu3; Haifeng Zhang4

10.1109/ACP/POEM59049.2023.10369858

2023

979-8-3503-1262-1

2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM)

1-4

4-7 Nov. 2023

Wuhan, China

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events.

DAS convolutional aliasing model FastIVA time-frequency entropy

第一

EI

20240515453655

IEEE

1.College of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen, China
2.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
3.School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu, China
4.Advanced Measurement and Control and Equipment R&D Center, Research Institute of Tsinghua University, Chengdu, China

Huaxin Gu,Shuaiqi Liu,Feihong Yu,et al. Frequency Domain Separation of DAS Multi-Source Aliased Signals[C],2023:1-4.