Multi-affinity clustering analysis based graph learning for multichannel signal utilization

doi:10.23919/JSEE.2026.000002

Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (1): 171-183.doi: 10.23919/JSEE.2026.000002

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Multi-affinity clustering analysis based graph learning for multichannel signal utilization

Zhicheng WANG¹^,*(), Huiming JIANG²(), Hui XU¹(), Gao SUN¹(), Jialian SHENG¹()

¹Shanghai Radio Equipment Research Institute, Shanghai 201109, China
²School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2024-07-29 Accepted:2025-04-16 Online:2026-02-18 Published:2026-03-09
Contact: Zhicheng WANG E-mail:wangzhicheng@sjtu.edu.cn;hmjiang@usst.edu.cn;fionaxuhui@163.com;gao_sun@163.com;SJL_Jialian@163.com
About author:
WANG Zhicheng was born in 1982. He received his Ph.D. degree from the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. He is currently a researcher at Shanghai Radio Equiment Research Institute. He has published more than 30 papers and authorized more than 20 patents. His main research interests include radar signal processing, target detection and recognition. E-mail: wangzhicheng@sjtu.edu.cn

JIANG Huiming was born in 1988. She received her B.S. degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 2011, and Ph.D. degree in mechanical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2017. She is currently an associate professor with the School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai. Her current research interests include machinery condition monitoring, intelligent fault diagnostics and performance degradation assessment. E-mail: hmjiang@usst.edu.cn

XU Hui was born in 1982. She received her B.S. degree from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2005, and M.S. degree from Shanghai Jiao Tong University, Shanghai, China, in 2011. She is currently a senior engineer with Shanghai Radio Equiment Research Institute. Her research interest is the reliability and quality analysis of electronic devices. E-mail: fionaxuhui@163.com

SUN Gao was born in 1985. He received his B.S. degree from Northeastern University, Shenyang, China, in 2008, and Ph.D. degree from University of Chinese Academy of Sciences, Beijing, China, in 2013. He is currently a senior engineer with Shanghai Institute of Radio Equipment. His research interests include guidance and control, radar target recognition and other related fields. E-mail: gao_sun@163.com

SHENG Jialian was born in 1987. She received her B.S. and Ph.D. degrees from Xidian University, Xi’an, China, in 2010 and 2016. She is currently a senior engineer with Shanghai Institute of Radio Equipment. Her research interests include radar imaging, terahertz radar detection, multi-source information fusion, and distributed detection. E-mail: SJL_Jialian@163.com
Supported by:
This work was supported by Shanghai Aerospace Science and Technology Innovation Foundation (SAST2023-075).

Abstract

Abstract:

Multichannel signals have the characteristics of information diversity and information consistency. To better explore and utilize the affinity relationship within multichannel signals, a new graph learning technique based on low rank tensor approximation is proposed for multichannel monitoring signal processing and utilization. Firstly, the affinity relationship of multichannel signals can be acquired based on the clustering results of each channel signal. Wherein an affinity tensor is constructed to integrate the diverse and consistent information of the clustering information among multichannel signals. Secondly, a low-rank tensor optimization model is built and the joint affinity matrix is optimized with the assistance of the strong confidence affinity matrix. Through solving the optimization model, the fused affinity relationship graph of multichannel signals can be obtained. Finally, the multichannel fused clustering results can be acquired though the updated joint affinity relationship graph. The multichannel signal utilization examples in health state assessment with public datasets and microwave detection with actual echoes verify the advantages and effectiveness of the proposed method.

Key words: clustering analysis, multichannel signals, health state assessment, target recognition

Zhicheng WANG, Huiming JIANG, Hui XU, Gao SUN, Jialian SHENG. Multi-affinity clustering analysis based graph learning for multichannel signal utilization[J]. Journal of Systems Engineering and Electronics, 2026, 37(1): 171-183.

Figures/Tables 12

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Bearing	Degradation stages
Bearing	Normal state	Early fault	Fault development	Complete failure
1-1	1−67	68−78	79−123	−
1-2	1−38	39−63	64−157	157−161
1-3	1−62	63−108	109−157	158

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Multi-affinity clustering analysis based graph learning for multichannel signal utilization

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