Decoupled Representation Learning with Physical Constraints for Indoor Visible Light Target Perception

ZHANG Ying; MOU Haibo; LI Xujie; YANG Yufeng; WANG Kan; WANG Mingjun

doi:10.11999/JEIT260990

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ZHANG Ying, MOU Haibo, LI Xujie, YANG Yufeng, WANG Kan, WANG Mingjun. Decoupled Representation Learning with Physical Constraints for Indoor Visible Light Target Perception[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260990

Citation:

ZHANG Ying, MOU Haibo, LI Xujie, YANG Yufeng, WANG Kan, WANG Mingjun. Decoupled Representation Learning with Physical Constraints for Indoor Visible Light Target Perception[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260990

Citation:

ZHANG Ying, MOU Haibo, LI Xujie, YANG Yufeng, WANG Kan, WANG Mingjun. Decoupled Representation Learning with Physical Constraints for Indoor Visible Light Target Perception[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260990

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Decoupled Representation Learning with Physical Constraints for Indoor Visible Light Target Perception

doi: 10.11999/JEIT260990 cstr: 32379.14.JEIT260990

ZHANG Ying^{1, 2, 3
,},
MOU Haibo^1
,,
LI Xujie^{4
,
,},
YANG Yufeng^{1, 2, 3
,},
WANG Kan^5
,,
WANG Mingjun^{1, 2, 3
,}

1.
College of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, China
2.
Key Laboratory of Wireless Optical Communication and Networks, Xi’an 710048, China
3.
Key Laboratory for Military-Civilian Integration in Shaanxi Province, Xi’an 710048, China
4.
College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China
5.
College of Computer Science and Engineering, Xi'an University of Technology, Xi’an 710048, China

Funds: Funding National Natural Science Foundation of China (92052106, 61771385), Key Industrial Chain Innovation Team of Shaanxi Province (2024RS-CXTD-12), Key Research and Development Program of Shaanxi Province (2023-YBGY-039)

Received Date: 2026-07-20
Accepted Date: 2026-11-05
Rev Recd Date: 2026-11-05

Available Online: 2026-11-14

Abstract

Abstract

To address the problem that target multi-attributes (position, color, and shape) are highly coupled and difficult to separate in Channel State Information (CSI) over visible light reflection links for indoor monitoring scenarios, this paper proposes a physically-constrained disentangled representation learning method. On the existing communication link, orthogonal frequency division multiplexing signals are utilized to acquire multi-channel CSI. Based on optical transmission mechanisms including Lambertian reflection, geometric occlusion, and multipath delay, physically interpretable features for color, position, and shape are respectively constructed, achieving explicit disentanglement among attributes at the feature construction stage. Through feature-level fusion and principal component analysis for dimensionality reduction, combined with KMeans++ unsupervised clustering, multi-attribute target recognition is realized without requiring labeled training data. Meanwhile, the coupling relationships among CSI estimation error, bit error rate, and feature separability are analyzed from the communication link perspective. Experimental results show that in single-attribute recognition tasks, the average silhouette coefficients for position, color, and shape reach 0.83, 0.65, and 0.77, respectively. Multi-feature fusion leads to enhanced feature coupling and increased inter-class overlap, with silhouette coefficients decreasing as the number of fused attributes increases. This work validates the effectiveness of using physical prior constraints for multi-attribute disentangled representation learning, and provides a reference for low-hardware-overhead indoor target perception and communication-sensing co-design.

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References(23)

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