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Attention Mechanism and Sparse Low-Rank Modeling Based Neural Network for Data Augmentation of Through-the-Wall Radar Human Motion Recognition.pdf (7.4 MB)
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Attention Mechanism and Sparse Low-Rank Modeling Based Neural Network for Data Augmentation of Through-the-Wall Radar Human Motion Recognition

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posted on 05.04.2022, 13:52 authored by Weicheng GaoWeicheng Gao, Tian Lan, Xiaopeng Yang, Xiaodong Qu
In order to better address the non-adaptability of image recognition algorithms on through-the-wall radar human motion data and the low signal-to-noise ratio (SNR) caused by microwave penetration through walls, an attention mechanism and sparse low-rank modeling based neural network is proposed in this paper. The method combines information from physical background of moving target and vision characteristics of imaging to achieve effective suppression of wall clutter and noise as well as enhancement of motion feature. The super resolution of human motion feature is achieved by sparsely encoded learning iterative shrinkage thresholding (LISTA) module. The location of human behind the wall is obtained by an improved coordinate attention mechanism, which automatically calibrates the regions associated with human motion characteristics. Chunked output super-resolution information after attention mechanism and LISTA module is finally weighted and aggregated by the parallel adaptive weight module. Experiments demonstrate that a better SNR is achieved for image feature extraction, while the accuracy and the convergence speed of the existing classification algorithms is effectively raised.

Funding

National Natural Science Foundation of China, 61860206012

National Natural Science Foundation of China, 62101042

National Natural Science Foundation of China, 61901441

Natural Science Foundation of Chongqing, cstc2021jcyj-msxmX0339

Natural Science Foundation of Chongqing, cstc2020jcyj-msxmX0768

History

Email Address of Submitting Author

JoeyBG@126.com

ORCID of Submitting Author

https://orcid.org/0000-0002-2719-3645

Submitting Author's Institution

Beijing Institute of Technology

Submitting Author's Country

China