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Extraction Code If Needed: x16q.

Abstract:

In order to solve the problems of reduced accuracy and prolonging convergence time of through-the-wall radar (TWR) human motion due to wall attenuation, multipath effect, and system interference, we propose a multi-link auto-encoding neural network (TWR-MCAE) data augmentation method. Specifically, the TWR-MCAE algorithm is jointly constructed by a singular value decomposition based data preprocessing module, an improved coordinate attention module, a compressed sensing learnable iterative shrinkage threshold reconstruction algorithm (LISTA) module, and an adaptive weight module. The data preprocessing module achieves wall clutter, human motion features, and noise subspaces separation. The improved coordinate attention module achieves clutter and noise suppression. The LISTA module achieves human motion feature enhancement. The adaptive weight module learns the weights and fuses the three subspaces. The TWR-MCAE can suppress the low rank characteristics of wall clutter, and enhance the sparsity characteristics in human motion at the same time. It can be linked before the classification step to improve the feature extraction capability without adding other prior knowledge or recollecting more data. Experiments show that the proposed algorithm gets a better peak signal-to-noise ratio (PSNR), which increases the recognition accuracy and speeds up the training process of the back-end classifers.

Research & Code:

Weicheng Gao.

Mentors & Co-workers:

Xiaopeng Yang, Xiaodong Qu, Tian Lan, Junbo Gong.

Publication:

IEEE Transactions on Geoscience and Remote Sensing.

Project Includes:

1. Signal preprocessing / Data preprocessing codes and GUI softwares.

2. Human motion recognition codes and softwares based on several algorithms (AI algorithms included).

3. Full packaged software.

Environment:

Matlab R2021a+.

Papers Involving This Work:

[1] Weicheng Gao, Xiaopeng Yang, Xiaodong Qu, and Tian Lan, “TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition,” in IEEE Transactions on Geoscience and Remote Sensing, 2022.

[2] Weicheng Gao, Xiaopeng Yang, Tian Lan, Xiaodong Qu, and Junbo Gong, “Triple-Link Fusion Decision Method for Through-the-Wall Radar Human Motion Recognition,” in IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies (MAPE), 2022.