Triple-Link Fusion Decision Method for Through-the-Wall Radar Human Motion Recognition

Weicheng Gao, Xiaopeng Yang, T. Lan, X. Qu, Junbo Gong
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Abstract

To better solve the accuracy degradation of human motion recognition due to low signal-to-clutter-plus-noise ratio (SCNR) and low resolution of through-the-wall radar (TWR) imaging, a triple-link fusion decision human motion recognition method for through-the-wall radar is proposed in this paper. This method combines the physical information, visual local information and visual global information in imaging. Specifically, the idea of complementarity of three weak models, including empirical modal decomposition (EMD) algorithm based on statistic signal detection, visual gradient-level based kernel method and visual regionalized macro-level based shuffle attention improved residual neural network (SA-Inception-ResNet) algorithm are introduced in the method, and the Dempster-Shafer (D-S) synthesis theory is used to achieve decision level fusion recognition. The final results are inferred by an adaptive boosting method on the trained weak models and the fused strong model. Experiments are carried out to demonstrate that the accuracy of the algorithm exceeds 99.54%, while the prediction performance and robustness are significantly improved compared with previous methods.
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穿墙雷达人体运动识别的三链路融合决策方法
为了更好地解决穿墙雷达成像低信噪比(SCNR)和低分辨率对人体运动识别精度的影响,本文提出了一种三链路融合决策穿墙雷达人体运动识别方法。该方法结合了成像中的物理信息、视觉局部信息和视觉全局信息。具体而言,该方法引入了基于统计信号检测的经验模态分解(EMD)算法、基于视觉梯度级的核方法和基于视觉区域宏观级的洗牌注意改进残差神经网络(sa - incept - resnet)算法三种弱模型的互补思想,并利用Dempster-Shafer (D-S)综合理论实现决策级融合识别。通过对训练好的弱模型和融合的强模型进行自适应增强,推导出最终结果。实验结果表明,该算法的准确率超过99.54%,预测性能和鲁棒性均较以往方法有显著提高。
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