提出新方法提升雷达人体动作识别跨环境泛化能力
Generalization Ability Analysis of Through-the-Wall Radar Human Activity Recognition
- 用线性神经网络分析雷达动作识别的泛化误差
- 通过微多普勒角特征降维，显著降低泛化误差
- 适合研究雷达感知与模型泛化性能的科研人员
基于低频超宽带（UWB）信号的穿墙雷达（TWR）人体动作识别（HAR）技术可检测并分析室内人体运动。然而，现有端到端识别模型高度依赖训练数据分布，难以在不同测试环境中实现良好泛化。本文针对TWR HAR的泛化能力进行分析：首先提出一种端到端线性神经网络方法及其泛化误差界；其次引入微多普勒角表示法，并验证降维前后的泛化误差变化。数值仿真与实验结果表明，特征维度降低能有效提升模型在不同室内测试者间的泛化性能。
原文摘要 · Abstract (English)
Through-the-Wall radar (TWR) human activity recognition (HAR) is a technology that uses low-frequency ultra-wideband (UWB) signal to detect and analyze indoor human motion. However, the high dependence of existing end-to-end recognition models on the distribution of TWR training data makes it difficult to achieve good generalization across different indoor testers. In this regard, the generalization ability of TWR HAR is analyzed in this paper. In detail, an end-to-end linear neural network method for TWR HAR and its generalization error bound are first discussed. Second, a micro-Doppler corner representation method and the change of the generalization error before and after dimension reduction are presented. The appropriateness of the theoretical generalization errors is proved through numerical simulations and experiments. The results demonstrate that feature dimension reduction is effective in allowing recognition models to generalize across different indoor testers.
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