提出雷达人体动作识别泛化理论框架，解释性能下降原因。
Generalization Theory for Through-the-Wall Radar Human Activity Recognition
- 构建从源到目标的统一学习框架，涵盖人体运动、雷达回波等模型。
- 推导出目标域泛化上界，揭示跨人、跨视角、跨墙的误差来源。
- 验证物理低维表示、多源训练等方法可有效提升泛化能力。
穿墙雷达（TWR）人体活动识别（HAR）在非视距室内感知、安全监控和应急救援中具有重要意义。然而，由人员差异、观测视角变化和墙体条件变化引起的结构化分布偏移严重降低识别泛化性能，而目标域误差的根源尚缺乏严谨的理论解释。本文提出一种TWR HAR的泛化分析框架。首先，在统一的源到目标学习范式下建立室内人体运动模型、雷达回波生成模型、雷达图像形成模型、特征表示模型及有界权重神经网络模型。随后定义源风险、目标风险、经验风险及可接受的物理域描述符，并推导出统一的目标域泛化上界。进一步将结构化偏移项分解为跨人、跨视角和跨墙三部分，分析物理低维表示、多源训练和参数空间覆盖对上界收紧的效果。仿真与实测实验共同支持理论分析，并展示其应用价值。
原文摘要 · Abstract (English)
Through-the-wall radar (TWR) human activity recognition (HAR) is important for non-line-of-sight indoor sensing, security monitoring, and emergency rescue. However, structured distribution shifts caused by person variation, observation-view variation, and wall-condition variation severely degrade recognition generalization, while the origin of the target-domain error still lacks a rigorous theoretical explanation. To address this issue, a generalization-analysis framework for TWR HAR is proposed in this paper. First, models for indoor human kinematics, TWR echo generation, radar image formation, feature representation, and bounded-weight neural networks are established within a unified source-to-target learning formulation. Then, the source risk, target risk, empirical risk, and admissible physical domain descriptor are defined, and a unified target-domain generalization bound is derived. Next, the structured shift term is decomposed into cross-person, cross-view, and cross-wall components, and the bound-tightening effects of physical low-dimensional representations, multi-source training, and parameter-space coverage are analyzed. Simulated and measured experiments jointly support the resulting theoretical analysis and illustrate its application value.
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