复杂着装场景下物理感知重构毫米波雷达步态识别

黄玲; 邱立迎; 王嘉诚; 韩鹏霖; 周青笛; 闫慧梅

doi:10.11999/JEIT260522

复杂着装场景下物理感知重构毫米波雷达步态识别

doi: 10.11999/JEIT260522 cstr: 32379.14.JEIT260522

兰州理工大学微电子现代产业学院甘肃兰州 730050

基金项目: 甘肃省教育厅产业支撑计划项目(2026CYZC-027，2025CYZC-02)，甘肃省自然科学基金重点项目(25JRRA062)，甘肃省重点人才项目(2025RCXM022)

详细信息

作者简介:
黄玲：女，博士，教授，博士生导师，研究方向为雷达行为识别

邱立迎：女，硕士，研究方向为雷达行为识别

王嘉诚：男，硕士，研究方向为雷达行为识别

韩鹏霖：男，硕士，研究方向为雷达行为识别

周青笛：女，硕士，研究方向为脑电情绪识别

闫慧梅：女，硕士，研究方向为雷达行为识别

通讯作者:
黄玲　hlfighting@163.com

中图分类号: TN957
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出版历程
- 收稿日期: 2026-04-27
- 修回日期: 2026-07-14
- 录用日期: 2026-07-14
- 网络出版日期: 2026-07-24

Physics-Aware Reconstruction for Millimeter-Wave Radar Gait Recognition Under Complex Wearing Scenarios

school of Modern Microelectronics Industry, Lanzhou University of Technology, Lanzhou 730050, China

Funds: Gansu Provincial Education Department Industrial Support Program (2026CYZC-027), The Key Project of Natural Science Foundation of Gansu Province (25JRRA062), 2025CYZC-02), Gansu Provincial Key Talent Program (2025RCXM022)

摘要

摘要: 毫米波雷达步态识别在智能安防领域具有重要应用潜力，但在实际应用中，复杂着装(如大衣、背包等)会引入非稳定高频干扰，降低了现有模型的识别性能。其主要原因是现有方法没能充分考虑多普勒频率与人体运动之间的物理映射关系。针对上述问题，本文提出一种基于物理感知微多普勒重构与自适应干扰抑制的步态识别框架。该框架利用人体运动产生的多普勒频率与生物力学机理间的映射关系，将混叠的原始微多普勒频谱分离为躯干与四肢专属频带，从物理特征层面隔离复杂着装因素(如穿大衣、挎包等)引入的噪声干扰。在此基础上，设计加权注意力抑制机制，对衣服遮挡引起的高频异常噪声进行自适应建模，并增强判别性躯干特征表示，实现鲁棒特征融合。在公开数据集MMRGait-1.0上的实验结果表明，所提方法在仅0.75 GFLOPs的计算开销下取得了89.7%的平均Rank-1识别率；在大衣遮挡等复杂场景下仍可达到85.1%的识别精度，较基线方法提升近18%。实验结果验证了该方法在复杂着装条件下的有效性与鲁棒性。
- 毫米波雷达 /
- 步态识别 /
- 微多普勒效应 /
- 注意力机制 /
- 深度学习
Abstract: Objective Millimeter-wave (MMW) radar gait recognition has demonstrated considerable potential in the domain of non-contact biometric identification, primarily due to its inherent advantages in privacy preservation and resilience to variable lighting conditions. However, a significant challenge persists when applying these systems to unconstrained real-world environments where subjects may wear complex clothing, such as long coats or carry backpacks. These external covariates introduce non-stationary, high-frequency spectral components that result in severe spectral aliasing and masking of the intrinsic micro-Doppler (m-D) signatures of the human body. Traditional deep learning approaches usually treat radar spectrograms as generic image data and neglect the physical relationship between Doppler frequency shifts and human motion. Consequently, clothing-induced interference may overlap with motion-related signals, reducing recognition accuracy. Therefore, it is imperative to develop a framework that integrates radar physics and biomechanical properties to achieve effective signal decoupling and interference suppression. This study aims to provide a robust solution for radar-based gait recognition in complex scenarios by incorporating explicit physical constraints into the neural network architecture. Methods To mitigate the adverse effects of clothing-induced interference, this paper introduces PRISM-Net, a physics-aware reconstruction framework for millimeter-wave radar gait recognition. The framework is grounded in the biomechanical characteristics of human motion, specifically the observation that the human torso, representing the primary mass, generates stable and low-frequency Doppler shifts, whereas the movement of limbs produces higher-frequency, periodically alternating spectral components. (1) Physics-aware Frequency Structural Reconstruction: The proposed method diverges from conventional uniform processing by implementing a structural decoupling strategy. Based on the velocity distribution defined by biomechanical properties, the aliased original m-D spectrogram is partitioned into discrete frequency bands. The low-frequency band is dedicated to the torso, capturing stable and identity-persistent features, while the high-frequency band encompasses limb dynamics. This spatial-frequency partitioning enables the network to isolate the spectral regions most susceptible to clothing-induced clutter at the physical layer, thereby suppressing interference propagation. (2) Adaptive Weighted Attention Mechanism (WAM): An adaptive WAM module is integrated to regulate the signal-to-noise ratio (SNR) in the feature space. Given that clothing interference is dynamic and predominantly occupies high-frequency regions, the WAM adaptively evaluates the reliability of different frequency-derived features. In cases where the high-frequency limb features are corrupted by non-stationary noise from swinging garments, the WAM applies soft-thresholding to reduce their response weights. Simultaneously, the contribution of the more reliable torso-related features is enhanced. (3) Experimental Configuration and Protocol: The model is evaluated using the MMRGait-1.0 dataset, which provides a comprehensive set of radar gait signatures under various covariates. To ensure the assessment reflects real-world generalization, an open-set testing protocol is strictly followed. The training set comprises data from 74 subjects, while 47 entirely unseen subjects are used for evaluation. All radar spectrograms are processed to a standard resolution of 224 × 224 pixels. The network is optimized using the AdamW algorithm with a joint loss function consisting of cross-entropy and feature regularization components to ensure both classification accuracy and feature discriminability. Results and Discussions The experimental results demonstrate the effectiveness of integrating the biomechanical characteristics of human motion into the recognition process. As shown in Table 1, PRISM-Net achieves an average Rank-1 recognition rate of 89.7% in the 90-degree side-view scenario. In the coat (CT) occlusion scenario, the model maintains an accuracy of 85.1%. This is a 18.1% improvement over traditional lightweight models such as ShuffleNetV2 and a 7.4% improvement over the standard ResNet-18 baseline. The stability of the model was verified through ten independent repeated trials. As illustrated in the box plot in Fig. 1, PRISM-Net shows a low standard deviation of ±0.55%, whereas the baseline model exhibits a higher deviation of ±1.75%. An independent samples t-test results in a p-value of less than 0.001, confirming statistical significance. Ablation studies in Table 2 confirm the contribution of each module. Removing frequency decoupling reduces accuracy in the CT scenario to 75.5%, proving that physical structural separation is necessary to prevent feature distortion. The WAM module further provides a 4.2% accuracy gain by adaptively filtering noise. Regarding computational efficiency, Table 3 shows that PRISM-Net requires 11.33M parameters and 0.75 GFLOPs. Compared with heavy 3D-convolutional models that require over 10 GFLOPs, the proposed method achieves superior accuracy with significantly lower resource consumption. Finally, t-SNE visualization in Fig. 5 shows that PRISM-Net produces more compact intra-class distributions and clearer inter-class margins demonstrating improved feature discriminability. Conclusions This research demonstrates that the integration of the biomechanical characteristics of human motion into deep learning architectures improves the robustness of radar gait recognition. The proposed PRISM-Net framework effectively decouples motion components and suppresses clothing-induced noise through physics-aware frequency reconstruction and adaptive weighting. The high recognition accuracy and low computational complexity observed in the MMRGait-1.0 dataset suggest that this approach is suitable for real-time security applications on edge-computing devices.
- Millimeter-wave radar /
- Gait recognition /
- Micro-Doppler effect /
- Attention mechanism /
- Deep Learning

HTML全文

图 1 PRISM-Net整体架构图

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图 2 不同着装场景下的时频谱图与平均频谱能量分布

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图 3 张量截断与切分示意图

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图 4 WAM模块结构图

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图 5 箱线图

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图 6 双折线图

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图 7 ResNet-18(a)与 PRISM-Net(b)在测试集上的特征分布

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表 1 不同速度区间的能量占比统计结果 (%)

场景	[–6,–3]	[–3,3]
NM	0.32	99.68
BG	0.33	99.67
CT	0.31	99.69

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表 2 不同方法在 90° 侧视视角下的 Rank-1 准确率对比 (%)

方法名称	正常 (NM)	挎包 (BG)	穿大衣 (CT)	平均 (Average)
PIWMW^[4]	55.8	39.5	34.8	43.4
ShuffleNet V2	87.2	61.7	67.0	72.0
MobileNetV2	87.2	86.2	66.0	79.8
GaitPar^[24]	88.3	83.0	76.6	82.6
ResNet-18	89.4	81.9	77.7	83.0
Du等人^[22]	91.8	80.2	80.2	84.1
Ding等人^[2]	92.7	81.9	77.7	84.1
本文方法	95.8	88.2	85.1	89.7

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表 3 消融实验(%)

模型变体	正常 (NM)	挎包 (BG)	穿大衣 (CT)	平均 (Mean)
Global Stream Only	94.7	84.0	75.5	84.8
PRISM Stream Only	94.7	87.2	73.4	85.1
No WAM	90.4	84.0	80.9	85.1
PRISM-Net (Ours)	95.7	88.3	85.1	89.7

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表 4 计算复杂度对比

模型	参数量 (M)	计算效率 (GFLOPs)
PIWMW^[4]	0.9	0.2
ResNet-18	11.31	0.74
MobileNetV2	4.78	0.53
ShuffleNet V2	2.77	0.25
GaitPart^[24]	2.79	0.58
Du等人^[22]	21.3	14.0
Ding等人^[2]	42.8	7.4
本文方法	11.33	0.75

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