用于仿真到真实跨方向无线感知的双注意力与对抗迁移网络

中文摘要 AI 辅助

毫米波人体活动识别在用户相对于感知系统的方向发生变化时，性能会显著下降，但收集带标注的多方向数据既费力又成本高昂。为了无需详尽的多方向实测数据，我们开发了一种物理引导的仿真器，可从单方向运动合成方向多样的无线训练数据。具体而言，为抑制方向导致的特征变化，我们提出了一种双注意力网络，用于从双链路多普勒频谱图中提取具有活动判别性且对方向鲁棒的表示。为弥合仿真到真实的差距，我们引入了一种对抗无监督迁移学习机制，仅使用少量未标注的目标域样本对齐特征分布。S2M-Sense平台在复现真实世界信号方面表现出高保真度，经与60.48 GHz毫米波实测数据验证，在所有4种活动和4种方向下，仿真与实测多普勒频谱图的平均结构相似性指数测度（SSIM）为0.84。实验结果表明，仅使用双链路多方向仿真数据集时，S2M-Sense的识别准确率为88.33%；在仅用16个未标注实测样本进行仿真到真实的迁移学习后，准确率提升至95%。迁移学习和未迁移学习的两种情况均优于最先进的跨域感知方法。

英文摘要

Millimeter-wave human activity recognition suffers significant performance degradation when the user's orientation changes relative to the sensing system, yet collecting labeled multi-orientation data is labor-intensive and costly. To eliminate the need for exhaustive multi-orientation measured data, we develop a physics-guided simulator that synthesizes orientation-diverse wireless training data from single-orientation motion. Specifically, to suppress orientation-induced feature variations, we propose a dual-attention network that extracts activity-discriminative and orientation-robust representations from dual-link Doppler spectrograms. To bridge the simulation-to-reality gap, we introduce an adversarial unsupervised transfer learning mechanism that aligns feature distributions using only a small number of unlabeled target-domain samples. The S2M-Sense platform shows high fidelity in reproducing real-world signatures, validated against 60.48 GHz mmWave measured data with an average structural similarity index measure (SSIM) of 0.84 between simulated and measured Doppler spectrograms across all 4 activities and 4 orientations. Experimental results show that S2M-Sense achieves 88.33% recognition accuracy using only the dual-link multi-orientation simulated dataset, which improves to 95% after simulation-to-reality transfer learning with as few as 16 unlabeled measured samples. Both cases with and without transfer learning outperform state-of-the-art cross-domain sensing methods.

发表机构

• National Graduate College for Engineers, Southern University of Science and Technology（南方科技大学国家工程师学院）
• Harbin Institute of Technology（哈尔滨工业大学）
• Southern University of Science and Technology（南方科技大学）

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