融合多模态信息提升Wi-Fi感知鲁棒性与精度
A Short Overview of Multi-Modal Wi-Fi Sensing
- 用其他模态作教师指导Wi-Fi感知模型学习
- 多模态融合显著提升动作识别与定位准确率
- 适合关注智能感知与无线传感融合的研究者
Wi-Fi感知已成为无线感知与感知通信一体化（ISAC）中的关键技术，具备低成本、高穿透性与隐私保护优势，广泛应用于动作识别、人体定位和人群计数等场景。然而，其仍面临鲁棒性差与数据采集困难等挑战。近年来，多模态Wi-Fi感知受到关注，通过引入其他模态作为教师提供真实标签或鲁棒特征，或直接与Wi-Fi信号融合以增强感知能力。尽管这些方法在实际应用中展现出显著效果，但相关研究缺乏系统性综述。本文回顾了过去24个月的多模态Wi-Fi感知文献，梳理当前局限、挑战与未来方向。
原文摘要 · Abstract (English)
Wi-Fi sensing has emerged as a significant technology in wireless sensing and Integrated Sensing and Communication (ISAC), offering benefits such as low cost, high penetration, and enhanced privacy. Currently, it is widely utilized in various applications, including action recognition, human localization, and crowd counting. However, Wi-Fi sensing also faces challenges, such as low robustness and difficulties in data collection. Recently, there has been an increasing focus on multi-modal Wi-Fi sensing, where other modalities can act as teachers, providing ground truth or robust features for Wi-Fi sensing models to learn from, or can be directly fused with Wi-Fi for enhanced sensing capabilities. Although these methods have demonstrated promising results and substantial value in practical applications, there is a lack of comprehensive surveys reviewing them. To address this gap, this paper reviews the multi-modal Wi-Fi sensing literature \textbf{from the past 24 months} and highlights the current limitations, challenges and future directions in this field.
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