论文标题
视觉辅助框架捕获基于WIFI传感的CSI重新组件：一种多模式方法
Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach
论文作者
论文摘要
从波束形成反馈矩阵（BFM）中重新组合通道状态信息（CSI），该矩阵是CSI的压缩版本，可以由于缺乏加密而被捕获，是实现固件敏捷的WiFi传感的另一种方法。在这项研究中，我们建议使用摄像头图像来准确增强BFM的CSI重新分配。这种视觉辅助的CSI重新分配的关键动机是绘制第一手见解，即BFM并不完全涉及空间信息以重新合材料CSI，并且可以通过相机图像来补偿这一点。为了利用相机图像，我们使用多模式深度学习，其中两种模式（即图像和BFM）都集成在一起以重新合作CSI。我们使用IEEE 802.11ac设备进行了实验。实验结果证实，与仅使用图像或BFM的单模式框架相比，提出的多模式框架的重组精度得到了提高。
Recompositing channel state information (CSI) from the beamforming feedback matrix (BFM), which is a compressed version of CSI and can be captured because of its lack of encryption, is an alternative way of implementing firmware-agnostic WiFi sensing. In this study, we propose the use of camera images toward the accuracy enhancement of CSI recomposition from BFM. The key motivation for this vision-aided CSI recomposition is to draw a first-hand insight that the BFM does not fully involve spatial information to recomposite CSI and that this could be compensated by camera images. To leverage the camera images, we use multimodal deep learning, where the two modalities, i.e., images and BFMs, are integrated to recomposite the CSI. We conducted experiments using IEEE 802.11ac devices. The experimental results confirmed that the recomposition accuracy of the proposed multimodal framework is improved compared to the single-modal framework only using images or BFMs.
