基于自适应主成分分析的软件定义无线电穿墙检测
Through-Wall Detection using Software-Defined Radio based on adaptive Principal Component Analysis
- Solinnov Pty Ltd（Solinnov有限公司）
- Monash University（莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于软件定义无线电的自适应主成分分析穿墙检测系统，通过谱域评分选择运动相关主成分，减少误检测并提升时频能量脊清晰度。
AI中文摘要：
利用机会性WiFi信号的穿墙检测（TWD）可实现安全和救援应用中的非侵入式感知；然而，许多现有方法依赖于受控接入点或专用硬件。本文提出了一种TWD系统，该系统使用定制的软件定义无线电（SDR）Bluebottle从环境WiFi数据包中提取信道状态信息（CSI），无需控制发射机。关键贡献是一种谱域评分机制，用于从CSI的主成分分析（PCA）分解中自适应选择与运动相关的主成分，利用Welch功率谱密度估计来量化每个成分在人体运动相关频带内的信噪比和谱集中度。然后使用连续小波变换对所选成分进行分析，以稳健地识别时间局部运动事件。实验结果表明，与传统的固定成分PCA相比，所提出的自适应成分选择方法持续减少误检测，并产生更清晰的时频能量脊。
英文摘要：
Through-Wall Detection (TWD) using opportunistic WiFi signals enables non-invasive sensing for security and rescue applications; however many existing approaches rely on controlled access points or specialised hardware. This paper presents a TWD system that extracts Channel State Information (CSI) from ambient WiFi packets using a customised software-defined radio (SDR), Bluebottle, without requiring transmitter control. The key contribution is a spectral-domain scoring mechanism for adaptively selecting motion-relevant principal components from a Principal Component Analysis (PCA) decomposition of the CSI, using Welch power spectral density estimates to quantify each component's signal-to-noise ratio and spectral concentration within the frequency band associated with human motion. The selected components are then analysed using a continuous wavelet transform to robustly identify time-localised motion events. Experimental results demonstrate that the proposed adaptive component-selection method consistently reduces false detections and produces sharper time-frequency energy ridges compared to conventional fixed-component PCA.
