Device-free human tracking is an essential ingredient for ubiquitous wireless sensing. Recent passive WiFi tracking systems face the challenges of inaccurate separation of dynamic human components and time-consuming estimation of multi-dimensional signal parameters. In this work, we present a scheme named WiFi Doppler Frequency Shift (WiDFS), which can achieve single-target real-time passive tracking using channel state information (CSI) collected from commercial-off-the-shelf (COTS) WiFi devices. We consider the typical system setup including a transmitter with a single antenna and a receiver with three antennas; while our scheme can be readily extended to another setup. To remove the impact of transceiver asynchronization, we first apply CSI cross-correlation between each RX antenna pair. We then combine them to estimate a Doppler frequency shift (DFS) in a short-time window. After that, we leverage the DFS estimate to separate dynamic human components from CSI self-correlation terms of each antenna, thereby separately calculating angle-of-arrival (AoA) and human reflection distance for tracking. In addition, a hardware calibration algorithm is presented to refine the spacing between RX antennas and eliminate the hardware-related phase differences between them. A prototype demonstrates that WiDFS can achieve real-time tracking with a median position error of 72.32 cm in multipath-rich environments.
基于信道状态信息（Channel State Information，CSI）的指纹识别技术在WIFI室内定位中得到了广泛的关注，频率多样且时间稳定的CSI比粗接收信号强度（Recovered Signal Strength，RSS）更能反映位置相关的信道特性。然而，CSI的获取需要接入点（AP）的合作并且仅涉及多个帧，这对现实世界的部署施加了限制。在本文中，我们提出了CRISLoc，第一个CSI指纹定位原型系统使用无处不在的智能手机。CRISLoc在一个完全被动的模式下运行，在他自己的CSI采集过程中偷听数据包。通过校准WiFi放大器电路施加的失真来净化智能手机CSI。CRISLoc通过联合聚类和离群值检测方法来解决改变AP的挑战。提出了一种新的迁移学习方法，在过时的指纹和一些新的测量的基础上重建高维CSI指纹数据库，并提出了一种增强的KNN方法来精确定位智能手机的位置。我们的研究揭示了智能手机CSI的稳定性和灵敏度的重要属性，这是以前没有报道过的。实验结果表明，CRISLoc在6m × 8m的研究实验室中可以实现约0.29m的平均误差。移动一个和两个AP后，平均误差分别增加了5.4 cm和8.6 cm，这验证了CRISLoc对环境变化的鲁棒性。
