WiTraj: Robust Indoor Motion Tracking With WiFi Signals
WiTraj: Robust Indoor Motion Tracking With WiFi Signals
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DOI：
10.1109/tmc.2021.3133114
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发表时间：
2023-05
影响因子：
7.9
通讯作者：
Dan Wu;Youwei Zeng;Ruiyang Gao;Shenjie Li;Yang Li;R. Shah;Hong Lu;Daqing Zhang
中科院分区：
文献类型：
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作者：
Dan Wu;Youwei Zeng;Ruiyang Gao;Shenjie Li;Yang Li;R. Shah;Hong Lu;Daqing Zhang
基于WiFi的无设备运动跟踪系统跟踪人员，而不需要他们携带任何设备。现有的工作已经探索了从WiFi信道状态信息（CSI）提取的信号参数，诸如飞行时间（ToF）、到达角（AoA）和多普勒频移（DFS），以定位和跟踪房间中的人。然而，由于信号参数的不可靠估计，它们不是鲁棒的。ToF和AoA估计对于通常仅具有两个天线和有限信道带宽的当前符合标准的WiFi设备是不准确的。另一方面，DFS可以在当前设备上相对容易地提取，但是易受CSI测量中的高噪声水平和随机相位偏移的影响，这导致速度-符号-模糊性问题，并且呈现模糊的行走速度。本文提出了WiTraj，一种使用商品WiFi设备的无设备室内运动跟踪系统。WiTraj从三个方面提高跟踪鲁棒性：1）通过使用来自每个接收器的两个天线的CSI的比率，它显著地提高了DFS估计质量，2）为了更好地跟踪人类行走，它利用放置在不同视角处的多个接收器来捕获人类行走，然后智能地组合最佳视图以实现鲁棒的轨迹重建，以及，3）它将步行与日常生活中通常交错的原地活动区分开来，因此非步行活动不会导致跟踪错误。实验表明，WiTraj可以显着提高跟踪精度在典型的环境相比，现有的基于DFS的系统。对9名参与者和3种不同环境的评估表明，典型房间大小的轨迹的中位跟踪误差为2.5%。
WiFi-based device-free motion tracking systems track persons without requiring them to carry any device. Existing work has explored signal parameters such as time-of-flight (ToF), angle-of-arrival (AoA), and Doppler-frequency-shift (DFS) extracted from WiFi channel state information (CSI) to locate and track people in a room. However, they are not robust due to unreliable estimation of signal parameters. ToF and AoA estimations are not accurate for current standards-compliant WiFi devices that typically have only two antennas and limited channel bandwidth. On the other hand, DFS can be extracted relatively easily on current devices but is susceptible to the high noise level and random phase offset in CSI measurement, which results in a speed-sign-ambiguity problem and renders ambiguous walking speeds. This paper proposes WiTraj, a device-free indoor motion tracking system using commodity WiFi devices. WiTraj improves tracking robustness from three aspects: 1) It significantly improves DFS estimation quality by using the ratio of the CSI from two antennas of each receiver, 2) To better track human walking, it leverages multiple receivers placed at different viewing angles to capture human walking and then intelligently combines the best views to achieve a robust trajectory reconstruction, and, 3) It differentiates walking from in-place activities, which are typically interleaved in daily life, so that non-walking activities do not cause tracking errors. Experiments show that WiTraj can significantly improve tracking accuracy in typical environments compared to existing DFS-based systems. Evaluations across 9 participants and 3 different environments show that the median tracking error $2.5% for typical room-sized trajectories.