Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate targets’ velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve $9.38cm/s$ , 13.42°, $31.08cm$ median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.

中文翻译：

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重新思考多普勒效应以使用商品 WiFi 设备进行准确的速度估计

使无处不在的 WiFi 设备具有非接触式传感能力是集成传感和通信领域的一个重要课题。多普勒效应已被广泛用于从无线信号估计目标的速度。然而，信号源和接收器的分离使基于 WiFi 的非接触式传感系统中多普勒频移 (DFS) 和目标速度之间的关系复杂化。与依赖近似关系或粗粒度信息（例如目标是向还是远离 WiFi 收发器移动）的现有工作相比，本文严格研究了 WiFi 传感系统中速度估计精度对目标位置和航向的依赖性。理论见解使我们能够推导出封闭形式的解决方案并了解速度估计的基本限制。为了优化速度估计性能，我们设计了一种接收设备选择方案，在多个可用 WiFi 设备中动态选择最佳接收器集。使用商品 WiFi 设备实现了原型实时目标跟踪系统。大量的实验结果表明，所提出的系统在速度估计和跟踪方面优于最先进的方法，并且能够实现 使用商品 WiFi 设备实现了原型实时目标跟踪系统。大量的实验结果表明，所提出的系统在速度估计和跟踪方面优于最先进的方法，并且能够实现 使用商品 WiFi 设备实现了原型实时目标跟踪系统。大量的实验结果表明，所提出的系统在速度估计和跟踪方面优于最先进的方法，并且能够实现 $9.38 厘米/秒$, 13.42°, $31.08 厘米$在三个室内环境中进行的实验中，速度、航向和位置估计的中值误差具有三个设备放置和八个人类受试者超过 15 条轨迹。