freeDoppler: A Doppler Effect Learning Network for Accurate RF-based Velocity Estimation

Accurately estimating the velocity (including speed and direction) of moving targets has recently attracted widespread attention in augmented reality, security monitoring and sports health. In particular, the Doppler Frequency Shift (DFS)-based velocity estimation schemes using WiFi devices have shown great potential and have been widely studied. However, previous Fast Fourier Transform (FFT)-based and path-parameter-based arts have inherent limitations in DFS estimation and, worse still, ignore the nonlinear measurement errors caused by the relative orientation between the moving target and the WiFi transceiver. The above limitations make it difficult to meet the requirements for fine-grained velocity estimation in practical applications. To cope with these limitations, in this article, we propose a learning-based velocity estimation framework, named freeDoppler , to achieve fine-grained, multi-target and orientation-independent velocity estimation. Specifically, we construct a WiFi-based Velocity Estimation Network (VEN), which leverages continuous complex-valued Channel State Information (CSI) sequences as input, to fully learn the inherent information of the Doppler effect and accurately predict velocity series. In addition, we adopt the electric field scattering model of Maxwell’s equations to construct a physics-informed CSI Generation Model (CGM), thereby generating large-scale and high-quality simulated CSI samples to improve the generalization of the VEN model. Throughout extensive real-world experiments, freeDoppler can achieve median errors of 7.98 cm/s for speed estimation, 28° for direction estimation and 35 cm for human tracking in one or two moving targets, significantly outperforming the state-of-the-art methods.