FusionTrack: Towards Accurate Device-free Acoustic Motion Tracking with Signal Fusion

Abstract

Acoustic motion tracking is rapidly evolving with various applications. However, existing approaches still have some limitations. Tracking based on single-frequency continuous wave (CW) faces cumulative errors in tracking and limited accuracy in tracking the absolute location of the target. Tracking based on frequency-modulated continuous wave (FMCW) faces errors introduced by the Doppler and multipath effects. To overcome these limitations, we propose FusionTrack, a novel device-free motion-tracking approach that leverages the fusion of CW and FMCW signals. We eliminate the absolute tracking errors of FMCW-based tracking by compensating for Doppler frequency offsets with the results of CW-based relative tracking. Furthermore, we address the static multipath with down-sampling and filtering and mitigate the dynamic multipath with chirp aggregation. We employ a Kalman filter-based fusion of relative and absolute tracking to enhance accuracy further. We implement FusionTrack on Android smartphones for real-time tracking and perform extensive experiments. The results show that FusionTrack achieves real-time 1D tracking with an accuracy of 1.5 mm, which is 46% better than the existing approaches and extends the 1D tracking range to 2.2 m, which is 3.1× of the existing approaches. FusionTrack also achieves a 2D tracking accuracy of 4.5 mm.