ML-Track: Passive Human Tracking Using WiFi Multi-Link Round-Trip CSI and Particle Filter

Abstract

In this study, we present ML-Track, an innovative uncooperative passive tracking system leveraging WiFi communication signals between multiple devices. Our approach is realized with three pivotal techniques. First, we introduce a novel protocol termed multi-link round-trip CSI, which enables multi-link bistatic Doppler detection within a WiFi network. Second, a phase error cancellation method is developed, and we demonstrate a 0.92 rad reduction in error (0.96 to 0.04 rad) experimentally. Lastly, we propose a particle-filter-based back-end to track a moving human in the room passively without the need for the participant to carry any type of cooperative or active device. A prototype system is constructed using four Raspberry Pi CM4 units and subjected to real-world evaluations. Experimental results indicate a median error of approximately 0.23 m for tracking, which corresponds to a relative error of 5.8% based on the 4 m side length of the experimental field. Compared to existing studies, a distinct advantage of our system is it can run with non-MIMO (single-antenna) WiFi devices, making it particularly suitable for budget or low-profile WiFi hardware. This compatibility makes it an ideal fit for real-world Internet-of-Things (IoT) devices. Moreover, in terms of computational demands, our solution excels, delivering real-time performance on the Raspberry Pi CM4 while utilizing just 20% of its CPU capability and drawing a modest 2.5 watts of power.