AdaptTrack: A Robust Tracking System for Complex Environments Based on WiFi Device Selection Strategy

In recent years, WiFi-based tracking has gained significant attention owing to its non-invasive, cost-effective, and ubiquitous coverage. These systems eliminate the need for wearable devices, making them highly suitable for applications in smart homes, health monitoring, and security surveillance. However, existing WiFi-based tracking systems face notable challenges, particularly in complex environments. Single-transceiver systems often rely on the estimation of Angle of Arrival (AoA) and Time of Flight (ToF), which is limited by the number of antennas and available bandwidth. Alternatively, multi-device systems are exploited to estimate the target's velocity via Doppler Frequency Shift (DFS). For the multidevice cooperative tracking systems, it is essential to evaluate the velocity estimation quality across devices and effectively leverage their complementarity to improve tracking performance. To this end, we propose AdaptTrack, an innovative human tracking system utilizing commercial WiFi devices. Specifically, we derive a quantization analysis of DFS estimation errors from Channel State Information (CSI) quotient. Furthermore, we design an adaptive device selection strategy that jointly considers the velocity estimation performance and the complementarity of WiFi devices to optimize tracking accuracy. In addition, we implement a prototype system based on commercial WiFi devices. These innovations enable AdaptTrack to achieve the high-precision tracking in complex scenarios. Extensive real-world experiments demonstrate the advantage of AdaptTrack in various environments, compared to the baselines. These results highlight its robustness, scalability, and potential as a practical solution for privacy-friendly human tracking in intelligent environments.