DuTrack: Long-Term Indoor Human Tracking with Dual-Channel Sensing and Inference

TL;DR

DuTrack is a novel indoor human tracking system that combines Wi-Fi and acoustic signals to improve long-term tracking stability and accuracy in smart home environments.

Contribution

The paper introduces a fusion-based approach that integrates electromagnetic and mechanical wave sensing to reduce tracking errors over time.

Findings

Achieves 89.37% reduction in median tracking error compared to model-based methods.

Achieves 65.02% reduction in median tracking error compared to data-driven methods.

Demonstrates superior performance in long-term indoor human tracking.

Abstract

Wi-Fi tracking technology demonstrates promising potential for future smart home and intelligent family care. Currently, accurate Wi-Fi tracking methods rely primarily on fine-grained velocity features. However, such velocity-based approaches suffer from the problem of accumulative errors, making it challenging to stably track users' trajectories over a long period of time. This paper presents DuTrack, a fusion-based tracking system for stable human tracking. The fundamental idea is to leverage the ubiquitous acoustic signals in households to rectify the accumulative Wi-Fi tracking error. Theoretically, Wi-Fi sensing in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios can be modeled as elliptical Fresnel zones and hyperbolic zones, respectively. By designing acoustic sensing signals, we are able to model the acoustic sensing zones as a series of hyperbolic clusters. We reveal how to fuse the fields of electromagnetic waves and mechanical waves, and establish the optimization equation. Next, we design a data-driven architecture to solve the aforementioned optimization equation. Experimental results show that the proposed multimodal tracking scheme exhibits superior performance. We achieve a 89.37% reduction in median tracking error compared to model-based methods and a 65.02% reduction compared to data-driven methods.