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

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.

Uni-Fi: Integrated Multi-Task Wi-Fi Sensing

Wi-Fi sensing technology enables non-intrusive, continuous monitoring of user locations and activities, which supports diverse smart home applications. Since different sensing tasks exhibit contextual relationships, their integration can enhance individual module performance. However, integrating sensing tasks across different research efforts faces challenges due to the absence of two key elements. The first is a unified architecture that captures the fundamental nature shared across diverse sensing tasks. The second is an extensible pipeline that can integrate sensing methodologies proposed in potential future research. This paper presents Uni-Fi, an extensible framework for multi-task Wi-Fi sensing integration. This paper makes the following contributions. First, we propose a unified theoretical framework that reveals the fundamental differences between single-task and multi-task sensing. Second, we develop a scalable sensing pipeline that automatically generates multi-task sensing solvers, enabling seamless integration of multiple sensing models. Experimental results show that Uni-Fi achieves robust performance across tasks, with a localization error of approximately 0.54 meters, 98.34 percent accuracy for activity classification, and 98.57 percent accuracy for presence detection.