Device-free Wi-Fi localization is a promising technology to localize users who do not carry smart devices. The basic idea is to separate and analyze the signals reflected off human body from the multi-path signals. However, previous works could only localize moving users, because they can not distinguish the signals reflected off static users or objects like walls and furniture. This paper presents the Respiration Localization system,RespLoc, which for the first time enables device-free Wi-Fi localization system for static users. To recognize static users, the key insight is that people can breathe but objects cannot. However, it is non-trivial to extract user’s location from the respiration signal, because the respiration signal is significantly weaker than the regular motion signal. To this end, we propose the equivalent analysis method. Instead of using traditional signal separation, which suffers from severe noise due to residual signal components, we propose to construct an equivalent signal with the following properties: First, the equivalent signal follows the same variation law as the respiration signal; Second, the equivalent signal is not affected by irrelevant static signals. Based on this equivalent signal, we are able to resolve location features from the multi-path signals directly without separating them. We implementRespLocon commodity Wi-Fi devices, and extensive experimental results demonstrate thatRespLoccan localize static users with a median error of 0.89 meters.

The evolution of Wi-Fi to next-generation 802.11bf demonstrates the potential of device-free Wi-Fi sensing applications, where we can remotely infer the behaviors of users without bringing into physical contact with them. Among these sensing applications, Wi-Fi tracking is critical to provide location based services. Recent Wi-Fi tracking systems can be cataloged into model-based and data-based approaches: (1) the model-based approach is to build the mathematical tracking model. However, this method is sensitive to environmental noise, and spends more execution time; (2) the data-based approach is to train a neural network. However, this method requires a lot of efforts to collect training dataset, and cannot handle all types of trajectories well. To resolve these issues, we propose theNNE-Tracking, a Neural Network Enhanced tracking framework. The core design principle ofNNE-Trackingis as follows: we improve the tracking accuracy based on the data-based approach, and utilize the model-based approach to supervise whether the neural network is already working well. Moreover, we also design a framework to estimate unknown parameters of the tracking model, so that the system can automatically generate the Wi-Fi map. We take the Wi-Fi passive tracking as a specific example to explain how to applyNNE-Trackingin practical applications. Experimental results demonstrate that our design can reduce 59.4% ∼ 85.3% tracking errors while significantly saving execution time. As for deployment costs, we can automatically infer the Wi-Fi map without manual calibration; As for stability, when we repeat the training process with different hidden layers and random seeds, the tracking standard deviation of these neural networks is only 1.4cm.

Device-free Wi-Fi tracking has become essential for ubiquitous wireless sensing. However, current device-free Wi-Fi tracking systems suffer from two limitations: First, abnormal signals interfere with tracking performance when the user walks across the direct link of the transceivers and second, the tracking error based on the velocity integral accumulates over time. This article proposes CrossTrack, the first device-free cross-link tracking system with commodity Wi-Fi. Our inspiration is to regard the cross-link behavior as an opportunity to correct the trajectory instead of disturbing noise like previous work. Our approach involves three main steps. First, we devise a metric that is capable of detecting cross-link behavior. Second, we propose a new theoretical model that identifies the cross-link position as a landmark. Third, we develop a path revision technique that utilizes this landmark to optimize the trajectory. The technique innovation of this article is to reveal the theoretical approach to transform cross-link interference into optimization in device-free tracking for the first time. We implement CrossTrack based on commercial Wi-Fi devices and conduct comprehensive experiments. Our results show that CrossTrack can reduce tracking errors by 48.75%, and the median tracking error is 0.41 m.