Abstract
Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%.
Highlights
Device-free localization (DFL) is a promising technology in wireless sensor networks (WSNs) that focuses on the detection of the position information and other moving status information of humans who do not carry any tractable electronic devices with them
The proposed algorithm uses the distance-weighted geometric midpoint of the segments with endpoints, which are the intersection points formed by the intersection of the links in the wireless network to estimate the position of the target
We proposed an effective wireless link detection method that can detect the fluctuations caused by targets correctly, especially in indoor environments wherein the multipath effect is severe
Summary
Device-free localization (DFL) is a promising technology in wireless sensor networks (WSNs) that focuses on the detection of the position information and other moving status information of humans who do not carry any tractable electronic devices with them. To solve the conflict between the demand for high accuracy and the problems mentioned above, we focus on the geometry of the intersection point among the links in the wireless network and propose a geometric method. The proposed algorithm uses the distance-weighted geometric midpoint of the segments with endpoints, which are the intersection points formed by the intersection of the links in the wireless network to estimate the position of the target. We proposed an effective wireless link detection method that can detect the fluctuations caused by targets correctly, especially in indoor environments wherein the multipath effect is severe. As this is the first step of the proposed GM scheme, it makes important contributions to the final system tracking accuracy.
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