Efficient Recognition of Informative Measurement in the RF-Based Device-Free Localization.

Jiaju Tan,Xuemei Guo,Xin Zhao,Guoli Wang

doi:10.3390/s19051219

Jiaju Tan, Xuemei Guo + Show 2 more

Open Access

https://doi.org/10.3390/s19051219

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Abstract

Device-Free Localization (DFL) based on the Radio Frequency (RF) is an emerging wireless sensing technology to perceive the position information of the target. To realize the real-time DFL with lower power, Back-projection Radio Tomographic Imaging (BRTI) has been used as a lightweight method to achieve the goal. However, the multipath noise in the RF sensing network may interfere with the measurement and the BRTI reconstruction performance. To resist the multipath interference in the observed data, it is necessary to recognize the informative RF link measurements that are truly affected by the target appearance. However, the existing methods based on the RF link state analysis are limited by the complex distribution of the RF link state and the high time complexity. In this paper, to enhance the performance of RF link state analysis, the RF link state analysis is transformed into a decomposition problem of the RF link state matrix, and an efficient RF link recognition method based on the low-rank and sparse decomposition is proposed to sense the spatiotemporal variation of the RF link state and accurately figure out the target-affected RF links. From the experimental results, the RF links recognized by the proposed method effectively reflect the target-induced RSS measurement variation with less time. Besides, the proposed method by recognizing the informative measurement is helpful to improve the accuracy of BRTI and enhance the efficiency in actual DFL applications.

Highlights

Device-Free Localization (DFL) technology, which can detect the position of the target without the target carrying any electronic devices or attaching any tags, has developed rapidly in the area of assisted living [1]
The performance of the proposed method was compared with Kernel Density Estimation (KDE)-LS and Mixture of Gaussians (MoG)-LS, which are the combinational methods by using the Link Subtraction (LS) modification [21] to improve the estimated results by KDE [23] and MoG [22]
The experimental results for comparison included the recognition results of the target-affected foreground-state Radio Frequency (RF) Links (Section 5.1), the localization results based on Back-projection Radio Tomographic Imaging (BRTI) reconstruction by the recognized RF links (Section 5.2), and the sensitivity analysis of an RF link (Section 5.3)

Summary

Introduction

Device-Free Localization (DFL) technology, which can detect the position of the target without the target carrying any electronic devices or attaching any tags, has developed rapidly in the area of assisted living [1]. As a low-cost wireless computational imaging technique to estimate the target location and protect the target privacy [2], narrowband Radio Tomographic Imaging (RTI) has been utilized in many. How to accurately sense the target position information by effectively dealing with the negative impact of multipath interference has become a key problem in the low-cost RTI-based DFL applications [9]. To improve the results of MoG and KDE, which only estimate the temporal variation of RSS, based on the spatial similarity between the adjacent links [21], Link Subtraction (LS) analyzes the RF link state and additively subtracts the misjudged target-induced links, which are far away from the target. A method that can accurately and quickly recognize the RF link state by simultaneously perceiving the spatiotemporal RSS variation in actual DFL application is needed. Through-wall tracking with radio tomography networks using foreground detection

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Conclusion