Device-Free Electromagnetic Passive Localization With Frequency Diversity

Abstract

Abstract-As an emerging wireless localization technique, the electromagnetic passive localization without the need of carrying any device, named device-free passive localization (DFPL) technique has drawn considerable research attention. The DFPL technique detects shadowed links in the monitored area and realizes localization with the received signal strength (RSS) measurements of these links. However, the current RSS-based DFPL techniques have two major challenges: one is that the RSS signal is particularly sensitive to noise, and the other is that it needs a sufficient number of nodes to provide enough RSS measurements of wireless links to guarantee good performance. To overcome these problems, in this paper we take advantage of compressive sensing (CS) theory to handle the spatial sparsity of the DFPL problem for reducing the number of nodes required by DFPL systems and exploit the frequency diversity technique to deal with the problem of the RSS sensitivity. Meanwhile, inspired by the fact that the target's movement is continuous and that the target's current location must be around the last location, we add prior information on the support region into the sparse reconstruction process for enhancing sparse reconstruction performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.

Enhanced Radio Tomographic Imaging Method for Device-Free Localization Using a Gradual-Changing Weight Model

“…So far, there are two widely used approaches to perform DFL: fingerprint-based method [3][4][5] and model-based method [6][7][8][9][10][11][12][13][14][15]. Fingerprint-based methods need to obtain a pre-trained database of training data labeled with a target's position in the offline stage.…”

“…This image is then used to infer the positions of targets within the deployed network. The performance of the shadowing-based RTI is further improved by the subsequent studies on variance-based RTI [7], histogram-based RTI [8], channel diversity RTI [9,10], fade-level RTI [11], directional RTI [12], or compressive sensing (CS) methods [13][14][15]. The benefits of the RTI approach are its intuitive expression through images and high computational efficiency.…”