A real-time device-free localization system using correlated RSS measurements

Abstract

Device-free localization (DFL) with wireless sensor networks (WSN) is an emerging technology for target localization, which has received much attention in the area of Internet of Things. Received signal strength (RSS) measurements are the key to realize DFL and mainly affects the localization performance. Most existing approaches need to measure the RSS of all the wireless links in WSN, which take much time on measurement process and localization algorithm due to the large amounts of RSS data, thus they are inefficient, especially in the case of target tracking. In this paper, by making full use of the consecutiveness of motion, we present an efficient measurement strategy based on a small set of correlated wireless links. Furthermore, a lightweight compressed maximum matching select (CMMS) algorithm is proposed to localize target, which only needs a small-scale matrix-vector product operating for one estimation. The proposed approach can significantly reduce the number of RSS measurements and improve the real-time capability of the DFL system. Experimental results demonstrate the superior performance of the proposed method in the context of target localization and tracking.

Device-Free Electromagnetic Passive Localization With Frequency Diversity

“…However, most CS-based localization researches concentrated on devicedependent methods, and few works adopted the CS method to realize DFPL [10][11][12]. To the best of our knowledge, Kanso and Rabbat carried out the first sparsity-based work to combine RF tomography and CS to solve the DFPL problem [10].…”

“…Then, we compare the proposed method with two state-of-the-art CS-based DFPL schemes, i.e., the l 1 norm minimization algorithm in [10] and the greedy-based CMMS algorithm in [11] under the same conditions. Table 1 summarizes detailed statistical results of tracking errors of the SF-DFPL and MF-DFPL with different algorithms.…”

“…Therefore, the number of targets, P , is considerably smaller than the number of grids N based on sufficient dense gridding, and thus the location information vector x t is an extremely sparse signal. Inspired by this fact, references [10][11][12] attempted to utilize CS theory to tackle the DFPL problem, which leads to arg min…”

“…Since the deployment area is divided into many grids, the shadowing loss can be approximated as the sum of attenuation that occurs in each square [8][9][10][11][12], hence, ∆R t (i, f ) can be written as…”

“…They used the l 1 norm minimization scheme to estimate the location of the target and achieved better performance than the regularization method. In [11,12], the greedy algorithms were used to estimate targets' positions in DFPL systems, which results in a substantial reduction of the amount of measurements. The reference [13] exploited the dictionary learning technique to adjust the basis matrix in order to compensate the inaccuracy of the basis matrix.…”