A New Elliptical Model for Device-Free Localization

Abstract

Device-free localization (DFL) based on wireless sensor networks (WSNs) is expected to detect and locate a person without the need for any wireless devices. Radio tomographic imaging (RTI) has attracted wide attention from researchers as an emerging important technology in WSNs. However, there is much room for improvement in localization estimation accuracy. In this paper, we propose a geometry-based elliptical model and adopt the orthogonal matching pursuit (OMP) algorithm. The new elliptical model uses not only line-of-sight information, but also non-line-of-sight information, which divides one ellipse into several areas with different weights. Meanwhile the OMP, which can eliminate extra bright spots in image reconstruction, is used to derive an image estimator. The experimental results demonstrate that the proposed algorithm could improve the accuracy of positioning by up to 23.8% for one person and 33.3% for two persons over some state-of-the-art RTI methods.

Logistic regression-based device-free localization in changeable environments

“…Device-free localization (DFL) [1] can detect and locate a person without the need of electronic devices, i.e., wireless sensors [2], [3]. Fingerprint-based DFL in changeable environments, has attracted a great deal of research attention [4]- [7].…”

Comments on “A New Elliptical Model for Device-Free Localization”

“…In [1], the authors propose a new geometry-based elliptical model to improve the accuracy of device-free localization (DFL). Different from the traditional elliptical model in [2], weights are different in the different areas in their model.…”

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

“…The dimension of the collected RSS measurements is smaller than the number of pixels. Several approaches have explored the sparse nature of the presence of objects in the sensing area, and seek a variety of optimization strategies for finding a unique solution [29,30,40]. However, it is obviously reasonable to assume that if a static subject or a dynamic interest target in the environment occupies a single or several pixels, the excessive image resolution may result in confused decisions when extracting multiple targets' locations.…”

“…It has been found that the recovery of attenuation image x is an ill-posed inverse problem, and the regularization methods need to be introduced to solve this problem [21,31]. The existing imaging-based localization methods using the ellipse model and its improved model have been proven to have good performance in the outdoor environment [16,17,29,30], but they have two limitations. Firstly, the elliptical sampling model is dependent on the spatial coordinates of the transceiver nodes.…”

“…The ellipse model-based methods make great efforts to build the refined spatial sampling model of each link and use the regularization algorithms to resolve ill-posed image reconstruction [16,17,21,[29][30][31]. The RSS fingerprint-based approaches seek to model the fading feature of the subject at each predefined location, and the localization is cast into a pattern-matching problem [32][33][34].…”