A Novel Bayesian Filter for RSS-Based Device-Free Localization and Tracking

Abstract

Received signal strength based device-free localization applications utilize a model that relates the measurements to position of the wireless sensors and person, and the underlying inverse problem is solved either using an imaging method or a nonlinear Bayesian filter. In this paper, it is shown that Bayesian filters nearly reach the posterior Cramér-Rao bound and they are superior with respect to imaging approaches in terms of localization accuracy because the measurements are directly related to position of the person. However, Bayesian filters are known to suffer from divergence issues and in this paper, the problem is addressed by introducing a novel Bayesian filter. The developed filter augments the measurement model of a Bayesian filter with position estimates from an imaging approach. This bounds the filter's measurement residuals by the position errors of the imaging approach and as an outcome, the developed filter has robustness of an imaging method and tracking accuracy of a Bayesian filter. The filter is demonstrated to achieve a localization error of 0.11 m in a 75 m 2 open indoor deployment and an error of 0.29 m in a 82 m 2 apartment experiment, decreasing the localization error by 30 − 48 % with respect to a state-of-the-art imaging method.

Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

“…In this regard, radio frequency (RF) sensing is particularly suitable for realizing AAL systems. The main advantages of the technology are four-fold: it is passive and does not require users to carry obtrusive and uncomfortable sensors [ 2 ]; it is suitable for a wide range of monitoring purposes such as localization and tracking [ 3 ], fall detection [ 4 ], vital sign monitoring [ 5 ], gesture recognition [ 6 ] and behavioral sensing [ 7 ]; RF signals can penetrate walls, clutter and other occlusions, unlike many other sensors that have a limited field of view [ 8 ]; and it is privacy preserving which increases acceptance of the monitoring technology, unlike vision-based systems that are intrusive [ 9 ]. …”

“…it is suitable for a wide range of monitoring purposes such as localization and tracking [ 3 ], fall detection [ 4 ], vital sign monitoring [ 5 ], gesture recognition [ 6 ] and behavioral sensing [ 7 ];…”

“…It is demonstrated that the EM algorithm not only improves the accuracy of the introduced system, but also other DFLT systems. The experiments conducted in this paper, together with Matlab code to run the presented filtering, smoothing and EM algorithms are made publicly available and are published in [ 21 ]. The aim is to lower the threshold to start research in the area and advance the field of DFLT in general.…”

Noise Adaptive Optimization Scheme for Robust Radio Tomographic Imaging Based on Sparse Bayesian Learning

“…R ADIO tomographic imaging (RTI) [1], [2] is an emerging technology for localizing the person without carrying any tags in low-cost radio frequency (RF) sensor networks. It has attracted much attention for decades due to certain distinct advantages [3]- [7]. RTI relies on the received signal strength (RSS) measurements of narrow-band radio links, thus it is capable of working in obstructed environments.…”

Voronoi diagram with fuzzy number and sensor data in an indoor navigation for emergency situation

“…The latter focuses on the navigation models which study the optimal waypoints to the nearest or the fastest ways to the safe exits. This work normally integrates intelligent systems such as Bayesian filter [7][8][9] or fuzzy to obtain the best solution for specific conditions.…”