Target Tracking in Confined Environments with Uncertain Sensor Positions

TL;DR

This paper introduces a novel method for simultaneous sensor position refinement and target tracking in confined environments with uncertain sensor locations, using belief propagation and simulation validation.

Contribution

It presents an automatic approach combining belief propagation with dynamic and measurement models to improve tracking accuracy despite sensor position uncertainties.

Findings

Outperforms standard Bayesian tracking algorithms

Provides robustness against measurement outliers

Effective in simulated mine-like environments

Abstract

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.