Informative Planning of Mobile Sensor Networks in GPS-Denied Environments

TL;DR

This paper introduces a novel algorithm for planning mobile sensor networks in GPS-denied environments, combining state estimation and uncertainty-aware planning without external sensors or prior environmental knowledge.

Contribution

It proposes a new method that estimates sensor and target states simultaneously and plans sensor deployment considering uncertainties, addressing limitations of previous approaches in GPS-denied settings.

Findings

The proposed algorithm improves target localization accuracy.

Monte Carlo experiments demonstrate superior performance over prior methods.

The approach does not require additional sensors or environmental data.

Abstract

This paper considers the problem to plan mobile sensor networks for target localization task in GPS-denied environments. Most researches on mobile sensor networks assume that the states of the sensing agents are precisely known during their missions, which is not feasible under the absence of external infrastructures such as GPS. Thus, we propose a new algorithm to solve this problem by: (i) estimating the states of the sensing agents in addition to the target's through the combination of a particle filter (PF) and extended Kalman filters (EKF) and (ii) involving the uncertainty of the states of the sensing agents in planning the sensor networks based on the combined filters. This approach does not require any additional internal/external sensors nor the prior knowledge of the surrounding environments. We demonstrate the limitations of prior works in GPS-denied environments and the improvements from the proposed algorithm through Monte Carlo experiments.