On Geometric Upper Bounds for Positioning Algorithms in Wireless Sensor Networks

TL;DR

This paper introduces geometric upper bounds for position error in wireless sensor networks, especially under biased distance measurements, by formulating and relaxing nonconvex optimization problems to derive practical bounds.

Contribution

It proposes two novel geometric upper bounds for positioning error based on feasible sets, addressing bias issues in range-based localization.

Findings

Bounds are reasonably tight in simulations

Bounds can be efficiently computed via convex relaxation

Applicable in non-line-of-sight conditions

Abstract

This paper studies the possibility of upper bounding the position error of an estimate for range based positioning algorithms in wireless sensor networks. In this study, we argue that in certain situations when the measured distances between sensor nodes are positively biased, e.g., in non-line-of-sight conditions, the target node is confined to a closed bounded convex set (a feasible set) which can be derived from the measurements. Then, we formulate two classes of geometric upper bounds with respect to the feasible set. If an estimate is available, either feasible or infeasible, the worst-case position error can be defined as the maximum distance between the estimate and any point in the feasible set (the first bound). Alternatively, if an estimate given by a positioning algorithm is always feasible, we propose to get the maximum length of the feasible set as the worst-case position error (the second bound). These bounds are formulated as nonconvex optimization problems. To progress, we relax the nonconvex problems and obtain convex problems, which can be efficiently solved. Simulation results indicate that the proposed bounds are reasonably tight in many situations.