RSSI-Based Self-Localization with Perturbed Anchor Positions

TL;DR

This paper introduces a closed-form weighted least-squares method for self-localization using RSSI measurements with perturbed anchor positions, improving accuracy in resource-limited environments.

Contribution

It develops a novel closed-form solution that accounts for perturbations in both RSSI and anchor positions, outperforming existing iterative methods.

Findings

Significant reduction in localization error and bias.

Performance approaches the Cramer-Rao lower bound.

Effective in arbitrary network topologies.

Abstract

We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.