Belief Propagation-based Rotation and Translation Estimation for Rigid Body Localization

TL;DR

This paper introduces a belief propagation-based method for estimating 3D rotation and translation in rigid body localization using only range measurements, outperforming existing techniques.

Contribution

It develops a novel Gaussian belief propagation framework for direct estimation of rotation and translation from range data, with interference cancellation for improved accuracy.

Findings

Superior accuracy over state-of-the-art methods

Effective estimation of 3D rotation and translation

Validated through numerical simulations

Abstract

We propose a novel solution to the rigid body localization (RBL) problem, in which the three-dimensional (3D) rotation and translation is estimated by only utilizing the range measurements between the wireless sensors on the rigid body and the anchor sensors. The proposed framework first constructs a linear Gaussian belief propagation (GaBP) algorithm to estimate the absolute sensor positions utilizing the range-based received signal model, which is used for the reconstruction of the RBL transformation model, linearized with a small-angle approximation. In light of the reformulated system, a second bivariate GaBP is designed to directly estimate the 3D rotation angles and translation distances, with an interference cancellation (IC) refinement to improve the angle estimation performance. The effectiveness of the proposed method is verified via numerical simulations, highlighting the superior performance of the proposed method against the state-of-the-art (SotA) techniques for the position, rotation, and translation estimation performance.