Distributed Estimation for a 3-D Moving Target in Quaternion Space with Unknown Correlation

TL;DR

This paper extends the Inverse Covariance Intersection algorithm to 3-D quaternion space for distributed estimation of moving targets, improving accuracy and robustness in sensor networks with unknown correlations.

Contribution

It introduces a fully distributed ICI-based estimation method in quaternion space for 3-D target tracking, addressing correlation uncertainty and network topology changes.

Findings

Effective in 3-D pose estimation in sensor networks

Robust to time-varying communication topology

Validated through extensive Monte Carlo simulations

Abstract

For distributed estimations in a sensor network, the consistency and accuracy of an estimator are greatly affected by the unknown correlations between individual estimates. An inconsistent or too conservative estimate may degrade the estimation performance and even cause divergence of the estimator. Cooperative estimation methods based on Inverse Covariance Intersection (ICI) can utilize a network of sensors to provide a consistent and tight estimate of a target. In this paper, unlike most existing ICI-based estimators that only consider two-dimensional (2-D) target state estimation in the vector space, we address this problem in a 3-D environment by extending the ICI algorithm to the augmented quaternion space. In addition, the proposed algorithm is fully distributed, as each agent only uses the local information from itself and its communication neighbors, which is also robust to a time-varying communication topology. To evaluate the performance, we test the proposed algorithm in a camera network to track the pose of a target. Extensive Monte Carlo simulations have been performed to show the effectiveness of our approach.