Particle Filtering for Attitude Estimation Using a Minimal Local-Error Representation: A Revisit

TL;DR

This paper revisits quaternion particle filtering for attitude estimation, highlighting limitations in previous approaches and proposing a normalized quaternion determination method to improve accuracy and robustness.

Contribution

It introduces a normalized quaternion procedure based on minimum mean-square error to enhance quaternion particle filtering methods.

Findings

Improved accuracy in attitude estimation.

Enhanced robustness of the filtering process.

More strict quaternion transformation approach.

Abstract

In this note, we have revisited the previously published paper "Particle Filtering for Attitude Estimation Using a Minimal Local-Error Representation". In the revisit, we point out that the quaternion particle filtering based on the local/global representation structure has not made full use of the advantage of the particle filtering in terms of accuracy and robustness. Moreover, a normalized quaternion determining procedure based on the minimum mean-square error approach has been investigated into the quaternion-based particle filtering to obtain the fiducial quaternion for the transformation between quaternion and modified Rodrigues parameter. The modification investigated in this note is expected to make the quaternion particle filtering based on the local/global representation structure more strict.