A Lie-Theoretic Approach to Propagating Uncertainty Jointly in Attitude and Angular Momentum

TL;DR

This paper introduces a Lie-theoretic framework for joint dynamic state estimation of attitude and angular momentum, providing non-parametric propagation equations and demonstrating improved accuracy over traditional methods.

Contribution

It reformulates dynamic state estimation on Lie groups, deriving non-parametric propagation equations for mean and covariance, and shows their effectiveness through numerical experiments.

Findings

Distribution fits sample data better than extended Kalman filter

Propagation equations derived non-parametrically for mean and covariance

Approximate solutions ignore higher moments effectively

Abstract

Dynamic state estimation, as opposed to kinematic state estimation, seeks to estimate not only the orientation of a rigid body but also its angular velocity, through Euler's equations of rotational motion. This paper demonstrates that the dynamic state estimation problem can be reformulated as estimating a probability distribution on a Lie group defined on phase space (the product space of rotation and angular momentum). The propagation equations are derived non-parametrically for the mean and covariance of the distribution. It is also shown that the equations can be approximately solved by ignoring the third and higher moments of the probability distribution. Numerical experiments show that the distribution constructed from the propagated mean and covariance fits the sample data better than an extended Kalman filter.