Robust Attitude Estimation with Quaternion Left-Invariant EKF and Noise Covariance Tuning

TL;DR

This paper introduces a quaternion left-invariant EKF with adaptive noise covariance estimation using EM, significantly improving attitude estimation accuracy and robustness in systems with unknown or changing noise parameters.

Contribution

The novel integration of an adaptive noise covariance estimation algorithm with the quaternion left-invariant EKF enhances its robustness and accuracy in attitude estimation under uncertain noise conditions.

Findings

Outperforms standard EKF in accuracy and robustness

Effectively estimates time-varying noise covariances

Demonstrates superior performance in simulations

Abstract

Accurate estimation of noise parameters is critical for optimal filter performance, especially in systems where true noise parameter values are unknown or time-varying. This article presents a quaternion left-invariant extended Kalman filter (LI-EKF) for attitude estimation, integrated with an adaptive noise covariance estimation algorithm. By employing an iterative expectation-maximization (EM) approach, the filter can effectively estimate both process and measurement noise covariances. Extensive simulations demonstrate the superiority of the proposed method in terms of attitude estimation accuracy and robustness to initial parameter misspecification. The adaptive LI-EKF's ability to adapt to time-varying noise characteristics makes it a promising solution for various applications requiring reliable attitude estimation, such as aerospace, robotics, and autonomous systems.