Unscented Kalman Filter with a Nonlinear Propagation Model for Navigation Applications

TL;DR

This paper introduces a novel sigma point propagation method for the unscented Kalman filter that enhances navigation accuracy in autonomous underwater vehicles by better modeling nonlinear dynamics.

Contribution

The paper presents an innovative sigma point propagation technique tailored for nonlinear navigation models, improving filter stability and accuracy.

Findings

Enhanced navigation accuracy demonstrated with real sensor data

Improved filter stability in nonlinear dynamic scenarios

Better modeling of navigation error dynamics

Abstract

The unscented Kalman filter is a nonlinear estimation algorithm commonly used in navigation applications. The prediction of the mean and covariance matrix is crucial to the stable behavior of the filter. This prediction is done by propagating the sigma points according to the dynamic model at hand. In this paper, we introduce an innovative method to propagate the sigma points according to the nonlinear dynamic model of the navigation error state vector. This improves the filter accuracy and navigation performance. We demonstrate the benefits of our proposed approach using real sensor data recorded by an autonomous underwater vehicle during several scenarios.