Minimum Error Entropy Rauch-Tung-Striebel Smoother

TL;DR

This paper introduces a robust RTS smoother based on the minimum error entropy criterion, improving state estimation accuracy in systems affected by non-Gaussian noise and outliers.

Contribution

A novel MEE-RTS smoother is developed for linear and nonlinear systems, enhancing robustness against heavy-tailed noise and impulsive disturbances.

Findings

Outperforms existing robust smoothers in steady-state error

Effective in handling non-Gaussian noise and outliers

Analyzed computational complexity and error behavior

Abstract

Outliers and impulsive disturbances often cause heavy-tailed distributions in practical applications, and these will degrade the performance of Gaussian approximation smoothing algorithms. To improve the robustness of the Rauch-Tung-Striebel (RTS) smother against complicated non-Gaussian noises, a new RTS-smoother integrated with the minimum error entropy (MEE) criterion (MEE-RTS) is proposed for linear systems, which is also extended to the state estimation of nonlinear systems by utilizing the Taylor series linearization approach. The mean error behavior, the mean square error behavior, as well as the computational complexity of the MEE-RTS smoother are analyzed. According to simulation results, the proposed smoothers perform better than several robust solutions in terms of steady-state error.