The Invariant Zonotopic Set-Membership Filter for State Estimation on Groups

TL;DR

This paper introduces an Invariant Zonotopic Set-Membership Filter (InZSMF) for state estimation on Lie groups, extending invariant filtering to set-membership filtering with unknown bounded noise, and demonstrates its superior performance over traditional methods.

Contribution

It proposes a novel InZSMF method on groups that extends invariant filtering theory to set-membership filtering with unknown bounded noise disturbances.

Findings

InZSMF outperforms traditional ZSMF in accuracy and convergence speed.

InZSMF maintains better estimation accuracy with imprecise initial estimates.

Simulation results confirm the effectiveness of the proposed method.

Abstract

The invariant filtering theory based on the group theory has been successful in statistical filtering methods. However, there exists a class of state estimation problems with unknown statistical properties of noise disturbances, and it is worth discussing whether the invariant observer still has performance advantages. In this paper, considering the problem of state estimation with unknown but bounded noise disturbances, an Invariant Zonotopic Set-Membership Filter (InZSMF) method on groups is innovatively proposed, which extends the invariant filtering theory to the field of non-statistical filtering represented by set-membership filtering. Firstly, the InZSMF method transforms the state space from the traditional Euclidean vector space to the Lie group space to construct group affine discrete systems with unknown but bounded noise uncertainty defined by the zonotope on groups. Secondly, the nonlinear observer on the group is defined and the corresponding linearized estimation error is derived. Then, two observer gain tuning algorithms under the InZSMF method are proposed, respectively, the pole configuration method and the F-radius optimization method. Finally, through simulation experiments, it is shown that the InZSMF state estimation method is generally superior to the traditional Zonotopic Set-Membership Filter (ZSMF) state estimation method. Especially, when the initial estimations are imprecise, the convergence speed of state estimation, the accuracy of set-membership center estimation, and the average interval area of zonotopic estimation of the InZSMF method are significantly better than those of the ZSMF method.