Attitude Estimation with Feedback Particle Filter

TL;DR

This paper develops and compares a feedback particle filter algorithm for attitude estimation on SO(3), demonstrating its effectiveness through simulations and contrasting it with existing methods like EKF and UKF.

Contribution

It extends the feedback particle filter to attitude estimation on SO(3) using quaternions and provides detailed algorithmic and comparative analysis.

Findings

FPF performs competitively with traditional filters.

Two numerical methods effectively solve the filter gain function.

Simulation results validate the proposed approach.

Abstract

This paper presents theory, application, and comparisons of the feedback particle filter (FPF) algorithm for the problem of attitude estimation. The paper builds upon our recent work on the exact FPF solution of the continuous-time nonlinear filtering problem on compact Lie groups. In this paper, the details of the FPF algorithm are presented for the problem of attitude estimation - a nonlinear filtering problem on SO(3). The quaternions are employed for computational purposes. The algorithm requires a numerical solution of the filter gain function, and two methods are applied for this purpose. Comparisons are also provided between the FPF and some popular algorithms for attitude estimation on SO(3), including the invariant EKF, the multiplicative EKF, and the unscented Kalman filter. Simulation results are presented that help illustrate the comparisons.