Generalized Nonlinear Complementary Attitude Filter

TL;DR

This paper introduces a generalized family of attitude estimators that unify nonlinear complementary filters and extended Kalman filters, providing insights into stability and gain selection.

Contribution

It presents a unified framework linking nonlinear complementary filters with extended Kalman filters, enhancing understanding and stability analysis.

Findings

Special cases of the filter are proven to be near globally asymptotically stable.

The framework offers a rational method for gain selection.

The relationship between different filters is mathematically clarified.

Abstract

This work describes a family of attitude estimators that are based on a generalization of Mahony's nonlinear complementary filter. This generalization reveals the close mathematical relationship between the nonlinear complementary filter and the more traditional multiplicative extended Kalman filter. In fact, the bias-free and constant gain multiplicative continuous-time extended Kalman filters may be interpreted as special cases of the generalized attitude estimator. The correspondence provides a rational means of choosing the gains for the nonlinear complementary filter and a proof of the near global asymptotic stability of special cases of the multiplicative extended Kalman filter.