Nonlinear Deterministic Filter for Inertial Navigation and Bias Estimation with Guaranteed Performance

TL;DR

This paper introduces a nonlinear deterministic filter for inertial navigation that guarantees convergence of attitude, position, and velocity estimates, demonstrating robustness through experimental validation.

Contribution

The paper proposes a novel nonlinear filter on the Lie Group _{2}(3) that guarantees systematic convergence from nearly any initial condition and asymptotic error reduction.

Findings

Filter ensures systematic convergence of errors

Errors asymptotically approach zero

Experimental results validate robustness

Abstract

Unmanned vehicle navigation concerns estimating attitude, position, and linear velocity of the vehicle the six degrees of freedom (6 DoF). It has been known that the true navigation dynamics are highly nonlinear modeled on the Lie Group of $\mathbb{SE}_{2}(3)$. In this paper, a nonlinear filter for inertial navigation is proposed. The filter ensures systematic convergence of the error components starting from almost any initial condition. Also, the errors converge asymptotically to the origin. Experimental results validates the robustness of the proposed filter.