arxiv:2109.08654

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

Published on Sep 17, 2021

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Abstract

A nonlinear filter for inertial navigation ensures robust and systematic convergence of error components from initial conditions and asymptotic convergence to zero.

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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 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.

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