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Unmanned vehicle navigation concerns estimating attitude, position, andlinear velocity of the vehicle the six degrees of freedom (6 DoF). It has beenknown that the true navigation dynamics are highly nonlinear modeled on the LieGroup of $\mathbb{SE}_{2}(3)$. In this paper, a nonlinear filter for inertialnavigation is proposed. The filter ensures systematic convergence of the errorcomponents starting from almost any initial condition. Also, the errorsconverge asymptotically to the origin. Experimental results validates therobustness of the proposed filter.

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