Investigation of Enhanced Inertial Navigation Algorithms by Functional Iteration

TL;DR

This paper evaluates traditional and enhanced inertial navigation algorithms using a novel functional iteration approach, demonstrating improved accuracy in attitude computation but limited benefits in full navigation scenarios.

Contribution

It introduces a functional iteration method for analytical accuracy evaluation of inertial navigation algorithms, proving convergence and highlighting error reduction in attitude algorithms.

Findings

Enhanced attitude algorithms significantly reduce error orders.

Impact of enhanced velocity algorithms on error reduction is minimal.

Functional iteration approach offers superior accuracy even in low dynamic conditions.

Abstract

The defects of the traditional strapdown inertial navigation algorithms become well acknowledged and the corresponding enhanced algorithms have been quite recently proposed trying to mitigate both theoretical and algorithmic defects. In this paper, the analytical accuracy evaluation of both the traditional algorithms and the enhanced algorithms is investigated, against the true reference for the first time enabled by the functional iteration approach having provable convergence. The analyses by the help of MATLAB Symbolic Toolbox show that the resultant error orders of all algorithms under investigation are consistent with those in the existing literatures, and the enhanced attitude algorithm notably reduces error orders of the traditional counterpart, while the impact of the enhanced velocity algorithm on error order reduction is insignificant. Simulation results agree with analyses that the superiority of the enhanced algorithm over the traditional one in the body-frame attitude computation scenario diminishes significantly in the entire inertial navigation computation scenario, while the functional iteration approach possesses significant accuracy superiority even under sustained lowly dynamic conditions.