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.

翻译：传统捷联惯性导航算法的缺陷已广为人知，为此近年来提出了相应的增强算法，旨在从理论和算法层面弥补这些缺陷。本文首次借助具有可证明收敛性的功能迭代方法，以真实参考值为基准，对传统算法和增强算法进行了解析精度评估。借助MATLAB符号工具箱的分析表明，所有受评算法的误差阶数与现有文献一致，其中增强姿态算法显著降低了传统算法的误差阶数，而增强速度算法对误差阶数降低的影响甚微。仿真结果与分析一致：在体坐标系姿态解算场景中，增强算法相比传统算法的优越性在全惯性导航解算场景中显著减弱，而功能迭代方法即使在持续低动态条件下仍具有显著的精度优势。