自主水下及水面航行器准静态惯性导航系统对准的非正交性约束优化应用
On the Optimized Use of Non-Orthonormality Constraints for the Quasi-Static INS Alignment of Autonomous Underwater and Surface Vehicles
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中文总结 AI 辅助
本文针对自主水下及水面航行器惯性导航系统对准的传统方法收敛慢、偏差可估计性有限的问题，优化TRIAD-CBE粗对准方法并提出带NON误差约束的精对准EKF观测模型，使收敛速度从分钟级提升至秒级且精度相当。
中文摘要 AI 辅助
惯性导航系统是几乎所有自主水下及水面航行器配备的专用导航设备，其需要精确的初始对准，即确定初始姿态，该过程通常在准静态条件下（尽可能实现）分两个阶段完成：粗对准（CA）采用三轴姿态确定（TRIAD）等方法，精对准（FA）采用基于零速度更新（ZVU）的扩展卡尔曼滤波（EKF）。但传统方法存在收敛慢、偏差可估计性有限的问题。针对该问题，本文提出：（a）近期提出的粗对准方法——带粗偏差估计的三轴姿态确定（TRIAD-CBE）的优化版本；（b）一种新型精对准EKF观测模型，其整合了源自TRIAD的非正交性（NON）误差约束，将这些误差与惯性传感器偏差直接关联。通过大量蒙特卡洛（MC）仿真及使用两个不同等级惯性测量单元（IMU）的真实实验验证，本文方法大幅加快了失准角及偏差估计的收敛速度（从分钟级提升至秒级），同时保持了与传统技术相当的精度与准确度。
英文摘要
Inertial navigation systems are specialized navigation apparatuses that equip almost all autonomous underwater and surface vehicles. They require precise initial alignment, i.e., determination of their initial attitude, which is typically achieved: (a) in quasi-static conditions (whenever possible); and (b) in two stages: Coarse Alignment (CA), using methods like TRI-axis Attitude Determination (TRIAD), and Fine Alignment (FA), via Zero Velocity Update (ZVU)-based Extended Kalman Filtering (EKF). However, conventional methods suffer from slow convergence and limited bias estimability. In response, this paper introduces: (a) an optimized version of a recently proposed CA method, namely, TRIAD with Coarse Bias Estimation (TRIAD-CBE); and (b) a novel FA EKF observation model that incorporates Non-Orthonormality (NON) error constraints derived from TRIAD, directly linking these errors to the inertial sensor biases. As validated through extensive Monte Carlo (MC) simulations, as well as real-world experiments using two Inertial Measurement Units (IMUs) of different grades, our approaches substantially accelerate the convergence of misalignment and bias estimates (from minutes to seconds), while maintaining accuracy/precision comparable to traditional techniques.
发表机构
- Federal University of Lavras（拉夫拉斯联邦大学）
- University of Haifa（海法大学）
- Fluminense Federal Institute of Education, Science and Technology（弗卢米嫩塞联邦教育科学技术学院）
- Federal Institute of Education, Science and Technology of Amazonas（亚马逊联邦教育科学技术学院）
- University of São Paulo（圣保罗大学）
- Zoox, Inc.（Zoox公司）
机构由 AI 辅助整理，请以论文原文为准。
