改进惯导初始对准，让水下无人艇更快更准定位。
On the Optimized Use of Non-Orthonormality Constraints for the Quasi-Static INS Alignment of Autonomous Underwater and Surface Vehicles
- 用非正交误差约束优化粗对准与精对准流程。
- 收敛时间从分钟级缩短至秒级，精度不降。
- 适合高动态环境下需快速初始化的自主水下/水面航行器。
惯性导航系统是几乎所有自主水下和水面航行器的核心组件，其性能依赖于精确的初始对准，即确定初始姿态。通常在准静态条件下分两阶段完成：粗对准（CA）采用三角姿态确定法（TRIAD），精对准（FA）则基于零速度更新（ZVU）的扩展卡尔曼滤波（EKF）。然而传统方法存在收敛慢、偏差估计能力有限的问题。本文提出：(a) 一种优化的新型粗对准方法——带粗略偏差估计的三角法（TRIAD-CBE）；(b) 一种创新的精对准EKF观测模型，直接引入由TRIAD获得的非正交性（NON）误差约束，并将其与惯性传感器偏差关联。通过大量蒙特卡洛仿真及两种不同等级惯性测量单元（IMU）的真实实验验证，所提方法显著加快了姿态与偏差估计的收敛速度（从分钟级降至秒级），同时保持与传统方法相当的精度与准确性。
原文摘要 · Abstract (English)
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.
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