面向卫星中基于主动激励的动态惯性辨识
Towards Active Excitation-Based Dynamic Inertia Identification in Satellites
AI总结：
针对纳卫星与微卫星在轨惯性辨识，本文仿真非线性姿态动力学，比较八种频谱丰富度的转矩激励以及最小二乘法和扩展卡尔曼滤波，揭示激励频率与估计器假设对精度和鲁棒性的共同影响。
摘要：
本文全面分析了激励设计如何影响刚性纳卫星和微卫星惯性特性的辨识。我们仿真了包含反作用轮耦合、执行器限制和外部扰动的非线性姿态动力学，并采用八种具有不同频谱丰富度的转矩剖面激励系统。研究在三种卫星构型和时变惯性场景下比较了两种估计器：批量最小二乘法和扩展卡尔曼滤波。结果表明，激励频率成分与估计器假设共同决定估计精度和鲁棒性；通过概述每种方法表现最佳的条件，为在轨自适应惯性辨识提供了实用指导。代码以开源形式提供。
英文摘要：
This paper presents a comprehensive analysis of how excitation design influences the identification of the inertia properties of rigid nano- and micro-satellites. We simulate nonlinear attitude dynamics with reaction-wheel coupling, actuator limits, and external disturbances, and excite the system using eight torque profiles of varying spectral richness. Two estimators are compared, a batch Least Squares method and an Extended Kalman Filter, across three satellite configurations and time-varying inertia scenarios. Results show that excitation frequency content and estimator assumptions jointly determine estimation accuracy and robustness, offering practical guidance for in-orbit adaptive inertia identification by outlining the conditions under which each method performs best. The code is provided as open-source .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
