AI 中文总结
该研究提出纯姿态框架，利用无扭矩运动数据估计航天器归一化惯性张量，方法无需陀螺仪与已知控制扭矩，经测试精度优于扩展卡尔曼滤波器且计算量更小，光近距离操作模拟验证了其有效性。
AI 中文摘要
我们提出一种纯姿态框架，用于从无扭矩旋转运动中估计航天器的归一化惯性张量。该方法支持连续单弧观测以及多个短无扭矩弧的联合使用，既不需要陀螺仪测量，也不需要已知的控制扭矩。Karush-Kuhn-Tucker公式提供快速线性初始化，通过使用欧拉方程的精确雅可比-椭圆解和Magnus展开四元数映射的非线性射击法进行优化。在受控姿态噪声下，使用单个500秒弧的测试表明，与从相同估计初始化的扩展卡尔曼滤波器（EKF）相比，惯性张量误差降低了约一个数量级，同时计算量减少了近两个数量级。从三个100秒弧的联合估计实现了相似的精度提升，且速度仍快一个数量级以上。基于单目图像获取姿态的光近距离操作模拟进一步评估了两种策略：2000秒单弧案例实现了千分之一以下的中值惯性张量误差，并支持10小时的姿态预测，中值误差为个位数度；在每个弧为30-300秒的三弧案例中，该方法始终优于EKF优化，性能受旋转激励和时间采样的控制。
英文摘要
We present an attitude-only framework for estimating a spacecraft's normalized inertia tensor from torque-free rotational motion. Our method supports both continuous single-arc observations and the joint use of multiple short torque-free arcs, while requiring neither gyroscope measurements nor known control torques. A Karush-Kuhn-Tucker formulation provides a fast linear initialization, which is refined by nonlinear shooting using the exact Jacobi-elliptic solution of Euler's equations and a Magnus-expansion quaternion map. Under controlled attitude noise, tests using a single 500-second arc reduced inertia-tensor error by approximately one order of magnitude relative to an Extended Kalman Filter initialized from the same estimate, while requiring nearly two orders of magnitude less computation. Joint estimation from three 100-second arcs provided a similar improvement in accuracy and remained more than one order of magnitude faster. Photorealistic proximity-operations simulations further evaluated both strategies using monocular image-derived attitudes. The 2000-second single-arc cases achieved sub-thousandth median inertia-tensor error and supported 10-hour attitude predictions with single-digit-degree median error. In three-arc cases using 30-300 seconds per arc, our method consistently outperformed the EKF refinement, with performance governed by rotational excitation and temporal sampling.
Comments34 pages, 12 figures
