基于UKF-GP的长期封闭期间对非合作空间物体的运动和惯性估计
Rabiul Hasan Kabir1, Xiaoli Bai1
1Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854, USA.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究使用高斯过程 (GP) 和无气卡尔曼波器 (UKF-GP) 估计掉落的空间物体的运动和惯性. 这种方法在长时间的传感器数据间隙中准确地跟踪物体,优于传统的UKF算法.
科学领域:
- 航天器的动态和控制控制.
- 机器人技术和自主系统
- 机器学习用于航空航天应用.
背景情况:
- 估计非合作空间物体的运动和惯性对于碎片清除和维修至关重要.
- 长期的传感器封闭对传统估计算法构成重大挑战.
- 数据驱动的方法越来越多地被探索,以克服模拟复杂动态的局限性.
研究的目的:
- 开发一种可靠的方法来估计颠倒空间物体的运动和惯性参数,即使存在长期遮蔽.
- 利用高斯过程 (GP) 来模拟传感器测量和处理非周期数据中的周期趋势.
- 提出和验证一个融合算法,将无气味卡尔曼波器和高斯过程 (UKF-GP) 结合起来,以提高估计准确度.
主要方法:
- 使用多输出高斯过程 (GP) 与产品内核 (两个周期内核) 来预测立体摄像头投影测量.
- 采用快速里叶变换 (FFT) 分析来得出GP周期性的初始超参数猜测.
- 开发了一个无气味的卡尔曼波器-高斯过程 (UKF-GP) 融合算法,使用GP预测作为遮蔽期间的伪测量.
主要成果:
- UKF-GP算法证明了对长时间 (数百秒) 的目标运动变量的准确估计.
- 通过蒙特卡洛 (MC) 模拟,在不同翻转频率上验证了性能.
- 拟议的方法在处理长期闭塞方面明显优于传统的UKF算法.
结论:
- UKF-GP 聚变算法提供了一个强大的解决方案,用于在具有挑战性的阻塞条件下估计倒的空间物体的运动和惯性参数.
- 高斯过程有效地建模和预测传感器数据,增强估计能力.
- 这种方法为涉及太空中不合作目标的自主操作提供了显著的进步.
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