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基于事件触发学习的航天器定轨算法

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针对通信和计算资源受限的航天器实时定轨问题,提出一种基于事件触发学习的无迹卡尔曼滤波航天器定轨算法.以无迹卡尔曼滤波方法为基础,设计随机事件触发机制驱动的自适应学习算法,实现有限系统动态学习频率下的实时定轨,在保证定轨精度的同时降低地面站与航天器双向通信次数,有效节省了航天器通信与计算资源.
Spacecraft Orbit Determination Based on Event-triggered Learning
For the problem of real-time orbit determination problems of spacecraft with limited communication and computation resources,an orbit determination algorithm of unscented Kalman filter(ELUKF)spacecraft based on event-triggered learning is proposed.Based on the unscented Kalman filter method,an adaptive learning algorithm driven by a stochastic event-triggering mechanism is designed to achieve real-time orbit determination under dynamic learning frequency of finite system.The proposed method can efficiently reduce the bidirectional communication times between the ground station and the spacecraft while ensuring the orbit determination accuracy,the communication and computation resources of the spacecraft are effectively saved.

orbit determination of spacecraftevent-triggered learningstate estimationunscented Kalman filteradaptive filtering

史大威、李双汐、邹恒光、张磊、王军政

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北京理工大学自动化学院,北京 100081

中国空间技术研究院,北京 100094

航天器定轨 事件触发学习 状态估计 无迹卡尔曼滤波 自适应滤波

2024

指挥与控制学报

指挥与控制学报

CSTPCD北大核心
ISSN:
年,卷(期):2024.10(2)
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