王密, 陈俊博, 皮英冬, 仵倩玉. 一种面向卫星在轨自主任务规划的快速精准轨道预报方法[J]. 武汉大学学报 ( 信息科学版), 2024, 49(6): 879-887. DOI: 10.13203/j.whugis20230223
引用本文: 王密, 陈俊博, 皮英冬, 仵倩玉. 一种面向卫星在轨自主任务规划的快速精准轨道预报方法[J]. 武汉大学学报 ( 信息科学版), 2024, 49(6): 879-887. DOI: 10.13203/j.whugis20230223
WANG Mi, CHEN Junbo, PI Yingdong, WU Qianyu. A Fast and Accurate Orbit Prediction Method for Satellite On-Orbit Autonomous Mission Planning[J]. Geomatics and Information Science of Wuhan University, 2024, 49(6): 879-887. DOI: 10.13203/j.whugis20230223
Citation: WANG Mi, CHEN Junbo, PI Yingdong, WU Qianyu. A Fast and Accurate Orbit Prediction Method for Satellite On-Orbit Autonomous Mission Planning[J]. Geomatics and Information Science of Wuhan University, 2024, 49(6): 879-887. DOI: 10.13203/j.whugis20230223

一种面向卫星在轨自主任务规划的快速精准轨道预报方法

A Fast and Accurate Orbit Prediction Method for Satellite On-Orbit Autonomous Mission Planning

  • 摘要: 卫星在轨自主任务规划需要高精度的轨道预报数据,计算待观测目标的可成像时间窗口、成像姿态需求,结合其他约束条件来完成任务规划过程。针对卫星星上计算资源有限的情况,为了减少轨道预报过程的资源消耗、满足自主任务规划的需求,提出一种在长周期的轨道预报中预报精度高、资源消耗少的轨道预报方法。基于两行根数(two-line element, TLE)及简化普适摄动模型,以多条轨道数据作为输入,迭代计算生成TLE,完成轨道预报,并计算轨道预报误差和模拟观测目标的成像参数初值误差,进行定量评价。实验结果表明,所提方法仅需少量输入数据即可完成72 h的轨道预报,预报精度优于4 km,平均耗时12.76 s,在长周期的轨道预报中预报精度高、运行速度快;使用轨道预报数据计算模拟观测目标的开始成像时间误差优于0.2 s,三轴方向上的成像姿态初值误差均优于0.03°,小于智能遥感卫星珞珈三号01星相机成像过程姿态指向精度指标。所提方法预报精度高,需求的时空资源少,对于星上在轨自主任务规划具有重要意义。

     

    Abstract:
    Objectives Satellite on-orbit autonomous mission planning requires high-precision orbit prediction data to calculate the feasible imaging time windows and imaging posture requirements for the intended observation targets. This process involves integrating these factors with other constraint conditions to accomplish the mission planning.
    Methods In the context of limited computational resources onboard satellites, this paper proposes a novel method to reduce resource consumption in orbit prediction process and meet the demands of autonomous mission planning. First, based on two-line element (TLE) and a simplified general perturbation model, the proposed method utilizes multiple sets of orbital data as input and introduces a new measurement metric. Then, it iteratively generates TLE by rationally setting data weights and progressively adjusting them to get the results of orbit prediction. Finally, the total error for orbit prediction and the initial value error of imaging parameters for simulating observation targets are calculated for quantitative evaluation.
    Results Experimental results demonstrate that the proposed method requires only a small amount of input data to achieve a 72-hour orbit prediction with the accuracy better than 4 km and the average computation time of 12.76 s. Compared to high precision orbit propagator model, the proposed method provides higher prediction accuracy and faster execution speed in long-period orbit prediction. The calculated start imaging time error for simulated observation targets is less than 0.2 s, and the initial imaging posture errors in the three-axis directions are all better than 0.03°, which is lower than the attitude pointing accuracy requirement for the imaging process of camera onboard Luojia3-01 satellite.
    Conclusions The proposed method offers high prediction accuracy and demands fewer temporal and spatial resources, making it of great significance for satellite on-orbit autonomous mission planning

     

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