天基低轨海上移动目标成像搜索任务调度
收稿日期: 2023-03-29
修回日期: 2023-04-26
录用日期: 2023-05-30
网络出版日期: 2023-05-31
基金资助
国家自然科学基金(12125207);载人航天工程科技创新团队资助
Space-based LEO-observation search planning for maritime moving targets
Received date: 2023-03-29
Revised date: 2023-04-26
Accepted date: 2023-05-30
Online published: 2023-05-31
Supported by
National Natural Science Foundation of China(12125207);the from Technology Innovation Team of Manned Spaced Engineering
搜索海上移动目标在民用和军事领域都有着迫切需求。相比于飞机和舰船等传统工具,低轨成像卫星更适用于远海大面积的搜索任务。从移动目标运动预测和卫星观测调度2个关键问题入手,对单先验点无反馈场景下的卫星成像搜索调度问题展开了研究。首先建立运动预测模型,提出了基于局部航向加权策略和偏航约束假设的区域概率分布建模方法。而后建立观测调度模型,定义了考虑单窗口卫星视场切换的设计变量,并提出了基于目标“逃逸概率”指标的目标函数模型。最后是优化求解,提出了一种基于启发式单窗口定向寻优策略的两层优化算法。通过3组仿真算例对所提出方法进行了分析。第1组基于轨迹打靶法验证了概率预测指标和“预测-调度”方法的整体有效性,第2组采用多组蒙特卡洛轨迹集打靶说明了“逃逸概率”指标的可行性,第3组通过算法对比证明了两层优化算法在求解效率和解的质量方面的优势。
杨迪 , 李振瑜 , 郭帅 , 张嘉城 , 罗亚中 . 天基低轨海上移动目标成像搜索任务调度[J]. 航空学报, 2023 , 44(15) : 528752 -528752 . DOI: 10.7527/S1000-6893.2023.28752
Searching for maritime moving targets is commonly demanded in civil and military fields. In contrast to conventional means via airplanes and ships, Low-Earth-Orbit (LEO) observation satellites are more applicable for large-area search in the distant sea. In this paper, a satellite observation search planning problem in the scenario of single prior location without feedback is studied, which contains two key issues, motion prediction of moving targets and planning of observation satellites. First is motion prediction modeling, in which an area-probability-distribution modeling method is proposed based on a local-heading-weighting strategy and a yaw-constraint assumption. Then, an observation-planning model is proposed.in which, the design variables are defined considering the switching of observation swaths. An objective function that is based on the “missing probability” of the target is proposed. Finally, a two-layer optimization algorithm based on a single-window-directional-search heuristic strategy is proposed to solve the satellite searching scheme. The methods proposed are analyzed with three groups of simulations. Firstly, the global validity of the probabilistic prediction indicator and “prediction-planning” method is verified by trajectory shooting. Secondly, the feasibility of the “missing probability” indicator is proved by shooting for multiple Monte-Carlo trajectory sets. Thirdly, a comparison of the two-layer optimization algorithm with two other methods shows the advantage of the algorithm in solution efficiency and quality.
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