Optimize-by-priority on-orbit task real-time planning for agile imaging satellite

Yue MIAO

doi:10.3788/OPE.20182601.0150

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Optimize-by-priority on-orbit task real-time planning for agile imaging satellite

Information Sciences | 更新时间：2020-08-13

- Optimize-by-priority on-orbit task real-time planning for agile imaging satellite
- Optics and Precision Engineering Vol. 26, Issue 1, Pages: 150-160(2018)
- 作者机构：
  
  哈尔滨工业大学航天学院, 黑龙江哈尔滨 150009
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182601.0150
  CLC： V474.2
- Received：13 January 2017，
  
  Accepted：30 March 2017，
  
  Published：25 January 2018
- 稿件说明：
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Yue MIAO. Optimize-by-priority on-orbit task real-time planning for agile imaging satellite[J]. Optics and precision engineering, 2018, 26(1): 150-160.
DOI：

Yue MIAO. Optimize-by-priority on-orbit task real-time planning for agile imaging satellite[J]. Optics and precision engineering, 2018, 26(1): 150-160. DOI： 10.3788/OPE.20182601.0150.

摘要

为实现对航班失联、自然灾害等突发事件的快速响应与有效观测，针对面向热点区域内多个目标的敏捷成像卫星在轨任务实时规划问题，设计了一种基于目标等级的逐级择优在轨任务实时规划算法，用以快速形成最优成像序列。首先，将热点区域内的多个地面目标按重要程度划分为多个等级，在分析目标位置与卫星性能之间约束条件的基础上，将任务规划问题转化为以成像收益为目标的优化问题。然后，针对该优化问题，在同等级目标中按"先到先得"的方法进行筛选，在当前级最优成像序列的基础上，进行下一级目标的筛选与插入，逐级实现成像收益的最大化。最后，对18个地面目标进行为期500 s的规划仿真。结果表明，算法能够在1 s内给出对应最大收益的对地成像序列。同遗传算法相比，所提算法解算更快，结果更为稳定，满足工程应用的快速性与高收益要求。

Abstract

In order to realize quick response and effective detection in emergencies such as flight missing and natural disaster

an optimize-by-priority on-orbit task real-time planning algorithm based on priorities of targets was proposed. First

ground targets were divided into several priorities in order of importance

and the task planning was transformed to an optimization problem with objective of maximizing the imaging gain by analyzing constraints between target locations and satellite performance. Then

the targets of the same priority were selected by the rule of "first come

first served"

lower-priority targets were inserted into current optimal imaging sequences filled with several former-priority targets

and the maximum imaging gain will be obtained. Finally

a 500 s planning simulation of 18 ground targets was given. Results indicate that the algorithm proposed can give imaging sequence with the maximum gain within 1 s. The proposed algorithm calculates faster and gives more stable results as compared with the genetic algorithm.

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references

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