Autonomous task planning of formation imaging satellites based on improved genetic algorithm

MIAO Yue; WANG Feng; ZHANG Yong-qiang

doi:10.3788/OPE.20172514.0168

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Autonomous task planning of formation imaging satellites based on improved genetic algorithm

更新时间：2020-08-13

- Autonomous task planning of formation imaging satellites based on improved genetic algorithm
- Optics and Precision Engineering Vol. 25, Issue 12z, Pages: 168-179(2017)
- 作者机构：
  
  1. 哈尔滨工业大学航天学院,黑龙江哈尔滨,150001
  2. 中国人民解放军63921部队北京,100094
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172514.0168
  CLC： V474.2
- Received：23 August 2017，
  
  Revised：06 September 2017，
  
  Published：31 December 2017
- 稿件说明：
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苗悦, 王峰, 张永强. 基于改进遗传算法的编队成像卫星自主任务规划[J]. 光学精密工程, 2017,25(12z): 168-179

MIAO Yue, WANG Feng, ZHANG Yong-qiang. Autonomous task planning of formation imaging satellites based on improved genetic algorithm[J]. Editorial Office of Optics and Precision Engineering, 2017,25(12z): 168-179
苗悦, 王峰, 张永强. 基于改进遗传算法的编队成像卫星自主任务规划[J]. 光学精密工程, 2017,25(12z): 168-179 DOI： 10.3788/OPE.20172514.0168.

MIAO Yue, WANG Feng, ZHANG Yong-qiang. Autonomous task planning of formation imaging satellites based on improved genetic algorithm[J]. Editorial Office of Optics and Precision Engineering, 2017,25(12z): 168-179 DOI： 10.3788/OPE.20172514.0168.

摘要

自主任务规划是成像卫星快速响应紧急观测任务，高效获取多个地面目标信息的重要途径。针对将成像区域内多个目标快速分配给多颗卫星的自主任务规划问题，提出了一种改进型遗传算法，用以快速形成每颗卫星的对地成像序列。首先，分析卫星对目标的成像条件，确定目标观测时刻与卫星期望侧摆等信息。然后，将目标、卫星成像序列、卫星编队依次编码为基因、串、个体，并设计一种基于卫星连续成像侧摆时间判断的初始种群生成方法，用以快速形成包含若干重点目标的卫星初始成像序列。再通过多次的选择-交叉-变异过程优化初始群体，使多星成像序列的观测收益达到对应于进化次数的现时最大值。最后，以10颗编队卫星规划包含3个等级的160个地面目标作为仿真实例。结果表明算法能够在30 s内给出各颗卫星的成像序列，相比于简单遗传算法，总成像收益提高40%，重点目标数量增加1.6倍。满足工程应用的快速性与高收益要求。

Abstract

Autonomous task planning is an important method for imaging satellites to fast react to emergent observing missions

and to capture ground targets effectively. For the autonomous task planning problem of fast selecting targets in observation area for formation flying satellites

an improved genetic algorithm was put forward to fast obtain imaging sequence for every satellite. First

constraints of a satellite observing a target were analyzed

and observation time and expected lateral swing angles were clarified for each satellite and target. Then

ground target

imaging sequence and formation satellites were coded as gene

string and individual respectively. For fast forming original imaging sequence containing several important targets for each satellite

an original population generation method was developed based on time judgement of satellite continuous lateral swings. Selection

crossover and mutation were applied iteratively to optimize the original population

so that imaging sequences with current-optimal gain corresponding to evolution times were achieved. Finally

a simulation example containing 10 satellites in formation and 160 ground targets with 3 grades was provided. The results show that the improved genetic algorithm can generate imaging sequences for all satellites within 30 s. Contrast with simple genetic algorithm

imaging gain of proposed algorithm is improved by 40%

and the number of important targets is increased by 1.6 times. Thus

it can meet rapidity and high-gain requirements of engineering application.

关键词

Keywords

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