基于改进遗传算法的SAR多星协同复杂区域观测规划  

SAR multi-satellite collaborative complex area observation planning based on improved genetic algorithm

作　　者：石鑫[1] 邢孟道[1,2] 张金松 刘会涛 王虹现[1] SHI Xin;XING Mengdao;ZHANG Jinsong;LIU Huitao;WANG Hongxian(National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China;Academy of Advanced Interdisciplinary Research,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学雷达信号处理全国重点实验室,西安710071 [2]西安电子科技大学前沿交叉研究院,西安710071

出　　处：《遥感学报》2024年第7期1822-1834,共13页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:62371376,62331020);中央高校基本科研业务费专项资金(编号:20199234731,20103227364);雷达信号处理全国重点实验室支持计划项目(编号:KGJ202201)。

摘　　要：遥感卫星大范围区域观测在地图绘制、灾害救援等领域均具有重要作用。SAR遥感卫星具有不受云雾夜间环境影响的特点,研究SAR多星协同区域观测技术具有重要意义。针对当前缺乏SAR多星协同对复杂区域快速观测规划方法的问题,本文首先对大范围复杂区域覆盖率计算进行分析,提出了结合高斯投影、网格划分与几何运算的复杂区域覆盖率计算方法;然后对SAR条带成像模式进行覆盖分析,提出了结合角度限制和两维分解的候选区域分解方法;最后提出了结合贪婪算法初始化、精英保留策略和3次适应度函数的改进遗传算法用于区域覆盖率优化。本文选取4颗在轨SAR卫星和3个区域目标进行仿真实验,实验结果证明本文方法在北京市、天津市、上海市3个区域都能够实现优异的区域覆盖率优化,相比贪婪算法,本文方法在上述3个区域的覆盖率分别提升3.17%、2.94%、9.02%。该算法可为SAR多星协同区域观测系统的建立提供技术基础。Large-scale regional observations by remote-sensing satellites play an important role in mapping,disaster relief,and other fields.The efficiency of single-satellite observation is low,and multisatellite collaborative observation is the main means for rapidly observing large areas.To date,multisatellite collaborative observation is mostly studied on optical remote-sensing satellites,but research on the collaborative observation of SAR satellites is limited.Moreover,SAR satellites have imaging mechanisms and modes distinct from those of optical satellites,and thus the optical satellite collaborative planning method cannot be fully applied to SAR satellites.For the optimization of the performance of SAR multisatellite collaborative observation,research into SAR multisatellite collaborative regional observation technologies is crucial.First,the coverage calculation of large-scale complex areas was analyzed,and a complex area coverage calculation method that combines Gaussian projection,grid division,and geometric operations and can realize the coverage calculation of any complex area,was proposed.Then,an accurate coverage analysis was performed on the SAR strip imaging mode,and a candidate area decomposition method combining angle restriction and two-dimensional decomposition was established.Optimization efficiency was improved by reducing the optimization space through angle restriction,and complex continuous optimization problems were discretized through two-dimensional decomposition.These approaches allowed the use of genetic algorithms for optimization.Finally,an improved genetic algorithm combining greedy algorithm initialization,elite retention strategy,and cubic fitness function was formulated for regional coverage optimization.Chromosome encoding,crossover,and mutation operations were designed for optimization,an optimal retention strategy was used to improve optimization speed and stability,and the cubic fitness function was used to improve the optimization effect.This study selected four on-orbit SAR satell

关 键 词：遥感 星载SAR 多星协同 区域观测 覆盖计算 区域分解 遗传算法 

分 类 号：TP701[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]