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基于信息几何的高超声速飞行器搜索方法

罗艺 谭贤四 王红 曲智国

罗艺, 谭贤四, 王红, 曲智国. 基于信息几何的高超声速飞行器搜索方法. 自动化学报, 2022, 48(6): 1520−1529 doi: 10.16383/j.aas.c200738
引用本文: 罗艺, 谭贤四, 王红, 曲智国. 基于信息几何的高超声速飞行器搜索方法. 自动化学报, 2022, 48(6): 1520−1529 doi: 10.16383/j.aas.c200738
Luo Yi, Tan Xian-Si, Wang Hong, Qu Zhi-Guo. Search method for hypersonic vehicle based on information geometry. Acta Automatica Sinica, 2022, 48(6): 1520−1529 doi: 10.16383/j.aas.c200738
Citation: Luo Yi, Tan Xian-Si, Wang Hong, Qu Zhi-Guo. Search method for hypersonic vehicle based on information geometry. Acta Automatica Sinica, 2022, 48(6): 1520−1529 doi: 10.16383/j.aas.c200738

基于信息几何的高超声速飞行器搜索方法

doi: 10.16383/j.aas.c200738
基金项目: 国家自然科学基金(61401504)资助
详细信息
    作者简介:

    罗艺:空军预警学院三系博士研究生. 主要研究方向为高超声速飞行器预警资源管理. 本文通信作者. E-mail: 13297983885@163.com

    谭贤四:空军预警学院三系教授. 主要研究方向为预警监视装备体系建设与运用. E-mail: tanxs-hust@163.com

    王红:中国人民解放军93184部队教授. 主要研究方向为体系结构技术、装备论证. E-mail: wanghong572g@sina.com

    曲智国:空军预警学院三系副教授. 主要研究方向为图像处理, 预警监视, 高超声速飞行器预警技术. E-mail: green20001@sina.com

Search Method for Hypersonic Vehicle Based on Information Geometry

Funds: Supported by National Natural Science Foundation of China (61401504)
More Information
    Author Bio:

    LUO Yi Ph.D. candidate at the No. 3 Department of Air Force Early Warning Academy. His main rese-arch interest is early warning resource management of hypersonic aircraft. Corresponding author of this paper

    TAN Xian-Si Professor at the No. 3 Department of Air Force Early Warning Academy. His research interest covers construction and application of early warning surveillance equipment system

    WANG Hong Professor at Unit 93184 of the PLA. Her research interest covers architecture technology and equipment demonstration

    QU Zhi-Guo Professor at the No. 3 Department of Air Force Early Warning Academy. His research interest covers image processing, early warning surveillance, and hypersonic aircraft early warning technology

  • 摘要: 由于地面雷达受视距限制无法对高超声速飞行器进行连续观测, 针对高超声速飞行器飞出雷达视距盲区后难以搜索的问题, 提出了一种基于信息几何的雷达搜索方法. 本文利用非参数概率密度估计法对高超声速飞行器的出现位置的概率密度进行估计, 并将估计的位置概率密度作为雷达搜索的引导信息; 根据引导信息确定搜索区域, 以区域覆盖率最大化作为优化目标在搜索区域内进行波位编排; 基于信息几何理论, 将搜索策略建模为统计流形, 利用KL (Kullback-Leibler)散度来度量搜索策略与引导信息之间的差异, 通过最小化KL散度获得最优搜索策略. 通过仿真实验验证了本文所提方法的有效性和可行性, 并验证了相比其他搜索方法具有较明显的优势.
  • 图  1  波位编排方式

    Fig.  1  Arrangement of wave position

    图  2  高超声速飞行器轨迹

    Fig.  2  Trajectory of hypersonic vehicle

    图  3  仿真场景示意图

    Fig.  3  Schematic diagram of simulation scene

    图  4  引导信息

    Fig.  4  Guide information

    图  5  搜索方法结果

    Fig.  5  Results of search method

    图  6  条目宽度与KL散度之间的关系

    Fig.  6  Relationship between item width and KL divergence

    图  7  波位编排优化求解中适应度变化

    Fig.  7  Change of fitness in the optimization of wave position arrangement

    图  8  KL散度距离变化

    Fig.  8  Change of KL divergence distance

    图  9  不同搜索方法下的捕获概率

    Fig.  9  Capture probability under different search methods

    图  10  不同搜索波位下的捕获概率

    Fig.  10  Capture probability under different search wave positions

    图  11  不同检测概率下的捕获概率

    Fig.  11  Capture probability under different detection probabilities

    图  12  未进行优化的波位编排

    Fig.  12  Arrangement of unoptimized wave position

    表  1  雷达参数

    Table  1  Radar parameters

    参数
    探测距离 (km) 2000
    测角误差 (°) 0.2
    测距误差 (m) 100
    方位角覆盖范围 (°) −75 ~ +75
    俯仰角覆盖范围 (°) 0 ~ 70
    波束宽度 (°) 1×1
    波束驻留时间 (ms) 100
    检测概率 80%
    下载: 导出CSV

    表  2  仿真结果对比

    Table  2  Comparison of simulation results

    方法波位编排方式搜索波位个数捕获概率 (%)
    本文所提方法优化6.278
    未优化7.576
    基于信息论的搜索方法优化7.339
    未优化7.840
    传统搜索方法平行搜索优化9.645
    未优化10.245
    随机搜索优化8.555
    未优化8.953
    螺旋搜索优化9.146
    未优化9.546
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-08
  • 录用日期:  2020-11-18
  • 网络出版日期:  2020-12-18
  • 刊出日期:  2022-06-02

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