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

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

罗艺^{1, 2,},
谭贤四^1,,
王红^3,,
曲智国^1,

1.
空军预警学院三系武汉 430019
2.
中国人民解放军94326部队济南 250023
3.
中国人民解放军93184部队北京 100028

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

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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

Search Method for Hypersonic Vehicle Based on Information Geometry

LUO Yi^{1, 2
,},
TAN Xian-Si^1
,,
WANG Hong^3
,,
QU Zhi-Guo^1
,

1.
No. 3 Department, Air Force Early Warning Academy, Wuhan 430019
2.
Unit 94326 of the PLA, Jinan 250023
3.
Unit 93184 of the PLA, Beijing 100028

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

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.
- 高超声速飞行器 /
- 非参数概率密度估计 /
- 波位编排 /
- 信息几何 /
- Kullback-Leibler散度 /
- 搜索策略
Abstract: For the limitation of the line-of-sight, the ground radar cannot continuously observe the hypersonic vehicle. To solve the problem that the hypersonic vehicle is difficult to search after flying out of the blind zone of radar line-of-sight, a search method based on information geometry is proposed. In this paper, the probability density of the occurrence position of hypersonic vehicle is estimated based on the nonparametric probability density estimation method, and the estimated position probability density is used as the guidance information for radar search. According to the guidance information the search area is determined, and the beam position arrangement is carried out in the search area with the maximization of regional coverage as the optimization goal. Based on the theory of information geometry, the search strategy is modeled as a statistical manifold. The difference between the search strategy and the guidance information is measured by the Kullback-Leibler (KL) divergence. The optimal search strategy is solved by minimizing the KL divergence. The effectiveness and feasibility of the proposed method are verified by simulation experiments, and it is proved that it has obvious advantages over other search strategies.
- Hypersonic vehicle /
- nonparametric probability density estimation /
- beam position arrangement /
- information geometry /
- Kullback-Leibler (KL) divergence /
- search strategy

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图 1 波位编排方式

Fig. 1 Arrangement of wave position

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图 2 高超声速飞行器轨迹

Fig. 2 Trajectory of hypersonic vehicle

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图 3 仿真场景示意图

Fig. 3 Schematic diagram of simulation scene

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图 4 引导信息

Fig. 4 Guide information

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图 5 搜索方法结果

Fig. 5 Results of search method

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图 6 条目宽度与KL散度之间的关系

Fig. 6 Relationship between item width and KL divergence

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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

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图 8 KL散度距离变化

Fig. 8 Change of KL divergence distance

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图 9 不同搜索方法下的捕获概率

Fig. 9 Capture probability under different search methods

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图 10 不同搜索波位下的捕获概率

Fig. 10 Capture probability under different search wave positions

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图 11 不同检测概率下的捕获概率

Fig. 11 Capture probability under different detection probabilities

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图 12 未进行优化的波位编排

Fig. 12 Arrangement of unoptimized wave position

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表 1 雷达参数

Table 1 Radar parameters

参数	值
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%

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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	6.2	78
本文所提方法		未优化	7.5	76
基于信息论的搜索方法		优化	7.3	39
基于信息论的搜索方法		未优化	7.8	40
传统搜索方法	平行搜索	优化	9.6	45
	平行搜索	未优化	10.2	45
	随机搜索	优化	8.5	55
	随机搜索	未优化	8.9	53
	螺旋搜索	优化	9.1	46
	螺旋搜索	未优化	9.5	46

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