基于改进A-Star算法的隐身无人机快速突防航路规划

doi:10.7527/S1000-6893.2020.23692

电子电气工程与控制

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基于改进A-Star算法的隐身无人机快速突防航路规划

张哲¹, 吴剑^1,2, 代冀阳¹, 应进¹, 何诚¹

1. 南昌航空大学信息工程学院, 南昌 330063;
2. 北京航空航天大学可靠性与系统工程学院, 北京 100083

收稿日期:2019-11-29 修回日期:2020-01-02 出版日期:2020-07-15 发布日期:2020-02-06
通讯作者: 吴剑 E-mail:wujiannchu@126.com
基金资助:
国家自然科学基金（61663032）；航空科学基金（2016ZC56003）；南昌航空大学研究生创新专项基金（YC2019026）

Fast penetration path planning for stealth UAV based on improved A-Star algorithm

ZHANG Zhe¹, WU Jian^1,2, DAI Jiyang¹, YING Jin¹, HE Cheng¹

1. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China;
2. School of Reliability and System Engineering, Beihang University, Beijing 100083, China

Received:2019-11-29 Revised:2020-01-02 Online:2020-07-15 Published:2020-02-06
Supported by:
National Natural Science Foundation of China (61663032); Aeronautical Science Foundation of China (2016ZC56003); Innovation Special Fund for Graduate of Nanchang Hangkong University (YC2019026)

摘要/Abstract

摘要： 针对现代战争中隐身无人机（UAV）在高严密的组网雷达防御体系下的生存及突防问题，提出了基于改进A-Star算法的隐身无人机战区突防航路规划技术。首先对隐身无人机突防过程进行了分析建模，分别建立了隐身无人机的运动学模型、动态雷达散射截面特性和组网雷达探测概率的计算模型。然后针对传统算法在解决隐身突防问题时的不足，充分考虑所规划航路时的快速性和安全性要求，设计了改进A-Star算法。在算法中引入了多层变步长搜索策略和无人机的姿态角信息，结合秩K融合准则，通过每段航迹上隐身无人机被组网雷达系统的发现概率来判断新航迹点的可行性。仿真结果表明，改进A-Star算法能够在复杂的组网雷达系统下快速生成更优的战区突防航路，具有一定的应用价值。

关键词: 无人机, 航路规划, 组网雷达, A-Star算法, 雷达散射截面

Abstract: Aiming to solve the survival and penetration problems of the stealth Unmanned Aerial Vehicle (UAV) under high-definition netted radar defense systems in modern warfare, this paper proposes a stealth UAV theater penetration path planning technology based on an improved A-Star algorithm. Firstly, the penetration process of the stealth UAV is analyzed and modeled, and the kinematics model of the stealth UAV, the dynamic radar cross section characteristics and the calculation model of the netted radar detection probability are established. Then, in view of the shortcomings of traditional algorithms in solving the problem of stealth penetration and taking into full consideration of the requirements of rapidity and safety in the planned route, an improved A-Star algorithm is designed. The multi-layer variable step size search strategy and the attitude angle information of UAVs are introduced into the algorithm. Further combined with the rank K fusion criterion, this algorithm can judge the feasibility of the new track point by the detection probability of the netted radar system of the stealth UAV on each track. The simulation results show that the improved A-Star algorithm can quickly generate better penetration routes in the combat area under complex netted radar systems, exhibiting certain application value.

Key words: unmanned aerial vehicles, route planning, netted radar, A-Star algorithm, radar cross sections

中图分类号:

V279

张哲, 吴剑, 代冀阳, 应进, 何诚. 基于改进A-Star算法的隐身无人机快速突防航路规划[J]. 航空学报, 2020, 41(7): 323692-323692.

ZHANG Zhe, WU Jian, DAI Jiyang, YING Jin, HE Cheng. Fast penetration path planning for stealth UAV based on improved A-Star algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(7): 323692-323692.

参考文献 23

[1]	ZEITZ III F H. UCAV path planning in the presenceof radar-guided surface-to-air missile threats[D]. Michigan:University of Michigan, 2005.
[2]	LI Y, WU Z, HUANG P L. A new method for analyzing integrated stealth ability of penetration aircraft[J]. Chinese Journal of Aeronautics,2010, 23(2):187-193.
[3]	ALVES M A, PORT R J, REZENDE M C. Simulations of the radar cross section of a stealth aircraft[C]//2007 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference. Piscataway:IEEE Press, 2007:409-412.
[4]	LI Y, HUANG J, HONG S, et al. A new assessment method for the comprehensive stealth performance of penetration aircrafts[J]. Aerospace Science and Technology, 2011, 15(7):511-518.
[5]	陈世春, 黄沛霖, 姬金祖. 典型隐身飞机的RCS起伏统计特性[J]. 航空学报, 2014, 35(12):3304-3314. CHEN S C, HUANG P L, JI J Z. Radar cross section fluctuation characteristics of typical stealth aircraft[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(12):3304-3314(in Chinese).
[6]	AMR M, JING R, ALHOSSEIN M. et al. Optimal path planning for unmanned ground vehicles using potential field method and optimal control method[J]. International Journal of Vehicle Performance, 2018, 4(1):1-14.
[7]	HE P, DAI S. Stealth coverage multi-path corridors planning for UAV fleet[C]//Proceedings 2013 Internati-onal Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC). Piscataway:IEEE Press, 2013:2922-2926.
[8]	INANC T, MUEZZINOGLU M K, MISOVEC K, et al. Framework for low-observable trajectory generation in presence of multiple radars[J]. Journal of Guidance, Control, and Dynamics, 2008, 31(6):1740-1749.
[9]	WU P P Y, CAMPBELL D, MERZ T. Multi-objective four-dimensional vehicle motion planning in large dynamic environments[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 41(3):621-634.
[10]	晏青, 熊峻江, 游思明. 基于动态RCS的无人机航迹实时规划[J]. 北京航空航天大学学报, 2011, 37(9):1115-1121. YAN Q, XIONG J J,YOU S M. Real-time programming method for flight path of unmanned vehiclebased on dynamic RCS[J].Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(9):1115-1121(in Chinese).
[11]	KABAMBA P T, MEERKOV S M, ZEITZ F H. Optimal path planning for unmanned combat aerial vehicles to defeat radar tracking[J]. Journal of Guidance, Control, and Dynamics, 2006, 29(2):279-288.
[12]	史伟强, 徐乐, 史小卫. 基于完备对数正态分布模型的隐形飞行器动态RCS统计特性研究[J]. 电子与信息学报, 2013(9):93-97. SHI W Q, XU L, SHI X W. Dynamic RCS statistic characterization of stealth aircraft using complete lognormal distribution[J].Journal of Electronic & Information Technology, 2013(9):93-97(in Chinese).
[13]	丁晓东, 刘毅, 李为民. 基于动态RCS的无人机航迹实时规划方法研究[J]. 系统工程与电子技术, 2008,30(5):868-871. DING X D, LIU Y, LI W M. Dynamic RCS and real-time based analysis of method of UAV route planning[J]. Systems Engineering and Electroncis, 2008, 30(5):868-871(in Chinese).
[14]	田阔, 符小卫, 高晓光. 威胁联网下无人机路径在线规划[J]. 西北工业大学学报, 2011, 29(3):367-373. TIAN K, FU X W, GAO X G. Exploring further UAV on-line path planning in the presence of threat netting[J]. Journal of Northwestern Polytechnical University, 2011, 29(3):367-373(in Chinese).
[15]	GRANT R. The radar game:Understanding stealth and aircraft survivability[M]. Arlington:IRIS Independent Research, 1998.
[16]	SCHWARTZ M. A coincidence procedure for signal detection[J]. IRE Transactions on Information Theory, 1956, 2(4):135-139.
[17]	CAO Y, LONG T, WANG Z, et al. Aircraft route planning for stealth penetration based on sparse A* search[C]//201729th Chinese Control and Decision Conference (CCDC). Piscataway:IEEE Press, 2017:5380-5385.
[18]	莫松, 黄俊, 郑征, 等. 基于改进快速扩展随机树方法的隐身无人机突防航迹规划[J]. 控制理论与应用, 2014, 31(3):375-385. MO S, HUANG J, ZHENG Z, et al. Stealth penetration path planning for stealth ummanned aerial vehicle based on improved rapidly exploring random tree[J].Control Theory & Applications,2014,31(3):375-385(in Chinese).
[19]	DUCHO F, BABINEC A, KAJAN M, et al. Path planning with modified a star algorithm for a mobile robot[J]. Procedia Engineering, 2014, 96:59-69.
[20]	CHENG L, LIU C, YAN B. Improved hierarchical Astar algorithm for optimal parking path planning of the large parking lot[C]//2014 IEEE International Conference on Information and Automation (ICIA). Piscataway:IEEE Press, 2014:695-698.
[21]	CHANG W Y, HSIAO F B, SHEU D. Two-point flightpath planning using a fast graph-search algorithm[J]. Journal of Aerospace Computing, Information, and Communication, 2006, 3(9):453-488.
[22]	GAO X, REN J, CHEN D. Developing an effective algorithm for dynamic UAV path planning with incomplete threat information[J]. Proceedings of the Institution of Mechanical Engineers, Part G:Journal of Aerospace Engineering, 2012, 226(4):413-421.
[23]	WU P P Y, CAMPBELL D, MERZ T. Multi-objective four-dimensional vehicle motion planning in large dynamic environments[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 41(3):621-634.

E-mail：hkxb@buaa.edu.cn

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基于改进A-Star算法的隐身无人机快速突防航路规划

Fast penetration path planning for stealth UAV based on improved A-Star algorithm

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