Hierarchical optimization algorithm for air fleet formation in air-combat

RAN Huaming; XIONG Rongling

doi:10.7527/S1000-6893.2020.24257

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2020 , Vol. 41 >Issue S2: 724257 - 724257

DOI: https://doi.org/10.7527/S1000-6893.2020.24257

Hierarchical optimization algorithm for air fleet formation in air-combat

RAN Huaming ,
XIONG Rongling

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Key Laboratory of Avionic Information System Technology, The 10 th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China

Received date: 2020-04-20

Revised date: 2020-05-20

Online published: 2020-06-04

Supported by

National Defence Pre-research Foundation

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Abstract

To solve the problem of computing complexity in air fleet formation optimization, this paper proposes a hierarchical optimization algorithm. A collaborative task allocation model was established based on the distance, the angle, the velocity, and the performance of the missiles and radars of both sides. The air fleet formation of the enemy was layered according to the basic aircraft formations usually adopted in air combat. For each layer, when our formations of this layer select a variety of basic formations, the task allocation results and formation optimization advantages were calculated, and the optimal formations of this layer in our air fleet were then calculated by comparison. When all the optimization formations of each layer were obtained, the optimal formation of our air fleet can be obtained through combinational decoding of the optimal formations of each layer. The simulation results demonstrate that this method can effectively solve the fleet formation optimization problem with good real-time performance.

Key words： air fleet formation; formation optimization; task allocation; hierarchical optimization; fleet coding

Cite this article

RAN Huaming , XIONG Rongling . Hierarchical optimization algorithm for air fleet formation in air-combat[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020 , 41(S2) : 724257 -724257 . DOI: 10.7527/S1000-6893.2020.24257

References

[1] 张佳龙, 闫建国, 张普. 基于反步推演法的多机编队队形重构控制[J]. 航空学报, 2019, 40(11):323177. ZHANG J L, YAN J G, ZHANG P. Multi-UAV formation forming reconfiguration control based on backing-steeping method[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(11):323177(in Chinese).
[2] 张佳龙, 闫建国, 张普. 基于自适应方法的多无人机编队队形控制[J]. 航空学报, 2020, 41(1):323385. ZHANG J L, YAN J G, ZHANG P. Multi-UAV formation forming control based on adaptive method under wind field disturbances[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(1):323385(in Chinese).
[3] ROSA M R, BALDI S, WANG X, et al. Adaptive hierarchical formation control for uncertain Euler-Lagrange systems using distributed inverse dynamics[J]. European Journal of Control, 2018, 48:52-56.
[4] 马培蓓, 雷明, 纪军. 均等通信时滞下多UAV协同编队控制[J]. 航空学报, 2017, 38(S1):721551. MA P B, LEI M, JI J. Control of multi-UAV cooperative formation with equality communication time-delay[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(S1):721551(in Chinese).
[5] LIAO F, TEO R, WANG J L, et al. Distributed formation and reconfiguration control of VTOL UAVs[J]. IEEE Transactions on Control Systems Technology, 2017, 25(1):270-277.
[6] 夏庆军, 张安, 张耀中. 大规模编队空战队形优化算法[J]. 控制理论与应用, 2010, 27(10):1418-1422. XIA Q J, ZHANG A, ZHANG Y Z. Formation-optimizing algorithm for large-scale air combat[J]. Control Theory & Applications, 2010, 27(10):1418-1422(in Chinese).
[7] 钱斌, 姜长生. 遗传算法在直升机空战编队优化中的应用[J]. 电光与控制, 2008, 15(1):6-9. QIAN B, JIANG C S. On air combat formation of helicopters based on genetic slgorithm[J]. Electronics Optics & Control, 2008, 15(1):6-9(in Chinese).
[8] 张科施. 基于遗传算法的空战编队优化研究[D]. 西安:西北工业大学, 2003:8-9. ZHANG K S. On optimizing large-scale air-combat formation with genetic algorithm[D]. Xi'an:Northwestern Polytechnical University, 2003:8-9(in Chinese).
[9] 张科施, 王正平. 基于遗传模拟退火算法的空战编队优化研究[J]. 西北工业大学学报, 2003, 21(4):477-480. ZHANG K S, WANG Z P. On Optimizing large-scale air-combat formation with simulated-annealing GA (Genetic Algorithm)[J]. Journal of Northwestern Polytechnical University, 2003, 21(4):477-480(in Chinese).
[10] MULGUND S, HARPER K, KRISHNAKUMAR K, et al. Air combat tactics optimization using stochastic genetic algorithms[C]//IEEE International Conference on Systems, Man, & Cybernetics. Piscataway:IEEE Press, 1998:3136-3141.
[11] MULGUND S, HARPER K, ZACHARIAS G. Large-scale air combat tactics optimization using genetic algorithms[J]. Journal of Guidance, Control, and Dynamics, 2001, 24(1):140-142.
[12] VIRTANEN K, KARELAHTI J, RAIVIO T. Modeling air combat by a moving horizon influence diagram game[J]. Journal of Guidance, Control, and Dynamics, 2006, 29(5):1080-1091.
[13] 费爱国, 张陆游, 丁前军. 基于拍卖算法的多机协同火力分配[J]. 系统工程与电子技术, 2012, 34(9):1829-1833. FEI A G, ZHANG L Y, DING Q J. Multi-aircraft cooperative fire assignment based on auction agorithm[J]. Systems Engineering and Electronics, 2012, 34(9):1829-1833(in Chinese).
[14] 冉华明,周锐,董卓宁,等. 空战中协同干扰、探测、攻击任务分配[J]. 北京航空航天大学学报, 2015, 29(5):911-918. RAN H M, ZHOU R, DONG Z N, et al. Cooperative jamming-detecting-attacking task allocation in air combat[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(5):911-918(in Chinese).
[15] 肖冰松,方洋旺,夏海宝,等. 多机协同对空目标探测与攻击任务的最优分配[J]. 火力与指挥控制, 2011, 36(6):19-23. XIAO B S, FANG Y W, XIA H B, et al. Optimal allocation of aerial target detection and attack in cooperative multi-fighter air combat[J]. Huoli yu Zhihui Kongzhi, 2011, 36(6):19-23(in Chinese).
[16] 崔晓宝, 李楠. 机载PD雷达对机动目标探测盲区计算模型研究[J].火控雷达技术, 2008, 37(3):36-40. CUI X B, LI N. Research on calculation models for detection blind zone of airborne PD radar against maneuvering targets[J]. Fire Control Radar Technology, 2008, 37(3):36-40(in Chinese).
[17] 朱宝鎏,朱荣昌,熊笑非.作战飞机效能评估[M]. 2版.北京:航空工业出版社,2006. ZHU B L, ZHU R C, XIONG X F. Fighter plane effectiveness assessment[M]. 2nd ed. Beijing:Aviation Industry Press, 2006(in Chinese).
[18] 王婷,符小卫,高晓光.基于改进遗传算法的异构多无人机任务分配[J].火力与指挥控制,2013,38(5):37-41. WANG T, FU X W, GAO X G. Cooperative task assignment for heterogeneous multi-UAVs based on improved genetic algorithm[J]. Fire Control and Command Control, 2013, 38(5):37-41(in Chinese).
[19] SHIMA T, RASMUSSEN S J, SPARKS A G, et al. Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms[J]. Computers & Operations Research, 2006, 33(11):3252-3269.
[20] CHOI H L, BRUNET L, HOW J P. Consensus-based decentralized auctions for robust task allocation[J]. IEEE Transactions on Robotics, 2009, 25(4):912-926.
[21] SHIMA T, RASMUSSEN S J, SPARKS A G. UAV cooperative multiple task assignments using genetic algorithms[C]//American Control Conference. Piscataway:IEEE Press, 2005:2989-2994.
[22] 吴华, 史忠亚, 沈文迪, 等. 基于IFS和IPSO算法的干扰资源分配方法[J]. 北京航空航天大学学报, 2017, 43(12):2370-2376. WU H, SHI Z Y, SHEN W D, et al. Distribution method of jamming resource based on IFS and IPSO algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(12):2370-2376(in Chinese).
[23] XING X J, FAN D SH, ZHAO Y Q, et al. PSO-based multi UCAVs cooperative attack tasks allocation and its simulation[C]//201612th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). Piscataway:IEEE Press, 2016:598-601.
[24] 李俨, 董玉娜. 基于SA-DPSO混合优化算法的协同空战火力分配[J]. 航空学报, 2010, 31(3):626-631. LI Y, DONG Y N. Weapon-target assignment based on simulated annealing and discrete particle swarm optimization in cooperative air combat[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(3):626-631(in Chinese).
[25] 宋遐淦, 江驹, 徐海燕. 改进模拟退火遗传算法在协同空战中的应用[J]. 哈尔滨工程大学学报, 2017, 38(11):1762-1768. SONG X G, JIANG J, XU H Y. Application of improved simulated annealing genetic algorithm in cooperative air combat[J]. Journal of Harbin Engineering University, 2017, 38(11):1762-1768(in Chinese).
[26] ASIM T, SEROL B. Weapon target assignment with combinatorial optimization techniques[J]. International Journal of Advanced Research in Artificial Intelligence, 2013, 2(17):39-50.

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