基于模糊聚类的近距空战决策过程重构与评估

doi:10.7527/S1000-6893.2014.0159

Abstract

Abstract:

At present, a large number of researches focus on the area of intelligent decision-making and to solve the problem of what the evaluation results of air combat is, while the work of studying the reasons which lead to the evaluation results of air combat has received little attention so far. According to the changing characteristic of objective data recorded by air combat training system in air combat training, a fuzzy clustering method calculating the sequence of decision-making items is put forward, and then a fuzzy rough decision-making system is built to reconstruct the decision-making process of close air combat. By calculating and analyzing the relative importance degree between pairwise condition attributes, a two-phase clustering method is employed to deal with the decision-making items with similar importance. Then, the key decision-making sets are available. A case study is provided to analyze and evaluate the decision-making process of close air combat in terms of energy and relative position. Results show that the key decision-making sets result in the ultimate outcome of the air combat from the viewpoint of decision-making.

Key words: evaluation of closing air combat, reconstruction of decision-making process, fuzzy clustering, two-phase clustering, relative importance degree

CLC Number:

V323

ZUO Jialiang, YANG Rennong, ZHANG Ying, WU Meng, XIAO Yuze. Reconstruction and evaluation of close air combat decision- making process based on fuzzy clustering[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(5): 1650-1660.

References

[1] Arar O F, Ayan K. A fexible rule-based framework for pilot performance analysis in air combat simulation system [J].Turkish Journal of Electrical Engineering and Computer Sciences, 2013, 22(21): 2397-2415.
[2] Fu L, Wang X G. Research on close air combat modeling of differential games for unmanned combat air vehicles[J]. Acta Armamentarii, 2012, 33(10): 1210-1216 (in Chinese). 傅莉, 王晓光. 无人战机近距空战微分对策建模研究[J].兵工学报, 2012, 33(10): 1210-1216.
[3] Wang L Y, Zhang H G, Xu H J. Multi-index synthesize evaluation model based on rough set theory for air combat efficiency[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29(4): 880-885 (in Chinese). 王礼沅, 张恒喜, 徐浩军. 基于粗糙集的空战效能多指标综合评估模型[J].航空学报, 2008, 29(4): 880-885.
[4] Kaplan J A, Chappell A R, McManus J W. The analysis of a generic air-to-air missile simulation model, ADA109057[R]. Washington, D.C.: NASA, 1994.
[5] Valerio S M. Probability of kill for VLA asroc torpedo launch[D]. Monterey, California: Naval Postgraduate School, 2009.
[6] Chen X, Liu M, Hu Y X. Study on UAV offensive /defensive game strategy based on uncertain information[J]. Acta Armamentarii 2012, 33(12): 1510-1515 (in Chinese). 陈侠, 刘敏, 胡永新. 基于不确定信息的无人机攻防博弈策略研究[J].兵工学报, 2012, 33(12): 1510-1515.
[7] Kaneshige J, Krishnakumar K, Shung F. Tactical maneuvering using immunized sequence selection, AIAA-2003-6640[R]. Reston: AIAA, 2003.
[8] Mittal S, Doyle M J, Watz E. Detecting intelligent agent behavior with environment abstraction in complex air combat systems[C]//Proceedings of 2013 IEEE International Systems Conference. Piscataway, NJ: IEEE Press, 2013: 662-670.
[9] Bennett Jr W, Schreiber B T, Andrews D H. Developing competency-based methods for near-real-time air combat problem solving assessment[J]. Computers in Human Behavior, 2002, 18(6): 773-782.
[10] Moore J T. Finite state machines for creating, evaluating, and refining air-to-air combat tactics, ADA490111[R]. Laurel, Maryland: Johns Hopkins University Applied Physics Laboratory, 2008.
[11] Huang C Q, Hu J, Cai J. Variable precision rough set decision-making method for situation assessment of UCAV[J]. Systems Engineering and Electronics. 2011,5(5): 1045-1050 (in Chinese). 黄长强, 胡杰, 蔡佳. 无人战斗机态势评估变精度粗集决策方法[J].系统工程与电子技术,2011, 33(5): 1045-1050.
[12] Zhang T, Yu L, Zhou Z L, et al. Decision-making for air combat maneuvering based on hybrid algorithm[J]. Systems Engineering and Electronics, 2013, 35(7): 1445-1450 (in Chinese). 张涛, 于雷, 周中良, 等. 基于混合算法的空战机动决策[J].系统工程与电子技术, 2013, 35(7): 1445-1450.
[13] Xue Y, Zhuang Y, Zhang Y Y, et al.Multiple UCAV cooperative jamming air combat decision making based on heuristic self-adaptive discrete differential algorithm[J]. Acta Aeronautica et Astronautica Sinca, 2013, 34(2): 343-351 (in Chinese). 薛羽, 庄毅, 张友益, 等. 基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策[J].航空学报,2013, 34(2): 343-351.
[14] Veerasamy N. A high-level mapping of cyberterrorism to the OODA loop[C]//Proceedings of 5th European Conference on Information Management and Evaluation. Sonning Common, UK: ACPI, 2011: 352-360.
[15] Gong M G, Wang S, Ma M, et al. Two-phase clustering algorithm for complex distributed data[J]. Journal of Software, 2011, 22(11): 2760-2772 (in Chinese). 公茂果, 王爽, 马萌, 等. 复杂分布数据的二阶段聚类算法[J].软件学报, 2011, 22(11): 2760-2772.
[16] Chen D F, Zhang L. Intuitionistic fuzzy identification of air attack target type[J]. Control and Decision, 2011, 26(7): 1046-1050 (in Chinese). 陈东锋, 张磊. 空袭目标类型的直觉模糊识别[J]. 控制与决策, 2011, 26(7): 1046-1050.
[17] Liu J F, Hu Q H, Yu D. A weighted rough set based method developed for class imbalance learning [J]. Information Sciences, 2008, 178(4): 1235-1256.

Reconstruction and evaluation of close air combat decision- making process based on fuzzy clustering

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	ZHEN Xu, LIU Fang. Track fusion algorithm based on improved FCM and information entropy correction [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 325236-325236.
[2]	CHEN Guo;ZUO Hong-fu. CLASSING PARAMETER'S STUDY OF LUBRICATING OIL METAL DEBRIS [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2002, 23(3): 279-281.