[1] KELLY M J. Performance measurement during simulated air-to-air combat[J]. Hum Factors, 1988, 30(4):495-506.
[2] CARPENTER G, FALCO M. Analysis of aircraft evasion strategies in air-to-air missile effectiveness models:No. RE-506[R]. Bethpage, New York:Grumman Aerospace Corp, 1975.
[3] Byrnes M W. Nightfall:Machine autonomy in air-to-air combat[J]. Air & Space Power Journal, 2014, 28(3):48-75.
[4] STILLION J. Trends in air-to-air combat:Implications for future air superiority[R]. Washington, D.C.:Center for Strategic and Budgetary Assessments, 2015.
[5] DONG Y, AI J, LIU J. Guidance and control for own aircraft in the autonomous air combat:A historical review and future prospects[J]. Proceedings of the Institution of Mechanical Engineers, Part G:Journal of Aerospace Engineering, 2019, 233(16):5943-5991.
[6] ISAACS R. Differential games:A mathematical theory with applications to warfare and pursuit, control and optimization[M]. North Chelmsford:Courier Dover Publications, 1999.
[7] WONG R E. Some aerospace differential games[J]. Journal of Spacecraft & Rockets, 1967, 4(11):1460-1465.
[8] LYNCH U H. Differential game barriers and their application in air-to-air combat:AFIT-DS/MC/73-1[R]. Ohio:Air Force Institute, 1973.
[9] MERZ A W. The homicidal chauffeur-a differential game[D]. California:Stanford University, 1971.
[10] MERZ A W. The homicidal chauffeur[J]. AIAA Journal, 1974, 12:259-260.
[11] OTHLING J R. W L. Application of differential game theory to pursuit-evasion problems of two aircraft:DS/MC/67-1[R]. Ohia:Air Force Institute, 1970.
[12] LYNCH U H. Differential game barriers and their application in air-to-air combat:AFIT-DS/MC/73-1[R]. Ohia:Air Force Institute, 1973.
[13] OLSTER G J, BREAKWELL J V. Role determination in an aerial dogfight[J]. International Journal of Game Theory, 1974, 3(1):47-66.
[14] KELLEY H, LEFTON L. A preference-ordered discrete gaming approach to air-combat analysis[J]. IEEE Transaction on Automatic Control, 1978, 23(4):642-645.
[15] GETZ W M, PACHTER M. Two-target pursuit-evasion differential games in the plane[J]. Journal of Optimization Theory & Applications, 1981, 34(3):383-403.
[16] GETZ W M, PACHTER M. Capturability in a twotarget game of two cars[J]. Journal of Guidance Control & Dynamics, 1981, 4(1):15-21.
[17] ARDEMA M D, HEYMANN M, RAJAN N. Combat games[J]. Journal of Optimization Theory & Applications, 1985, 46(4):391-398.
[18] ARDEMA A, HEYMANN M, RAJAN N. Analysis of a combat problem:the turret game[J]. Journal of Optimization Theory & Applications, 1987, 54(1):23-42.
[19] LEATHAM A, LINCH U. Two numerical methods to solve realistic air-to-air combat differential games[C]//Proceedings of the 12th AIAA aerospace sciences meeting. Reston:AIAA 1974:74-22.
[20] BURGIN G H, OWENS A J. An adaptive maneuvering logic computer program for the simulation of one-to-one air-to-air combat:NASA CR 2583[R]. Washington, D.C.:NASA, 1975.
[21] BURGIN G H, OWENS A J. An adaptive maneuvering logic computer program for the simulation of one-to-one air-to-air combat:NASA CR 2582[R]. Washington, D.C.:NASA, 1975.
[22] BURGIN G H, EGGLESTON D M. Design of an all-attitude flight control system to execute commanded bank angles and angles of attack:NASA CR 145004[R]. Washington, D.C.:NASA, 1976.
[23] HANKINS III W W. Computer-automated opponent for manned air-to-air combat simulations:NASA TP-1518[R]. Washington, D.C.:NASA, 1979.
[24] GOODRICH K, MCMANUS J. Development of a tactical guidance research and evaluation system (TGRES):AIAA 89-3312[J]. Reston:AIAA, 1989.
[25] GOODRICH K, MCMANUS J. An integrated environment for tactical guidance research and evaluation[C]//Orbital Debris Conference:Technical Issues and Future Directions, 1990:1287.
[26] GOODRICH K H. A high-fidelity, six-degree-of-freedom batch simulation environment for tactical guidance research and evaluation:NASA TM-4440[R]. Washington, D.C.:NASA, 1993.
[27] BATTERSON J G, MORELLI E A. Parameter identification flight test maneuvers for closed loop modeling of the F-18 High Alpha Research Vehicle (HARV):NASACR-198269[R]. Washington, D.C.:NASA, 1996.
[28] ILIFF K W, WANG K C. Flight-determined subsonic longitudinal stability and control derivatives of the F-18 high angle of attack:NASA Technical Memorandum 4786[R]. Washington, D.C.:NASA, 1997.
[29] DONG Y Q, AI J L. Trial input method and own-aircraft state prediction in autonomous air combat[J]. Journal of Aircraft, 2015, 49(3):947-954.
[30] DONG Y Q, AI J L. Maneuvering strategy and own aircraft movement prediction in trial input method-low angle of attack[C]//Proceedings of AIAA Infotech@Aerospace. Reston:AIAA, 2012:2012-2594.
[31] DONG Y Q, HUANG J, AI J L. Visual perception-based target aircraft movement prediction for autonomous air combat[J]. Journal of Aircraft, 2014, 52(2):538-552.
[32] DONG Y Q. Deep Learning-based opponent aircraft attitude detection in autonomous air combat[J]. Journal of Aerospace Information Systems 2019, 16:162-167.
[33] VIRTANEN K, RAIVIO T, HAMALAINEN R. Modeling pilot's sequential maneuvering decisions by a multistage influence diagram[J]. Journal of Guidance, Control, and Dynamics, 2004, 27(4):665-677.
[34] 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.
[35] FENG C, YAO P. On close-range air combat based on hidden Markov model[C]//2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). Piscataway:IEEE Press, 2016:687-694.
[36] FENG C, JING X N, LI Q N, et al. Theoretical research of decision-making point in air combat based on hidden Markov model[J]. Journal of Beijing University of Aeronautics and Astronsutics, 2017, 43(3):615-626(In Chinese). 冯超, 景小宁, 李秋妮, 等. 基于隐马尔可夫模型的空战决策点理论研究[J]. 北京航空航天大学学报(自然科学版), 2017, 43(3):615-626.
[37] HE X, JING X N, FENG C. Air combat maneuver decision based on MCTS method[J]. Journal of Air Force Engineering University (Natural Science Edition), 2017(5):36-41(in Chinese). 何旭, 景小宁, 冯超. 基于蒙特卡洛树搜索方法的空战机动决策[J],空军工程大学学报(自然科学版), 2017(5):36-41.
[38] AUSTIN F, CARBONE G, FALCO M, et al. Game theory for automated maneuvering during air-to-air combat[J]. Journal of Guidance, Control, and Dynamics, 1990, 13(6):1143-1149.
[39] XU G D, LV C, WANG G, et al. Research on UCAV Autonomous air combat maneuvering decision-making based on bi-matrix game[J]. Ship Electronic Engineering, 2017, 37(11):24-28(In Chinese). 徐光达, 吕超, 王光辉, 等. 基于双矩阵对策的UCAV空战自主机动决策研究[J]. 舰船电子工程, 2017, 37(11):24-28.
[40] KATZ A. Tree lookahead in air combat[J]. Journal of Aircraft, 1994, 31:970-973.
[41] MA Y, MA X, SONG X. A case study on air combat decision using approximated dynamic programming[J]. Mathematical Problems in Engineering, 2014(4):10.
[42] MCGREW J, HOW J, WILLIAMS B, et al. Air-combat strategy using approximate dynamic programming[J]. Journal of Guidance, Control, and Dynamics, 2010, 33(5):1641-1654.
[43] RODIN E Y,AMIN S M. Maneuver prediction in air combat via artificial neural networks[J]. Computers & Mathematics with Applications, 1992, 24:95-112.
[44] TENG T H, TAN A H, TAN Y S, et al. Self-organizing neural networks for learning air combat maneuvers[C]//The 2012 International Joint Conference on Neural Networks (IJCNN). Piscataway:IEEE Press, 2012.
[45] POROPUDAS J, VIRTANEN K. Analyzing air combat simulation results with dynamic Bayesian networks[C]//Simulation Conference, 2007:1370-1377.
[46] DU P, LIU H. Study on air combat tactics decision making based on Bayesian networks[C]//20102nd IEEE International Conference on Information Management and Engineering (ICIME). Piscataway:IEEE Press, 2010:317-322.
[47] AKBARI S, MENHAJ M B. A fuzzy guidance law for modeling offensive air-to-air combat maneuver[C]//Joint 9th IFSA world congress and 20th NAFIPS international conference. Piscataway:IEEE Press, 2001:3027-3031.
[48] GHASEMI R, NIKRAVESH S K Y, MENHAJ M B, et al. A near optimal fuzzy modeling of pursuit-evasion in an air combat[J]. WSEAS Transactions on Mathematics, 2004, 3(3):514-521.
[49] GHASEMI R, NIKRAVESH S K Y, MENHAJ M B, et al. A 3-D fuzzy modeling of pilot's performance in the dogfight[EB/OL].[2020-05-22] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.536.7262.
[50] RAO D, BALAS-TIMAR D. A neuro-fuzzy hybridization approach to model the pilot agent in air warfare simulation systems[J]. Journal of Battlefield Technology, 2014, 17(1):17-23.
[51] KARLI M, EFE M O, SEVER H. Air combat learning from F-16 flight information[C]//2017 IEEE international conference on fuzzy systems (FUZZIEEE). Piscataway:IEEE Press,2017:1-6.
[52] ERNEST N, COHEN K, SCHUMACHER C, et al. Learning of intelligent controllers for autonomous unmanned combat aerial vehicles by genetic cascading fuzzy methods[C]//SAE Technical Paper, 2014.
[53] ERNEST N, COHEN K, KIVELEVITCH E, et al. Genetic fuzzy trees and their application towards autonomous training and control of a squadron of unmanned combat aerial vehicles[J]. Unmanned Systems, 2015, 3(3):185-204.
[54] ERNEST N, CARROLL D, SCHUMACHER C, et al. Genetic fuzzy based artificial intelligence for unmanned combat aerial vehicle control in simulated air combat missions[J]. Journal of Defense Management, 2016, 6(1):1-7.
[55] ERNEST N, CARROLL D, BOGART N, et al. Perspectives on genetic fuzzy based artificial intelligence for cooperative control of unmanned fighter aircraft[C]//Proceedings of the 2017 World Congress on Unmanned Systems Engineering, 2017:27-28.
[56] AN X, KOU Y X, LEI Y, et al. Engagement maneuvering strategy of air combat based on fuzzy markov game theory[C]//2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering (CCIE). Piscataway:IEEE Press, 2011:126-129.
[57] LIU P, MA Y. A deep reinforcement learning based intelligent decision method for UCAV air combat[C]//Asian Simulation Conference, 2017:74-286.
[58] ZHOU Y, MA Y, SONG X, et al. Hierarchical fuzzy ART for Q-learning and its application in air combat simulation[J]. International Journal of Modeling Simulation & Entific Computing, 2017, 8:1750052.
[59] DONG Y Q, ZHANG Y, AI J L. Experimental test of artificial potential field-based automobiles automated perpendicular parking[J]. International Journal of Vehicular Technology, 2016, 10:2306818.
[60] LIU X, GONG D. A comparative study of A-star algorithms for search and rescue in perfect maze[C]//2011 International Conference on Electric Information and Control Engineering. Piscataway:IEEE Press, 2011:24-27.
[61] DONG Y, CAMCI E, KAYACAN E. Faster rrt-based nonholonomic path planning in 2d building environments using skeleton-constrained path biasing[J]. Journal of Intelligent & Robotic Systems, 2018, 89(3-4):387-401.
[62] ICHTER B, HARRISON J, PAVONE M. Learning sampling distributions for robot motion planning[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). Piscataway:IEEE Press, 2018:7087-7094.
[63] DONG Y, ZHONG Y, YU W, et al. Mcity data collection for automated vehicles study[J]. arXiv preprint arXiv:1912.06258, 2019.
[64] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics:The kitti dataset[J]. The International Journal of Robotics Research, 2013, 32(11):1231-1237.
[65] SHAW R L. Fighter combat-tactics and maneuvering[M]. Annapolis, Maryland:Naval Institute Press, 1985.
CJA