These sequences serve as the symbolic input to the artificial neural network we have provided. Additional inforraation can be used to establish which of the several alternative behaviors will actually take place. This method has been used to describe the forms of relationships between accelerations and velocities (not the values themselves.) All possible modes of a system can be identified while offering a complete parametrization of all possible tactical maneuvers. We find tiust the resulting sequences of vectors uniquely express the time evolution of interacting dynamic objects. We have broken our central dynamical problem down into several smaller subproblems ("eigencm-ves"), which describe the states of a continuous-trajectory dynamic system. This problem is solved using a qualitative representation of the maneuvers and their implementation as a neural network. ![]() The problem involves prediction and identification of continuous-trajectory air combat maneuvers where only partial/incomplete information is given. & Tech.A.bstract-The goal of this paper is to consider, formulate, and solve prediction problems encountered in tactical air combat. IFSA-NAFIPSĪkbari S, Menhaj M B, Nikravesh S K Y (2002) A two-phase fuzzy guidance law for planar offensive air-to-air combat maneuver. IEEE Transactions on: Systems, Man and Cybernetics, Part C 31:35–41Īkbari S, Menhaj M B, Nikravesh S K Y (2001) A fuzzy guidance law for modeling offensive air-to-air combat maneuvers. Kachroo P, Shedied S A, Bay J S, Vanlandingham H (2001) Dynamic programming solution for a class of pursuit evasion problems: the herding problem. Proceedings of the American Control conference, vol. Hovakiyan N, Melikan A (2000) Optimal pursuit-evasion paths in a game on complete cone. (2000) How to live and die in the virtual sky, Shaw R L (1988) Fighter combat: Tactics and Maneuvering. AIAA paper 89-3525Īnderson J D (1989) Introduction to flight, 3rd edition, McGraw-Hill, New York ![]() McManus J W, Goodrich K H (1989) Application of artificial intelligence (AI) programming techniques to tactical guidance for fighter aircraft. Proceeding of AIAA guidance, control and dynamics conference Virtanen K, Raivio T, Hamalainen R P (2001) Modeling pilot’s sequential maneuvering decisions by multistage influence diagram. In Rouse W B (ed.) Human/technology interactions in complex systems 7 Hammer J M, Small R L (1996) An intelligent interface in an associate system. Journal of Guidance, Control and Dynamics 15:448–456 Menon P K A, Duke E L (1992) Time-optimal aircraft pursuit evasion with a weapon envelop constraint. Journal of Guidance, Control and Dynamics 7:471–476 Guelman M, Shinar J (1984) Optimal guidance law in the plane. Journal of Guidance, Control and Dynamics 7:690–694 Hillberg C, Jamark B (1984) Pursuit-evasion between two realistic aircraft. Rodin E Y, Geist D, Lirov Y (1989) Flight and fire control knowledge representation. In: Rouse W B (ed) Advances in man-machine system research, v.4, 1988. Reising J M, Emerson T J (1988) Research in cockpit control and displays. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. The results show human like maneuvers can be generated by the proposed model. A large amount of simulations are used to approve the satisfactory performance of the model. The control parameters of the aircraft are computed through a mean square error scheme. Each rule relates the desired moving directions of the pursuer to the task parameters. ![]() The rules are directly obtained from expert’s knowledge. then.” rules are used to represent the pursuer preferences in guiding his/her system. Based on human expert’s decision-making process, an intelligent based method is proposed to model the maneuvering. In this contribution we propose a new guidance law based on fuzzy logic that can be successfully applied to modeling and generating complicated offensive maneuver in an air combat between two aircraft.
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