Strategy synthesis in aerial dogfight game models

Falco, M.; Cohen, Victor; Carpenter, Gilbert; Kaercher, Arthur

doi:10.2514/6.1973-233

Cited by 5 publications

(6 citation statements)

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“…Another line for the role determination problem, in contrast to the retrogressive manner, is the progressive solution. Readers can find related works in Meier; 53 Falco and Cohen; 70 Ciletti; 71 Olster and Breakwell; 72 Peng and Vincen; 73 and Kelley and Lefton, 74 and the last chapter in Baron et al 75 Meier 53 still followed a formal approach similar to Section ''Mathematical formal approach''. However, the authors propagated from the current moment to the future, following the best (most tight turning) maneuvers of both players.…”

Section: C) Role Determinationmentioning

confidence: 99%

“…Ciletti et al; 71 Olster and Breakwell; 72 and Peng and Vincen 73 defined the reachable sets/barriers of the aircraft (and weapon) by extrapolating for a certain period to the future, (current) tactical status of the players was then determined. Falco and Cohen; 70 Kelley and Lefton; 74 and Baron et al 75 hinged on the progressive postulation of the combat simulation, which was similar to the dynamic programing technique. The simulation was either solved in continuous space via Runge-Kutta scheme, 70,74 or with Markov process modeling in discrete space.…”

Section: C) Role Determinationmentioning

confidence: 99%

“…Falco and Cohen; 70 Kelley and Lefton; 74 and Baron et al 75 hinged on the progressive postulation of the combat simulation, which was similar to the dynamic programing technique. The simulation was either solved in continuous space via Runge-Kutta scheme, 70,74 or with Markov process modeling in discrete space. 70,75 Involving the weapon capability in this line of work is very straightforward-one just needs to add the simulation of weapon effectiveness.…”

Section: C) Role Determinationmentioning

confidence: 99%

“…The simulation was either solved in continuous space via Runge-Kutta scheme, 70,74 or with Markov process modeling in discrete space. 70,75 Involving the weapon capability in this line of work is very straightforward-one just needs to add the simulation of weapon effectiveness. Complex weapon envelopes can be involved.…”

Section: C) Role Determinationmentioning

confidence: 99%

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Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Dong

Liu

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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The Autonomous Air Combat technique has been a lasting research topic for decades. However, no complete solutions seem to have appeared because of the highly dynamic and complex nature of the Autonomous Air Combat problem. In devising the Autonomous Air Combat solutions, we follow similar methodologies in the robotics community, and divide the overall scheme into two folds: the perception of other (enemy/friendly) aircraft, and the guidance/control for own aircraft. While the perception in the first fold serves as a foundation, this paper is mainly focused on the second one. Based on our survey, a review of own aircraft guidance/control in the (primarily one-to-one) Autonomous Air Combat solutions is presented. We divide different Autonomous Air Combat solutions into three groups, i.e. mathematics-based, knowledge-encoded, and learning-driven. In each group, we present the representative methods first; problem definition, solution, and a brief overview of the historical development are illustrated. We also comment on both weakness and strengths for each group/method. We point out certain technical paths/challenges that need to be addressed in the future Autonomous Air Combat development, i.e. to abstract and emulate the human pilot experiences, and to develop the online learning capabilites. Inspired by the state-of-art techniques in other similar fields (robotics, autonomous driving), we also propose potential solutions, i.e. traditional approaches enhanced by the novel data-driven technique. Via this paper, we hope to deliver an in-depth analysis of past experiences and potential challenges/solutions for the Autonomous Air Combat technique. We also advocate referring to the approaches/techniques that are utilized in other similar fields in devising the Autonomous Air Combat solutions.

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Section: C) Role Determinationmentioning

confidence: 99%

Section: C) Role Determinationmentioning

confidence: 99%

Section: C) Role Determinationmentioning

confidence: 99%

Section: C) Role Determinationmentioning

confidence: 99%

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Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Dong

Liu

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…A discrete treatment, taking some account of incomplete information as well as the necessity of a finite lethal exposure time, is described in [FALco and COHEN, 1973].…”

Section: Introductionmentioning

confidence: 99%

Role determination in an aerial dogfight

Olsder

Breakwell

1974

Int J Game Theory

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A coplanar aerial dogfight is analyzed by assuming constant, not necessarily identical, speeds and individual maximum turning rates and lethal ranges. A combatant (A) is assumed to be victorious when his opponent (B) has been maneuvered into a relative position within A's lethal range and in the direction of A's velocity.Three variables are required to define the instantaneous "state" of the game, namely the relative position (2) and the angle (1) between their velocities. A computer program has been constructed to divide the 3-dimensional region of possible initial (and subsequent) states into regions corresponding to victory by one or the other combatant, and, if the faster combatant has the smaller lethal range, a "no contest" region corresponding to escape by the faster combatant.The critical separating surface (or surfaces) is composed of a number of pieces corresponding to initial conditions leading either to simultaneous kill or to "near miss" situations of one type or another. Optimal play is defined in the immediate neighborhood of the entire separating surface, guaranteeing victory (or escape) to one combatant or the other, depending on location on one side or the other of the separating surface.

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A multimicroprocessor approach to numerical analysis

McGill¹,

Steinhoff²

1976

SIGARCH Comput. Archit. News

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A parallel processing system is described that consists of a minicomputer host and a set of bipolar microcomputer modules. It is argued that such a system in which the microcomputers operate with little mutual interaction should be effective for an important class of problems in numerical analysis. In particular, estimates are given for the operation of the system on a problem in gaming theory. In this problem, the extensive I/O and software capabilities of the minicomputer provide ease of use for a large part of the problem. The relatively simple part of the problem, which requires almost all of the computational time, is executed in parallel on the microcomputers. It is argued that the system, with 10 to 20 modules, would offer one to two orders of magnitude more speed at several orders of magnitude less cost than current large general-purpose machines. The potential for the development of new algorithms that exploit fully the characteristics of the new devices is discussed.

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Strategy synthesis in aerial dogfight game models

Cited by 5 publications

References 0 publications

Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Role determination in an aerial dogfight

A multimicroprocessor approach to numerical analysis

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