A New Machine Learning Framework for Air Combat Intelligent Virtual Opponent

Xiang, Lei; Huang, An-xiang; Zhao, Tao; Su, Yuqiang; Ren, Chuan

doi:10.1088/1742-6596/1069/1/012031

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Intelligent information processing has been widely used in the age of internet of things [1][2][3][4][5] . Artificial intelligence technology has brought new weapons to intelligent air combat such as the ALPHA software system 6 . Situation assessment in air combat environment is a key issue for successful defence and attack 7 .…”

Section: Situation Assessment In Air Combat Considering Incomplete Fr...mentioning

confidence: 99%

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Zhou

Tang

Zhao

2022

Sci Rep

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For situation assessment in air combat, there may be incomplete information because of new technologies and unknown or uncertain targets and threats. In this paper, an improved method of situation assessment for air combat environment considering incomplete frame of discernment in the evidence theory is proposed to get a more accurate fusion result for decision making in the battlefield environment. First, the situation in air combat is assessed with knowledge. Then, the incomplete frame of discernment in the generalized evidence theory, which is an extension of Dempster–Shafer evidence theory, is adopted to model the incomplete and unknown situation assessment. After that, the generalized combination rule in the generalized evidence theory is adopted for fusion of situations in intelligent air combat. Finally, real-time decision-making in situation assessment can be reached for actions to take. Experiments in situation assessment of air combat with incomplete and uncertain situations show the rationality and effectiveness of the proposed method.

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Section: Situation Assessment In Air Combat Considering Incomplete Fr...mentioning

confidence: 99%

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Zhou

Tang

Zhao

2022

Sci Rep

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“…The method entails the adaptation of behavior represented as finite-state machines (FSMs) through a reinforcement learning technique called dynamic scripting. Lei et al (2018) use a machine learning approach with data samples generated by a "human-in-loop" air combat simulation system. The data includes the aircraft model, attributes of the two opposing groups (blue and red), aircraft position, attitude, and critical actions (target locking, real-time interference, launch missiles).…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Unmanned Combat Air Vehicle in Beyond Visual Range Air Combat Based on Artificial Neural Networks

Filho

Medeiros²,

Passaro

2021

J. Aerosp. Technol. Manag.

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In a beyond visual range (BVR) air combat, one of the challenges is identifying the best time to launch a missile, which is a decision that must be made quickly. The decision involves combining knowledge about altitude, speed, distance, onboard sensor systems information, aircraft type, and type of missile on the aircraft, as well as intelligence on the opponent's behavior. This paper discusses an approach to evaluate the probability of shoot-down of an unmanned combat air vehicle (UCAV) in a BVR air combat, based on a decision support system model that makes use of parameters available from the onboard sensors of the shooter UCAV. The strategic options development and analysis (SODA) method is applied to select the main features available in the on-board sensor systems of the shooter aircraft required to launch a missile successfully. Such features help us to develop an artificial neural network (ANN) for shoot-down prediction. The ANN was trained with a data set with 1093 registered shoots in military exercises, and it shows 78.0% accuracy with the cross-validation procedure.

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“…Artificial intelligence technology has brought new weapons to intelligent air combat, e.g., the ALPHA software system [ 1 , 2 , 3 ]. In air combat situation assessment, the decision-making procedure of pilots and unmanned combat aerial vehicle is deeply dependent on intelligent information processing methods [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy

Zhou

Tang

Zhao

2019

Entropy

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Uncertain information exists in each procedure of an air combat situation assessment. To address this issue, this paper proposes an improved method to address the uncertain information fusion of air combat situation assessment in the Dempster–Shafer evidence theory (DST) framework. A better fusion result regarding the prediction of military intention can be helpful for decision-making in an air combat situation. To obtain a more accurate fusion result of situation assessment, an improved belief entropy (IBE) is applied to preprocess the uncertainty of situation assessment information. Data fusion of assessment information after preprocessing will be based on the classical Dempster’s rule of combination. The illustrative example result validates the rationality and the effectiveness of the proposed method.

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A New Machine Learning Framework for Air Combat Intelligent Virtual Opponent

Cited by 4 publications

References 5 publications

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Decision Support System for Unmanned Combat Air Vehicle in Beyond Visual Range Air Combat Based on Artificial Neural Networks

A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy

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