Design of Target Aircraft Auto Air-Combat Tactics Decision System

Yuan, Kungang; Liu, Dengdi; Jiang, Daogang; Zhang, Zhiwei; Lei, Xiang

doi:10.1007/978-981-10-2666-9_28

Cited by 6 publications

(4 citation statements)

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“…Based on the survey, we summarize the following shortcomings of the existing air combat maneuvering decision methods. (i)Many research on the air combat maneuver decision problem address one‐on‐one air combat [13, 28–39]. However, in modern air combat, one‐on‐one air combat scenarios are rare, and most air combat are conducted in formations. (ii)Existing research on multi‐aircraft air combat focus on cooperative target assignments [40, 41], WEZ computation [42], and high‐level maneuver tactics sequence solution in fixed‐size formation.…”

Section: Introductionmentioning

confidence: 99%

“…The model-free methods include expert system method [28][29][30][31], supervised learning [32][33][34], and RL [13,[35][36][37][38][39]. Concretely, the expert system method relies on a knowledge base built by specialists, but it is hard to adapt to a changing environment and build a multi-aircraft air combat knowledge base.…”

mentioning

confidence: 99%

“…(i) Many research on the air combat maneuver decision problem address one-on-one air combat [13,[28][29][30][31][32][33][34][35][36][37][38][39]. However, in modern air combat, one-on-one air combat scenarios are rare, and most air combat are conducted in formations.…”

mentioning

confidence: 99%

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Hierarchical multi‐agent reinforcement learning for multi‐aircraft close‐range air combat

Kong

Zhou

et al. 2022

IET Control Theory & Appl

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The close‐range autonomous air combat has gained significant attention from researchers involved in applications related to artificial intelligence (AI). A majority of the previous studies on autonomous air combat were focused on one‐on‐one air combat scenarios, however, the modern air combat is mostly conducted in formations. With regard to the aforementioned factors, a novel hierarchical maneuvering control architecture is introduced that is applied to the multi‐aircraft close‐range air combat scenario, which can handle air combat scenarios with variable‐size formation. Subsequently, three air combat sub‐tasks are designed, and recurrent soft actor‐critic (RSAC) algorithm combined with competitive self‐play (SP) is incorporated to learn the sub‐strategies. A novel hierarchical multi‐agent reinforcement learning (HMARL) algorithm is proposed to obtain the high‐level strategy for target and sub‐strategy selection. The training performance of the training algorithm of sub‐strategies and high‐level strategy in different air combat scenarios is evaluated. The obtained strategies are analyzed and it is found that the formations exhibit effective cooperative behavior in symmetric and asymmetric scenarios. Finally, the ideas of engineering implementation of the maneuvering control architecture are given. The study provides a solution for future multi‐aircraft autonomous air combat.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Hierarchical multi‐agent reinforcement learning for multi‐aircraft close‐range air combat

Kong

Zhou

et al. 2022

IET Control Theory & Appl

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“…Air combat may occur in two primary forms: Within Visual Range (WVR) and Beyond Visual Range (BVR) [1], with the latter being recently more developed in the operational context, due to more extensive availability of more advanced weapons and sensors [2]. Notice that, even though modern air combat may still end WVR, through a series of complex decisions and maneuvers, it usually begins BVR, which frequently is the most critical phase of the combat since it may provide advantages and drawbacks for succeeding phases [3]. There is no clear definition of the distance to differentiate these two forms of air combat since this may be subject to the conditions in which the air combat happens.…”

Section: Introductionmentioning

confidence: 99%

Engagement Decision Support for Beyond Visual Range Air Combat

Dantas¹,

Costa²,

Geraldo³

et al. 2021

Preprint

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This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simulac ¸ão Aeroespacial -ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.

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