A stochastic game-theoretic approach for analysis of multiple cooperative air combat

Ha, Jung-Su; Chae, Hyeok-Joo; Choi, Han‐Lim

doi:10.1109/acc.2015.7171909

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…With the increase in the distance between the enemy and us, the superiority value increases, but if it is too close, the effectiveness of airborne radar searching for prime targets and the attack effect of air-to-air missiles on the enemy will also be substantially reduced. Therefore, the corresponding distance advantage function can be constructed according to the performance of airborne radar and missiles, as shown in Equation (6).…”

Section: Geometric Situation Modelingmentioning

confidence: 99%

“…In the increasingly complex air combat environment, an advanced intelligent airborne system is built to generate maneuver commands and guide UAVs to perform maneuvers during combat. The traditional autonomous maneuver decision-making methods for UAV air combat are mainly divided into game theory [6][7][8], differential game strategy [9,10], influence graph method [11], and Bayesian theory [12]. A threat assessment model for UAV air combat and a target allocation problem to search for the best strategy to achieve the UAV air combat mission were established by Liu et al [13].…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

Lyu²,

Shi³

et al. 2022

Aerospace

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With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs.

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Section: Geometric Situation Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

Lyu²,

Shi³

et al. 2022

Aerospace

View full text Add to dashboard Cite

show abstract

“…The existing air combat maneuver strategies can be divided into two categories: rule-based strategy and learning-based strategy. Rule-based strategy mainly select actions according to the given behavior rules in air combat, and not need online training and optimization, including matrix game algorithm [9,10], expert system [11], influence graph method [12,13], differential game method [14], etc. The matrix game method is prone to the phenomenon that the overall strategy effect is not good.…”

Section: Introductionmentioning

confidence: 99%

Autonomous maneuver strategy of swarm air combat based on DDPG

Wang

Zhao

et al. 2021

Auton. Intell. Syst.

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Unmanned aerial vehicles (UAVs) have been found significantly important in the air combats, where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics. The key to empower the UAVs with such capability is the autonomous maneuver decision making. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. First, based on the process of air combat and the constraints of the swarm, the motion model of UAV and the multi-to-one air combat model are established. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. The results show that the algorithm is suitable for UAV swarms of different scales.

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Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions

Chen,

Li,

Yan

et al. 2024

Advanced Intelligent Systems

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Decision‐making in unmanned combat aerial vehicles (UCAVs) presents a multifaceted challenge because of the complexity and dynamics of the flight environment, which leads to hurdles in training convergence, low decision validity, and the dimensionality catastrophe for decision‐making neural networks. A novel framework is proposed to address breaking down the complicated decision issues, which combines the strengths of graph convolutional networks in relation extraction with the ability of hierarchical reinforcement learning. To solve the problem of decision validity under high‐dimensional inputs, the joint framework is applied to the Maneuver Intent's decision, and a maneuver library‐based state space design method is suggested. The joint framework executes adaptable strategies and flight maneuvers to address the issue of training non‐convergence or task failure due to difficult‐to‐obtain reward signals across various scenarios. Then, the recurrent curriculum training and cross‐entropy rewards are designed to train decisions on different sub‐strategies. The experimental evaluation demonstrated more flexibility and adaptability in decision‐making problems under complex tasks compared to rule‐based and reinforcement learning baseline methods. The method proposed in this article provides a novel approach to resolving intricate decision problems, and which has certain theoretical significance and reference value for engineering applications.

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A stochastic game-theoretic approach for analysis of multiple cooperative air combat

Cited by 8 publications

References 10 publications

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

Autonomous maneuver strategy of swarm air combat based on DDPG

Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions

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