Research on Decision–Making Method of Air Combat Embedded Training Based on Extended Influence Diagram

To improve the accuracy and real-time performance of autonomous decision-making by the unmanned combat aerial vehicle (UCAV), a decision-making method combining the dynamic relational weight algorithm and moving time strategy is proposed, and trajectory prediction is added to maneuver decision-making. Considering the lack of continuity and diversity of air combat situation reflected by the constant weight in situation assessment, a dynamic relational weight algorithm is proposed to establish an air combat situation system and adjust the weight according to the current situation. Based on the dominance function, this method calculates the correlation degree of each subsituation and the total situation. According to the priority principle and information entropy theory, the hierarchical fitting function is proposed, the association expectation is calculated by using if-then rules, and the weight is dynamically adjusted. In trajectory prediction, the online sliding input module is introduced, and the long- and short-term memory (LSTM) network is used for real-time prediction. To further improve the prediction accuracy, the adaptive boosting (Ada) method is used to build the outer frame and compare with three traditional prediction networks. The results show that the prediction accuracy of Ada-LSTM is better. In the decision-making method, the moving time optimization strategy is adopted. To solve the problem of timeliness and optimization, each control variable is divided into 9 gradients, and there are 729 control schemes in the control sequence. Through contrast pursuit simulation experiments, it is verified that the maneuver decision method combining the dynamic relational weight algorithm and moving time strategy has a better accuracy and real-time performance. In the case of using prediction and not using prediction, the adaptive countermeasure simulation is carried out with the current more advanced Bayesian inference maneuvering decision-making scheme. The results show that the UCAV maneuvering decision-making ability combined with accurate prediction is better.

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An Air Combat Decision-Making Method Based on Knowledge and Grammar Evolution

Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems - Communications in Computer and Information Science ◽

10.1007/978-981-10-2666-9_52 ◽

2016 ◽

pp. 508-518 ◽

Cited By ~ 1

Author(s):

Duan Yang ◽

Yaofei Ma

Keyword(s):

Decision Making ◽

Air Combat

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Intelligent Decision-Making of MAV/UAV in Air Combat Based on DDPG Algorithm

10.1007/978-981-15-8155-7_403 ◽

2021 ◽

pp. 4881-4891

Author(s):

Yue Li ◽

Wei Han ◽

Weiguo Zhong ◽

Jiazheng Ji ◽

Wanhui Mu

Keyword(s):

Decision Making ◽

Intelligent Decision Making ◽

Intelligent Decision ◽

Air Combat

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Research on Air Combat Maneuver Decision-Making Method Based on Reinforcement Learning

Electronics ◽

10.3390/electronics7110279 ◽

2018 ◽

Vol 7 (11) ◽

pp. 279 ◽

Cited By ~ 6

Author(s):

Xianbing Zhang ◽

Guoqing Liu ◽

Chaojie Yang ◽

Jiang Wu

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Learning Algorithm ◽

Signal Design ◽

Training Environment ◽

Strategy Space ◽

Intelligent Decision Making ◽

Combat Training ◽

Network Method ◽

Air Combat

With the development of information technology, the degree of intelligence in air combat is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon air combat, this paper constructs a super-horizon air combat training environment, which includes aircraft model modeling, air combat scene design, enemy aircraft strategy design, and reward and punishment signal design. In order to improve the efficiency of the reinforcement learning algorithm for the exploration of strategy space, this paper proposes a heuristic Q-Network method that integrates expert experience, and uses expert experience as a heuristic signal to guide the search process. At the same time, heuristic exploration and random exploration are combined. Aiming at the over-the-horizon air combat maneuver decision problem, the heuristic Q-Network method is adopted to train the neural network model in the over-the-horizon air combat training environment. Through continuous interaction with the environment, self-learning of the air combat maneuver strategy is realized. The efficiency of the heuristic Q-Network method and effectiveness of the air combat maneuver strategy are verified by simulation experiments.

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Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information

Entropy ◽

10.3390/e22030279 ◽

2020 ◽

Vol 22 (3) ◽

pp. 279 ◽

Cited By ~ 1

Author(s):

Tongle Zhou ◽

Mou Chen ◽

Yuhui Wang ◽

Jianliang He ◽

Chenguang Yang

Keyword(s):

Decision Making ◽

Decision Tree ◽

Incomplete Information ◽

Time Series Data ◽

Short Term Memory ◽

Prediction Method ◽

Series Data ◽

Air Combat ◽

Solid Foundation ◽

Long Short Term Memory

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

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