An Air Combat Decision Learning System Based on a Brain-Like Cognitive Mechanism

Zhou, Kai; Wei, Ruixuan; Xu, Zhijie; Lu, Hongxuan

doi:10.1007/s12559-019-09683-7

Cited by 21 publications

(8 citation statements)

References 37 publications

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“…Several algorithms and approaches have been applied to realize control systems that enable autonomous systems to execute complex missions [29][30][31][32][33][34][35]. For instance, an autonomous decision-making for unmanned combat aerial vehicles (UCAVs) using genetic fuzzy trees is presented by authors in [29].…”

Section: Related Workmentioning

confidence: 99%

“…The proposed system shows the capability of obtaining strategies that are robust, aggressive and responsive against opponents. In [30], an intelligent air combat learning system based on the learning dynamics of the human brain is designed and its strategy acquisition without prior rules is demonstrated. The authors in [34] used deep q-neural networks to obtain combat strategies in an attack-defense pursuit-warfare of multiple UCAVs in a simplified environment.…”

Section: Related Workmentioning

confidence: 99%

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A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Nantogma

Ran

2021

Symmetry

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Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of the learning classifier system and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Nantogma

Ran

2021

Symmetry

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“…Compared with single UAV, multiple UAVs can form a swarm cooperative combat system with mutual cooperation, complementary advantages, and doubled efficiency in flight space. e 2009-2047 UAV system flight plan of the U.S. Air Force points out that "with the trans-swarm from automatic capability to autonomous capability, UAVs will realize multiaircraft cooperative operation, so that a single operator can monitor multiple multimission UAVs at the same time, and the attack will become more concentrated, sustained, and largescale" [16][17][18]. erefore, swarm cooperative operation will be the inevitable development trend of the multi-UAV autonomous attack operation mode.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Guidance and Control Methods for Missile Swarm

Huang

Zhao

Zhang

2022

Computational Intelligence and Neuroscience

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High-speed unmanned aerial vehicles (UAVs) are more and more widely used in both military and civil fields at present, especially the missile swarm attack, and will play an irreplaceable key role in the future war as a special combat mode. This study summarizes the guidance and control methods of missile swarm attack operation. First, the traditional design ideas of the guidance and control system are introduced; then, the typical swarm attack guidance and control methods are analyzed by taking their respective characteristics into considering, and the limitations of the traditional design methods are given. On this basis, the study focuses on the advantages of intelligent integrated guidance and control design over traditional design ideas, summarizes the commonly used integrated guidance and control design methods and their applications, and explores the cooperative attack strategy of missile swarm suitable for the integrated guidance and control system. Finally, the challenges of missile swarm guidance and control are described, and the problems worthy of further research in the future are prospected. Summarizing the guidance and control methods of missile will contribute to the innovative research in this field, so as to promote the rapid development of unmanned swarm attack technology.

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“…In modern air combat, the vigorous development of aviation science and military technology leads to more and more severe threats of aerial targets. Meanwhile, due to the application of high technology of Unmanned Combat Air Vehicle (UCAV), such as space early warning systems, radar stealthy composites, artificial intelligence technology, etc., the complexity of a confrontation environment including uncertainty and incompleteness is increasing [2]. It is difficult for commanders to process a large amount of confrontation information and identify enemy target intentions in a short time.…”

Section: Introductionmentioning

confidence: 99%

A thermal grid-based method for intention recognition of air target formations

Chen

Feng

Zhou

2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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Intention recognition of air target formations plays a crucial role in modern air warfare. Therefore, the paper combines expert experience and domain knowledge to propose a formation intention determination method based on a thermal grid map. Firstly, the confrontation area is reasonably gridded, secondly, a thermal distribution grid map is generated based on the spatial characteristics of the target intelligence data, then formations are derived based on the map, and finally the intention of each formation is judged based on the adversarial rules. In order to evaluate the feasibility of this method, a data case is given in this paper to demonstrate its effectiveness in practical application.

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An Air Combat Decision Learning System Based on a Brain-Like Cognitive Mechanism

Cited by 21 publications

References 37 publications

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Intelligent Guidance and Control Methods for Missile Swarm

A thermal grid-based method for intention recognition of air target formations

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