Hesitant fuzzy and dynamic deep reinforcement learning combined unmanned aerial vehicle maneuvering decision-making method

Abstract

The invention discloses a hesitant fuzzy and dynamic deep reinforcement learning combined unmanned aerial vehicle maneuvering decision-making method, which comprises the steps of firstly, establishingan unmanned aerial vehicle air combat motion model, and establishing a decision-making model based on a weighted optimization target according to attack parameters of friends and my parties and an energy parameter difference based on a situation; secondly, according to a hesitant fuzzy theory, determining an optimal weight of a decision model of an optimization target in real time by adopting a maximum deviation method; then, constructing a state space and an action space of air combat maneuver decision reinforcement learning; then, combining the unmanned aerial vehicle states at multiple moments into a state set as neural network input, and constructing a dynamic deep Q network to perform unmanned aerial vehicle maneuvering decision training; and finally, obtaining an optimal maneuveringdecision through the trained dynamic deep Q network. The hesitant fuzzy and dynamic deep reinforcement learning combined unmanned aerial vehicle maneuvering decision-making method mainly solves the problem of maneuvering decision making of the unmanned aerial vehicle under the condition of incomplete environmental information, considers the influence of the air combat process in the decision making process, and better meets the requirements of actual air combat.

Description

technical field [0001] The invention belongs to the field of UAV air combat decision-making, in particular to a UAV maneuver decision-making method combining hesitant fuzzy and dynamic deep reinforcement learning. [0002] technical background [0003] Unmanned combat aircraft (Unmanned Combat Aerial Vehicle, UCAV) needs to decide the optimal tactical plan or maneuver based on the complex battlefield situation information in the process of air combat. The essential. As the air combat environment becomes more and more complex and unknown, improving the intelligence level of UAVs so that UAVs can independently perceive the battlefield environment and automatically generate control commands to complete the maneuver selection in air combat is the main research direction of UAV air combat. [0004] In recent years, with the rapid development of artificial intelligence technology, deep learning and machine learning have shown great potential in the field of UAV air combat decision...

AI Technical Summary

Problems solved by technology

However, in the actual air combat decision-making process, different air combat situations have different requirements for environmental parameters, and each optimization target parameter has certain fuzzy and inaccurate, this type of method cannot meet such requirements

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