Decision-making method for large-scale unmanned aerial vehicle (UAV) cluster dynamic confrontation

Abstract

The invention discloses a decision-making method for large-scale unmanned aerial vehicle (UAV) cluster dynamic confrontation. The method comprises the steps that UAVs conduct situation assessment according to environment information of a battlefield and make multi-UAV cooperation and multi-target distribution decisions with maximizing attack benefits as the target; a cluster motion model based onthe society is established, the UAVs select corresponding behavior rules according to the target distribution result to update the speeds and locations; the UAVs make attack decisions and judge whether distributed targets meet the attack conditions or not, if yes, the UAVs attack the corresponding targets and update the number of weapons and the survival probabilities of the enemy targets, and ifnot, the UAVs judge whether the military operation is ended or not. According to the decision-making method, the UAVs conduct target distribution and negotiation through communication in real time; the cluster motion model is established to achieve updating the speeds and locations of the UAVS; and accordingly, dynamic confrontation between UAV clusters is further realized.

Description

technical field [0001] The invention belongs to the technical field of air combat decision-making, and in particular relates to a decision-making method for large-scale unmanned aerial vehicle cluster dynamic confrontation. Background technique [0002] With the increasing complexity of the battlefield environment and the increasing diversification of combat missions, the combat style of UAVs is gradually developing from a single platform to a multi-platform "swarm" (Swarm). Using a large number of low-cost UAVs to form a cluster to carry out saturation strikes on important enemy targets will become the main combat style of UAV clusters. The most effective way to deal with drone swarm saturation attacks is to use drone swarms to intercept the invading fleet, which leads to aerial confrontation of drone swarms. In the process of cluster confrontation, the air combat situation is constantly changing, and each UAV must make real-time decisions according to the changing situati...

AI Technical Summary

Problems solved by technology

At present, the methods to solve the target allocation problem mainly include Ant Colony Algorithm (ACO), Particle Swarm Algorithm (PSO), Genetic Algorithm (GA), etc., but there are generally problems such as low efficiency and non-convergence, and are only suitable for static target allocation; For the movement decision-making of UAV clusters, the behavior control method based on bionic intelligence is usually adopted. By designing the individual behavior rules and control strategies of UAVs, large-scale UAVs with autonomous capabilities can interact to form an orderly system. Overall, but the swarm movement control strategy for specific combat tasks still needs further research

At present, for the UAV swarm confrontation problem, the main strategy is to decompose the many-to-many cluster air combat into one-to-one confrontation, but the multi-UAV air combat decision-making method is generally only suitable for small-scale UAV swarms. When the number of confrontation individuals is huge and the combat situation is constantly changing, it will be difficult for traditional air combat decision-making methods to solve the dynamic confrontation problem between large-scale UAV clusters.

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