A small unmanned aerial vehicle prevention and control decision-making method and system based on a mixed decision-making model

Abstract

The invention discloses a small UAV prevention and control decision-making method and system based on a mixed decision model. The method includes: obtaining the position and motion information of a small UAV, and constructing a three-degree-of-freedom particle motion model; constructing a small UAV prevention and control Rule model, using the small UAV prevention and control rule model and the three-degree-of-freedom particle motion model to describe the small UAV prevention and control steps; according to the Markov decision process, the state space S, the action space A, and the reward function R are constructed; the dueling-based The structured D3QN network trains and optimizes the prevention and control decision-making model; according to the prevention and control decision-making model, the small UAV prevention and control rule model is updated. The invention builds a mixed decision-making model for the prevention and control of small UAVs, which can effectively improve the automation level of the small UAVs control system for carrying out the prevention and control tasks, and solve the problems of slow decision-making speed and difficulty in the existing small UAVs control and command decision-making. Handle complex scenes and other issues to meet the command and decision-making needs of preventing and controlling small UAVs.

Description

technical field [0001] The invention belongs to the technical field of command and control, and in particular relates to a method and system for preventing and controlling a small unmanned aerial vehicle based on a hybrid decision-making model. Background technique [0002] With the rapid development and application of "low, slow and small" drones, it poses a great threat to the public safety and national security of all countries. In the civilian field, drones have seriously disrupted the order of air traffic control; in the military field, drones have become a new type of combat weapon, and have achieved good strike results in local conflicts. Therefore, countries around the world are speeding up the research on anti-UAV technology and means. Among them, command decision-making needs to coordinate and control multi-source detection and multi-disposal means to prevent and control targets and evaluate the prevention and control effect. It is the current anti-UAV technology a...

AI Technical Summary

Problems solved by technology

However, the existing prevention and control systems have the following problems: (1) The environment for the prevention and control of small UAVs is complex, and the existing decision-making system and process are difficult to adapt to; (2) The decision-making time is short, and manual operation is slow and difficult to deal with Many goals

However, there are also some problems in the command decision-making based on deep reinforcement learning: (1) In practical problem scenarios such as the prevention and control of small UAVs, deep reinforcement learning requires a large number of training samples generated by interacting with the environment; The learned command decision-making model has no initial experience, and first randomly explores in the huge policy space, which leads to low exploration efficiency, long training period and slow learning speed

[0004] Although the knowledge of prevention and control tasks is huge and complex, it is impossible to establish an intelligent expert system with a complete range of rules

However, the traditional command and decision-making method based on expert rules can effectively organize a large amount of empirical knowledge for the analysis and solution of practical problems.

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