Unmanned aerial vehicle maneuvering target tracking method based on DDPG transfer learning

Abstract

The invention relates to an unmanned aerial vehicle maneuvering target tracking method based on DDPG transfer learning, and the unmanned aerial vehicle maneuvering target tracking method carrying outtask decomposition, initializes an environment state, neural network parameters and other hyper-parameters, and carries out the training of a neural network. At the beginning of each round, the unmanned aerial vehicle executes an action to change the speed and the course angle, to obtain a new state, stores the experience of each round in an experience pool to serve as a learning sample, and continuously iterates and updates parameters of the neural network. And when the training is completed, the neural network parameters trained by the sub-tasks are stored, and are migrated to the unmanned aerial vehicle maneuvering target tracking network in the next task scene until the final task is completed.

Description

technical field

[0001] The invention relates to a method for tracking a maneuvering target of an unmanned aerial vehicle based on DDPG transfer learning, which belongs to the field of robot intelligent control. Background technique

[0002] With the continuous development of drone technology, drones have been widely used in the civil field. Among the many tasks of UAVs, surveillance and reconnaissance tasks are performed most. If UAVs can independently and accurately track other maneuvering targets, expand the scope of surveillance, and effectively avoid the threat area, it can be greatly improved. Improve surveillance, reconnaissance and even attack efficiency.

[0003] Most of the existing research on UAV maneuvering targets focuses on the state estimation and measurement information processing of the maneuvering target. Few studies have been done on how to determine the maneuvering behavior of the UAV after the state of the maneuvering target is determined, so that it ca...

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