Unmanned aerial vehicle group flight path optimization method for multi-radiation source tracking

Abstract

An unmanned aerial vehicle group flight path optimization method for multi-radiation source tracking comprises an establishment module, an estimation module, a matching module, a positioning module and a tracking module. The establishment module is used for establishing an unmanned aerial vehicle group trajectory optimization problem under a multi-constraint condition; the estimation module obtains a mapping relation between the received signal strength and the distance by adopting a deep neural network; the matching module adopts an interactive matrix generation method to obtain an unmanned aerial vehicle and radiation source matching scheme; the positioning module obtains a reference position of the radiation source by adopting a multi-ball intersection positioning method; and the tracking module adopts a deep reinforcement learning method to design a flight path optimization algorithm of the unmanned aerial vehicle group. Compared with a traditional method, the method has obvious advantages in the index aspects of average tracking time, task completion rate, convergence speed and the like.

Description

technical field [0001] The invention relates to the technical field of radiation source positioning and tracking, in particular to a method for optimizing the flight trajectory of a group of unmanned aerial vehicles (UAVs) oriented to multi-radiation source tracking. Background technique [0002] In recent years, UAVs have inherent advantages such as high mobility, on-demand deployment, and low cost, and have been widely used as mobile sensors in positioning and tracking systems. In addition, compared with a single UAV, UAV swarms have unique advantages in multi-task and complex task scenarios. However, UAV swarms also face multiple challenges such as communication interaction, task assignment, and trajectory design. [0003] The communication interaction and task assignment between UAVs is a key technology to realize UAV swarm tracking, and it is one of the important research directions in the field of swarm intelligence. Before executing the mission, according to differen...

AI Technical Summary

Problems solved by technology

Existing research mainly focuses on the optimal design of the two-dimensional trajectory of the UAV group, while the design and optimization of the three-dimensional trajectory is more practical but more challenging, such as the complexity of the channel environment and the hugeness of the action and state space.

In addition, existing research has attempted to apply reinforcement learning methods to radiation source tracking scenarios. However, for UAV swarm tracking multi-radiation source target scenarios, how to design effective algorithms to improve the convergence speed of reinforcement learning is an urgent research problem.

[0005] There are many existing trajectory optimization algorithms, including the classic particle swarm optimization algorithm, simulated annealing algorithm, genetic algorithm, and ant colony algorithm. solve

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