UAV formation optimization model based on ant colony algorithm and particle swarm optimization algorithm

Abstract

Based on the research of azimuth-only passive positioning of unmanned aerial vehicles (UAVs) flying in formation, this paper establishes a reasonable positioning information determination mode, which can effectively analyze and judge the flight status of UAVs, minimize the positioning time and improve the accuracy. Based on the ant colony algorithm, for the determination of uav in the signal receiving process, to simulate the way of by drone signal more comprehensive to determine the location of the unmanned aerial vehicle (uav), according to the direction of the received information for unmanned aerial vehicle (uav) position adjustment, through data analysis and calculation more reasonable distribution of unmanned aerial vehicle (uav) location scheme, then using the particle swarm optimization (pso) algorithm, To further optimize the positioning problem of different types of formation UAVs, the previous model is still adopted. At the same time, the particle swarm optimization algorithm is used to improve, upgrade and optimize the latest position of UAVs, and the two groups of reasonable and accurate flight modes and schemes under the conical formation are summarized.