Multi UAVs cooperative target localization based on GA-BP neural network
As highly mobile platforms,UAVs often have a large error in their own position.A genetic algorithm optimized back-propagation neural network(GA-BP)real-time localization method is proposed for multiple radiation source targets in a specific region in this contribution.Firstly,the position information of different known targets is obtained as the expected output of the network from a specific region and their arrival time differences are calculated as the input of the network to construct training data set;then,the initial weights and thresholds of the BP neural network are optimized by using the adaptive nature of the genetic algorithm so that it can quickly jump out of the local optimal solution to achieve high accuracy localization.The corresponding GA-BP network model is obtained through training,and the unknown target can be localized in real time by using this model.Simulation results show that,compared with the two-step weighted least squares algorithm and the BP neural network,the localization accuracy of the method suggested in this work is closer to the Cramer-Rao boundary.
passive localizationarrival time differenceUAVsGA-BP neural networkreal-time localizationCramer-Rao boundarymulti-target localizationsensor position error
万方数据
维普
