The application of GNSS has been fully penetrated into the national security and national economy,but due to the weak signal strength of GNSS signal after reaching the ground,it is extremely vulnerable to unintentional or intentional human interference.When there is suppression interference and it will affect the normal operation of the receiver,resulting in a certain area of navigation and positioning being affected,so the identification and elimination of interference sources is very important.For the suppressed interference in the above,the location estimation of the interference source is achieved by distributing a certain number of low-cost receivers in the monitoring area and using the characteristics of their received carrier-to-noise data.Considering that the attenuation model during signal propagation is nonlinear,an interference source location method based on Genetic Algorithm(GA)optimized Back Propagation(BP)neural network is proposed.The complex nonlinear relationship of the carrier-to-noise ratio characteristics in the monitoring area is obtained through neural network learning and the genetic algorithm optimizes the initial weights and thresholds of the neural network,finally the interference source location is searched in the monitoring area by the gradient descent method.The results show that the prediction error of the optimized network by genetic algorithm is smaller,and it can initially locate the interference source location with an Average Localization Error Rate(ALER)of about 0.23％,which verifies the reasonableness and effectiveness of the model.
关键词
载噪比/压制干扰/全球导航卫星系统干扰源定位/反向传播神经网络
Key words
carrier to noise ratio/suppression of interference/GNSS interference source location/BP neural network
维普
万方数据