Emitter Beam State Sensing Based on Convolutional Neural Network and Received Signal Strength  

作　　者：JIANG Yilin LI Xiang ZHANG Haoping 蒋伊琳;李向;张昊平

机构地区：[1]College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China [2]Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology,Harbin Engineering University,Harbin 150001,China [3]China Ordnance Industry Group Aviation Ammunition Research Institute Co.,Ltd.,Harbin 150030,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2024年第6期1017-1022,共6页上海交通大学学报(英文版)

基　　金：the National Natural Science Foundation of China(No.62071137);the Fundamental Research Funds for Central Universities(No.3072021CF0816);the Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology Project(No.AMCIT2102-04)。

摘　　要：In this paper,a classification method based on convolutional neural network(CNN)and received signal strength(RSS)is proposed to solve the problem of non-cooperative emitter beam state sensing in electrical situational awareness.RSS,sensor coordinates,and received signal frequency are taken as the input features of CNN,while real state is taken as the output of CNN.To increase the RSS gradient contained in the eigenvector,a multi-layer sensor array is proposed to measure RSS.Simulation results show that the proposed method is robust to array location disturbance,and has the ability to generalize the mismatches in target location and main lobe beam width between first nulls.

关 键 词：electromagnetic situation awareness convolutional neural network(CNN) received signal strength(RSS) emitter beam pointing 

分 类 号：TN911.7[电子电信—通信与信息系统]