Parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on micro-Doppler features using CNN

作     者：WANG Wantian TANG Ziyue CHEN Yichang SUN Yongjian WANG Wantian;TANG Ziyue;CHEN Yichang;SUN Yongjian

作者机构：Department of Early Warning TechnologyAir Force Early Warning AcademyWuhan 430019China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2020年第31卷第5期

页      面：884-889页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 081105[工学-导航、制导与控制] 0835[工学-软件工程] 081001[工学-通信与信息系统] 081002[工学-信号与信息处理] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (61901514) the Young Talent Program of Air Force Early Warning Academy (TJRC425311G11) 

主　　题：micro-Doppler convolutional neural network(CNN) parity recognition of blade number manoeuvre intention classification 

摘      要：This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier ***, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.