{"title":"基于PCA+CNN的发射信号识别算法","authors":"Wenqiang Ye, Cong Peng","doi":"10.1109/IAEAC.2018.8577538","DOIUrl":null,"url":null,"abstract":"In order to solve the problem of low recognition rate of emitter signal under low SNR by the traditional method, a recognition algorithm based on PCA+CNN is proposed. The radar emitter signal is processed time-frequency image. The image is processed, and is reduced dimensionality by PCA. Learning model is adjusted by pretraining, and the softmax classifier commonly used on the pretraining model adopts supervised sizing and recognition, finally complete the identification task. The simulation results show that the algorithm can achieve high recognition rate, compared with traditional algorithm.","PeriodicalId":6573,"journal":{"name":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"36 1","pages":"2410-2414"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Recognition Algorithm of Emitter Signals Based on PCA+CNN\",\"authors\":\"Wenqiang Ye, Cong Peng\",\"doi\":\"10.1109/IAEAC.2018.8577538\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to solve the problem of low recognition rate of emitter signal under low SNR by the traditional method, a recognition algorithm based on PCA+CNN is proposed. The radar emitter signal is processed time-frequency image. The image is processed, and is reduced dimensionality by PCA. Learning model is adjusted by pretraining, and the softmax classifier commonly used on the pretraining model adopts supervised sizing and recognition, finally complete the identification task. The simulation results show that the algorithm can achieve high recognition rate, compared with traditional algorithm.\",\"PeriodicalId\":6573,\"journal\":{\"name\":\"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"volume\":\"36 1\",\"pages\":\"2410-2414\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAEAC.2018.8577538\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC.2018.8577538","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognition Algorithm of Emitter Signals Based on PCA+CNN
In order to solve the problem of low recognition rate of emitter signal under low SNR by the traditional method, a recognition algorithm based on PCA+CNN is proposed. The radar emitter signal is processed time-frequency image. The image is processed, and is reduced dimensionality by PCA. Learning model is adjusted by pretraining, and the softmax classifier commonly used on the pretraining model adopts supervised sizing and recognition, finally complete the identification task. The simulation results show that the algorithm can achieve high recognition rate, compared with traditional algorithm.