{"title":"基于离散傅里叶变换的频移键控信号分类","authors":"Z. Yu, Y.Q. Shi, W. Su","doi":"10.1109/MILCOM.2003.1290361","DOIUrl":null,"url":null,"abstract":"The existing decision-theory based classifiers for M-ary frequency shift keying (MFSK) signals have assumed that there is some prior knowledge of the transmitted MFSK signal parameters; while the feature-based classifiers have some limitations such as that their thresholds are signal-to-noise-ratio-dependent (SNR-dependent). In this paper, we investigate some useful properties of the amplitude spectrum of MFSK signals. Using these properties as classification criteria, a fast Fourier transform based classifier (FFTC) of MFSK signals has been developed. The FFTC algorithm is practical since it only requires some reasonable knowledge of a received signal. It is found that the FFTC algorithm works well in classifying 2-FSK, 4-FSK, 8-FSK, 16-FSK, and 32-FSK signals when SNR>0dB. The FFTC algorithm also gives good estimation of the frequency deviation of the received MFSK signal.","PeriodicalId":435910,"journal":{"name":"IEEE Military Communications Conference, 2003. MILCOM 2003.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":"{\"title\":\"M-ary frequency shift keying signal classification based-on discrete Fourier transform\",\"authors\":\"Z. Yu, Y.Q. Shi, W. Su\",\"doi\":\"10.1109/MILCOM.2003.1290361\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The existing decision-theory based classifiers for M-ary frequency shift keying (MFSK) signals have assumed that there is some prior knowledge of the transmitted MFSK signal parameters; while the feature-based classifiers have some limitations such as that their thresholds are signal-to-noise-ratio-dependent (SNR-dependent). In this paper, we investigate some useful properties of the amplitude spectrum of MFSK signals. Using these properties as classification criteria, a fast Fourier transform based classifier (FFTC) of MFSK signals has been developed. The FFTC algorithm is practical since it only requires some reasonable knowledge of a received signal. It is found that the FFTC algorithm works well in classifying 2-FSK, 4-FSK, 8-FSK, 16-FSK, and 32-FSK signals when SNR>0dB. The FFTC algorithm also gives good estimation of the frequency deviation of the received MFSK signal.\",\"PeriodicalId\":435910,\"journal\":{\"name\":\"IEEE Military Communications Conference, 2003. MILCOM 2003.\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"34\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Military Communications Conference, 2003. MILCOM 2003.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MILCOM.2003.1290361\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Military Communications Conference, 2003. MILCOM 2003.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MILCOM.2003.1290361","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
M-ary frequency shift keying signal classification based-on discrete Fourier transform
The existing decision-theory based classifiers for M-ary frequency shift keying (MFSK) signals have assumed that there is some prior knowledge of the transmitted MFSK signal parameters; while the feature-based classifiers have some limitations such as that their thresholds are signal-to-noise-ratio-dependent (SNR-dependent). In this paper, we investigate some useful properties of the amplitude spectrum of MFSK signals. Using these properties as classification criteria, a fast Fourier transform based classifier (FFTC) of MFSK signals has been developed. The FFTC algorithm is practical since it only requires some reasonable knowledge of a received signal. It is found that the FFTC algorithm works well in classifying 2-FSK, 4-FSK, 8-FSK, 16-FSK, and 32-FSK signals when SNR>0dB. The FFTC algorithm also gives good estimation of the frequency deviation of the received MFSK signal.