{"title":"Seeking pattern recognition principles for intelligent detection of FSK signals","authors":"V. Neagoe","doi":"10.1109/ICPR.1992.201878","DOIUrl":null,"url":null,"abstract":"Proposes the following cascade for intelligent detection of the presence of binary frequency-shift-keying (FSK) signals corrupted by additive white Gaussian noise: (1) discrete Fourier Transform (DFT) for periodogram estimation, computed at the two modulating frequencies; (2) a specific pattern recognition algorithm in the spectral space IR/sup 2/, consisting of one of the following variants: (a) perceptron; (b) fuzzy perceptron; (c) Bayes. The computer simulation results show the significant improvement of the proposed pattern recognition methods by comparison to the classical technique of detection theory by matched filter. The proposed paper tries to build a bridge between the worlds of communications, signal processing and pattern recognition.<<ETX>>","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"模式识别与人工智能","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICPR.1992.201878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 4
Abstract
Proposes the following cascade for intelligent detection of the presence of binary frequency-shift-keying (FSK) signals corrupted by additive white Gaussian noise: (1) discrete Fourier Transform (DFT) for periodogram estimation, computed at the two modulating frequencies; (2) a specific pattern recognition algorithm in the spectral space IR/sup 2/, consisting of one of the following variants: (a) perceptron; (b) fuzzy perceptron; (c) Bayes. The computer simulation results show the significant improvement of the proposed pattern recognition methods by comparison to the classical technique of detection theory by matched filter. The proposed paper tries to build a bridge between the worlds of communications, signal processing and pattern recognition.<>
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