{"title":"ARMA相关源分离的近似联合对角化","authors":"Saliha Meziani, A. Belouchrani, K. Abed-Meraim","doi":"10.1109/SAM48682.2020.9104225","DOIUrl":null,"url":null,"abstract":"In this paper, an Approximate Joint Diagonalization (AJD) approach is proposed to separate dependent source signals. The diagonal structure of the Auto Regressive Moving Average (ARMA) matrix coefficients moves the problem from Blind Source Separation (BSS) to AJD one. The identified matrix coefficients of the observed signal are jointly diagonalized to achieve the mixture matrix identification. Simulation results are provided to illustrate the effectiveness of the proposed approach.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Approximate Joint Diagonalization for ARMA Dependent Source Separation\",\"authors\":\"Saliha Meziani, A. Belouchrani, K. Abed-Meraim\",\"doi\":\"10.1109/SAM48682.2020.9104225\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an Approximate Joint Diagonalization (AJD) approach is proposed to separate dependent source signals. The diagonal structure of the Auto Regressive Moving Average (ARMA) matrix coefficients moves the problem from Blind Source Separation (BSS) to AJD one. The identified matrix coefficients of the observed signal are jointly diagonalized to achieve the mixture matrix identification. Simulation results are provided to illustrate the effectiveness of the proposed approach.\",\"PeriodicalId\":6753,\"journal\":{\"name\":\"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)\",\"volume\":\"1 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAM48682.2020.9104225\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAM48682.2020.9104225","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Approximate Joint Diagonalization for ARMA Dependent Source Separation
In this paper, an Approximate Joint Diagonalization (AJD) approach is proposed to separate dependent source signals. The diagonal structure of the Auto Regressive Moving Average (ARMA) matrix coefficients moves the problem from Blind Source Separation (BSS) to AJD one. The identified matrix coefficients of the observed signal are jointly diagonalized to achieve the mixture matrix identification. Simulation results are provided to illustrate the effectiveness of the proposed approach.
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