{"title":"Blind system identification using fourth order spectral analysis of complex signals","authors":"C. Huet, J. Le Roux","doi":"10.1109/HOST.1997.613513","DOIUrl":null,"url":null,"abstract":"In this paper we give an analytic optimal solution to the identification problem of non minimum phase systems using the fourth order spectra. We show that this solution is in first approximation equivalent to the solution given by the well-known kurtosis maximization method. The proposed solution gives the phase of the system transfer function, the modulus can be obtained from the second order statistics. However this solution requires trispectrum phase unwrapping as the trispectrum phase is known in the interval [-/spl pi/,/spl pi/] but needs to be unwrapped in the interval [-4/spl pi/,4/spl pi/] in order to obtain the optimal solution. Therefore, we present different phase unwrapping solutions. Next, we propose a method to improve the trispectrum phase estimation using a factorizability condition. Simulation results are given and the algorithm shows good behavior even with few data.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HOST.1997.613513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper we give an analytic optimal solution to the identification problem of non minimum phase systems using the fourth order spectra. We show that this solution is in first approximation equivalent to the solution given by the well-known kurtosis maximization method. The proposed solution gives the phase of the system transfer function, the modulus can be obtained from the second order statistics. However this solution requires trispectrum phase unwrapping as the trispectrum phase is known in the interval [-/spl pi/,/spl pi/] but needs to be unwrapped in the interval [-4/spl pi/,4/spl pi/] in order to obtain the optimal solution. Therefore, we present different phase unwrapping solutions. Next, we propose a method to improve the trispectrum phase estimation using a factorizability condition. Simulation results are given and the algorithm shows good behavior even with few data.