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dc.contributor.authorWu, WRen_US
dc.contributor.authorChen, PCen_US
dc.date.accessioned2014-12-08T15:01:47Z-
dc.date.available2014-12-08T15:01:47Z-
dc.date.issued1997-05-01en_US
dc.identifier.issn1053-587Xen_US
dc.identifier.urihttp://dx.doi.org/10.1109/78.575693en_US
dc.identifier.urihttp://hdl.handle.net/11536/549-
dc.description.abstractAutoregresssive (AR) modeling is widely used in signal processing, The coefficients of an AR model can be easily obtained with a least mean square (LMS) prediction error filter, However, it is known that this filter gives a biased solution when the input signal is corrupted by white Gaussian noise, Treichler suggested the gamma-LMS algorithm to remedy this problem and proved that the mean weight vector can converge to the Wiener solution. In this paper, we develop a new algorithm that extends works of Vijayan et al, for adaptive AR modeling in the presence of white Gaussian noise, By theoretical analysis, we show that the performance of the new algorithm is superior to the gamma-LMS filter, Simulations are also provided to support our theoretical results.en_US
dc.language.isoen_USen_US
dc.titleAdaptive AR modeling in white Gaussian noiseen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/78.575693en_US
dc.identifier.journalIEEE TRANSACTIONS ON SIGNAL PROCESSINGen_US
dc.citation.volume45en_US
dc.citation.issue5en_US
dc.citation.spage1184en_US
dc.citation.epage1192en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department電信工程研究所zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentInstitute of Communications Engineeringen_US
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