Adaptive AR modeling in white gaussian noise

Wen-Rong Wu*, Po Cheng Chen

*此作品的通信作者

研究成果: Article同行評審

49 引文 斯高帕斯(Scopus)

摘要

Autoregressive (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 7-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 7-LMS filter. Simulations are also provided to support our theoretical results.

原文English
文章編號575693
頁(從 - 到)1184-1192
頁數9
期刊IEEE Transactions on Signal Processing
45
發行號5
DOIs
出版狀態Published - 5月 1997

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