Adaptive AR modeling in Gaussian noise

Wen-Rong Wu; Po Cheng Chen

doi:10.1109/ICCS.1994.474074

Adaptive AR modeling in Gaussian noise

Wen-Rong Wu^*
, Po Cheng Chen

^*此作品的通信作者

電機工程學系

研究成果: Paper › 同行評審

摘要

The AR modeling is widely used in signal processing. The coefficients of AR model can be easily obtained by a LMS prediction error filter. However, it is known that such filter will give bias coefficients when the input signal is corrupted by noise. In this paper, we proposes a new type of filter for adaptive AR modeling. It is shown that the new filter can converge to the Wiener solution without bias. Simulations are provided to demonstrate the excellent results of the new filter.

原文	English
頁面	225-228
頁數	4
DOIs	https://doi.org/10.1109/ICCS.1994.474074
出版狀態	Published - 14 11月 1994
事件	Proceedings of the IEEE International Conference on Communication Systems. Part 1 (of 3) - 持續時間: 14 11月 1994 → 18 11月 1994

Conference

Conference	Proceedings of the IEEE International Conference on Communication Systems. Part 1 (of 3)
期間	14/11/94 → 18/11/94

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10.1109/ICCS.1994.474074

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@conference{4fe42ec981784613b6f35a2d7b117ab7,

title = "Adaptive AR modeling in Gaussian noise",

abstract = "The AR modeling is widely used in signal processing. The coefficients of AR model can be easily obtained by a LMS prediction error filter. However, it is known that such filter will give bias coefficients when the input signal is corrupted by noise. In this paper, we proposes a new type of filter for adaptive AR modeling. It is shown that the new filter can converge to the Wiener solution without bias. Simulations are provided to demonstrate the excellent results of the new filter.",

author = "Wen-Rong Wu and Chen, \{Po Cheng\}",

year = "1994",

month = nov,

day = "14",

doi = "10.1109/ICCS.1994.474074",

language = "English",

pages = "225--228",

note = "Proceedings of the IEEE International Conference on Communication Systems. Part 1 (of 3) ; Conference date: 14-11-1994 Through 18-11-1994",

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AU - Wu, Wen-Rong

AU - Chen, Po Cheng

PY - 1994/11/14

Y1 - 1994/11/14

N2 - The AR modeling is widely used in signal processing. The coefficients of AR model can be easily obtained by a LMS prediction error filter. However, it is known that such filter will give bias coefficients when the input signal is corrupted by noise. In this paper, we proposes a new type of filter for adaptive AR modeling. It is shown that the new filter can converge to the Wiener solution without bias. Simulations are provided to demonstrate the excellent results of the new filter.

AB - The AR modeling is widely used in signal processing. The coefficients of AR model can be easily obtained by a LMS prediction error filter. However, it is known that such filter will give bias coefficients when the input signal is corrupted by noise. In this paper, we proposes a new type of filter for adaptive AR modeling. It is shown that the new filter can converge to the Wiener solution without bias. Simulations are provided to demonstrate the excellent results of the new filter.

UR - https://www.scopus.com/pages/publications/0028737396

U2 - 10.1109/ICCS.1994.474074

DO - 10.1109/ICCS.1994.474074

M3 - Paper

AN - SCOPUS:0028737396

SP - 225

EP - 228

T2 - Proceedings of the IEEE International Conference on Communication Systems. Part 1 (of 3)

Y2 - 14 November 1994 through 18 November 1994

ER -

Adaptive AR modeling in Gaussian noise

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