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

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Pages	225-228
Number of pages	4
DOIs	https://doi.org/10.1109/ICCS.1994.474074
State	Published - 14 Nov 1994
Event	Proceedings of the IEEE International Conference on Communication Systems. Part 1 (of 3) - Duration: 14 Nov 1994 → 18 Nov 1994

Conference

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

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10.1109/ICCS.1994.474074

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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,

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doi = "10.1109/ICCS.1994.474074",

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AU - Wu, Wen-Rong

AU - Chen, Po Cheng

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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.

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DO - 10.1109/ICCS.1994.474074

M3 - Paper

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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

Abstract

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