Unbiased LMS filtering in the presence of white measurement noise with unknown power (Q2732955)
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scientific article; zbMATH DE number 1632324
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Unbiased LMS filtering in the presence of white measurement noise with unknown power |
scientific article; zbMATH DE number 1632324 |
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Unbiased LMS filtering in the presence of white measurement noise with unknown power (English)
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2 December 2001
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autoregressive model
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adaptive filtering
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measurement noise
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unbiased estimation
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autoregressive models
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first order filter
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The paper presents an algorithm for asymptotically unbiased estimation of the parameters of autoregressive models when the observations are contaminated by additive white noise. The authors propose to apply a first order filter to the observed process and use the resulting process to estimate the noise-induced bias in the estimators of the parameters. The estimated bias then can be subtracted from the standard estimates to achieve unbiasedness.
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0.802866518497467
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0.7969933152198792
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0.796186625957489
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0.7903852462768555
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