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- W2116720211 abstract "Consider n-dimensional observations where random signals are present or absent in independent and additive white Gaussian noise (AWGN) with standard deviation sigmaO. On the basis of recent results in statistical decision theory, this paper presents a new algorithm for estimating sigmaO when the signals are less present than absent and have unknown probability distributions. The bias, the consistency and the minimum attainable mean square estimation error of the estimator we propose are still unknown. However, experimental results are very promising. When the minimum-probability-of-error decision scheme for the non-coherent detection of modulated sinusoidal carriers in independent AWGN is tuned with the estimate instead of the true value sigmaO, the binary error rate obtained tends rapidly to the optimal error probability after a few hundred observations" @default.
- W2116720211 created "2016-06-24" @default.
- W2116720211 creator A5070406096 @default.
- W2116720211 date "2006-08-03" @default.
- W2116720211 modified "2023-10-16" @default.
- W2116720211 title "Estimating the Standard Deviation of Some Additive White Gaussian Noise on the Basis of Non Signal-Free Observations" @default.
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- W2116720211 doi "https://doi.org/10.1109/icassp.2006.1660737" @default.
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