2005Unpublished venueRequires access

Complex white noises and autoregressive signals

B. Picinbono, Michel Bouvet

Open publisher page 3 citations

Abstract

A real autoregressive signal is the output of a real autoregressive filter whose input is a real white noise. The same definition can be used for complex signals and systems, but the second order statistics of a complex white noise are not completely defined by its correlation function as in the real case. We present the consequences of this fact for autoregressive complex signals. In particular we show that the linear predictor of such signals is not necessarily a finite moving average filter.

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What this paper is about

A real autoregressive signal is the output of a real autoregressive filter whose input is a real white noise. The same definition can be used for complex signals and systems, but the second order statistics of a complex white noise are not completely defined by its correlation function as in the real case. We present the consequences of this fact for autoregressive complex signals. In particular we show that the linear predictor of such signals is not necessarily a finite moving average filter.

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Available abstract

A real autoregressive signal is the output of a real autoregressive filter whose input is a real white noise. The same definition can be used for complex signals and systems, but the second order statistics of a complex white noise are not completely defined by its correlation function as in the real case. We present the consequences of this fact for autoregressive complex signals. In particular we show that the linear predictor of such signals is not necessarily a finite moving average filter.

Key concepts: Autoregressive model, White noise, STAR model, Filter (signal processing), Noise (video), SIGNAL (programming language), Computer science, Mathematics

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