Topic
White noise
About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.
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Papers
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TL;DR: A set of decision criteria for identifying different types of digital modulation is developed and it is found that all modulation types of interest have been classified with success rate ≥90% at SNR = 10 dB.
242 citations
21 Jun 1998
TL;DR: In this article, an optimal two-stage identification algorithm for Hammerstein-Wiener systems is presented, which is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise.
Abstract: An optimal two stage identification algorithm is presented for Hammerstein-Wiener systems where two static nonlinear elements surround a linear block. The proposed algorithm consists of two steps: The first one is the recursive least squares and the second one is the singular value decomposition of two matrices whose dimensions are fixed and do not increase as the number of the data point increases. Moreover, the algorithm is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise.
241 citations
TL;DR: The identification problem for time-invariant single-input single-output linear stochastic systems driven by non-Gaussian white noise is considered and a least-squares criterion that involves matching the second- and the fourth-order cumulant functions of the noisy observations is proposed.
Abstract: The identification problem for time-invariant single-input single-output linear stochastic systems driven by non-Gaussian white noise is considered The system is not restricted to be minimum phase, and it is allowed to contain all-pass components A least-squares criterion that involves matching the second- and the fourth-order cumulant functions of the noisy observations is proposed Knowledge of the probability distribution of the driving noise is not required An order determination criterion that is a modification of the Akaike information criterion is also proposed Strong consistency of the proposed estimator is proved under certain sufficient conditions Simulation results are presented to illustrate the method
241 citations
TL;DR: An adaptive IF estimator with a time-varying and data-driven window length, which is able to provide quality close to what could be achieved if the smoothness of the IF were known in advance is developed.
Abstract: The estimation of the instantaneous frequency (IF) of a harmonic complex-valued signal with an additive noise using the Wigner distribution is considered. If the IF is a nonlinear function of time, the bias of the estimate depends on the window length. The optimal choice of the window length, based on the asymptotic formulae for the variance and bias, can be used in order to resolve the bias-variance tradeoff. However, the practical value of this solution is not significant because the optimal window length depends on the unknown smoothness of the IF. The goal of this paper is to develop an adaptive IF estimator with a time-varying and data-driven window length, which is able to provide quality close to what could be achieved if the smoothness of the IF were known in advance. The algorithm uses the asymptotic formula for the variance of the estimator only. Its value may be easily obtained in the case of white noise and relatively high sampling rate. Simulation shows good accuracy for the proposed adaptive algorithm.
240 citations
TL;DR: In this article, the power spectrum and the correlation of the laser Doppler velocimeter velocity signal obtained by sampling and holding the velocity at each new doppler burst are studied and the measured spectrum is filtered at the mean sample rate and it contains a filtered white noise spectrum caused by the steps in the sample and hold signal.
Abstract: The power spectrum and the correlation of the laser Doppler velocimeter velocity signal obtained by sampling and holding the velocity at each new Doppler burst are studied Theory valid for low fluctuation intensity flows shows that the measured spectrum is filtered at the mean sample rate and that it contains a filtered white noise spectrum caused by the steps in the sample and hold signal In the limit of high data density, the step noise vanishes and the sample and hold signal is statistically unbiased for any turbulence intensity
239 citations