Results 241 to 250 of about 701,584 (283)

Nonlinear Filtering of Non-Gaussian Noise

Journal of Intelligent and Robotic Systems, 1997
This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes.
K. N. Plataniotis, D. Androutsos, A. N. Venetsanopoulos   +2 more
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Detection in multivariate non-Gaussian noise

IEEE Transactions on Communications, 1994
The applications of multivariate Edgeworth series and higher-order statistics to the discrete-time detection of a known constant signal in multivariate non-Gaussian noise are considered. A technique to derive suboptimum detectors from the Neyman-Pearson optimum and locally optimum detectors is described.
B.C.Y. Wong, I.F. Blake
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Recursive filtering with non-Gaussian noises

IEEE Transactions on Signal Processing, 1996
The Kalman filter is an optimal recursive filter, although its optimality can only be claimed under the Gaussian noise environment. In this paper, we consider the problem of recursive filtering with non-Gaussian noises. One of the most promising schemes, which was proposed by Masreliez (1972, 1975), uses the nonlinear score function as the correction ...
null Wen-Rong Wu, A. Kundu
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Vibration with Non-Gaussian Noise

Journal of the IEST, 2009
Three methods are introduced for generating realizations of time histories with a specified auto-spectral density while controlling the kurtosis. One of the methods also allows the skewness to be specified. A second method allows large excursions (that produce large kurtosis) to be randomly distributed or almost periodic. In addition, the second method
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Nonlinear and non-Gaussian ocean noise

The Journal of the Acoustical Society of America, 1986
Bispectral analysis is a statistical tool for detecting and identifying a nonlinear stochastic signal generating mechanism from data containing its output. Bispectral analysis can also be employed to investigate whether the observed data record is consistent with the hypothesis that the underlying stochastic process has Gaussian distribution.
Patrick L. Brockett, Melvin Hinich, Gary R. Wilson   +2 more
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Non-Gaussian noise in quantum wells

SPIE Proceedings, 2007
Gaussian generation-recombination is accepted to be a dominant mechanism of current noise source in quantum well systems biased by electric field normal to the layers. Recent experiments in n-type and p-type multiple quantum wells have revealed an additional pronouncedly non-Gaussian excess current noise with a low cut-off frequency in the kHz ...
A. Ben Simon, Y. Paltiel, G. Jung, H. Schneider   +3 more
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On non-Gaussian innovations processes for observations with non-Gaussian noise

1986 25th IEEE Conference on Decision and Control, 1986
It is well-known that for observations with additive Gaussian noise, the innovations process is a Brownian motion process which, under certain conditions, has the same information as the observation. In this paper, it is shown that for observations with non-Gaussian noise, a Brownian motion process cannot be informationally equivalent to the ...
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Signal interception in non-Gaussian noise

IEEE Transactions on Communications, 1992
The interception of weak signals in nonGaussian noise is discussed. The spectral correlation property exhibited by all cyclostationary signals is exploited to synthesize multi-cycle and single-cycle detectors which assure a superior tolerance (as compared to radiometric techniques) to one of the most challenging problems in interception, namely ...
IZZO, LUCIANO, PAURA, LUIGI, TANDA, MARIO   +2 more
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Sequential Bayesian kernel modelling with non-Gaussian noise

Neural Networks, 2008
This paper presents a sequential Bayesian approach to kernel modelling of data, which contain unusual observations and outliers. The noise is heavy tailed described as a one-dimensional mixture distribution of Gaussians. The development uses a factorised variational approximation to the posterior of all unknowns, that helps to perform tractable ...
Nikolaev, Nikolay Y., De Menezes, Lilian M.   +1 more
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DETECTION OF NON-GAUSSIAN PROCESSES IN NON GAUSSIAN NOISE

1963
Abstract : The detection of stochastic processes in noise is considered, under the assumption that neither the signal nor the noise need be Gaussian. The detector structure is found in terms of the semiinvariants of the signal and noise processes. The general detector structure is extremely complicated, but a threshold form may be obtained.
openaire   +1 more source