Results 221 to 230 of about 146,825 (261)

Control for nonlinear system with non-Gaussian noise

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017
In this paper, a new nonlinear loop transfer recovery control method is proposed for nonlinear systems with non-Gaussian noise. For the proposed control scheme, a dynamical feedback and an optimized cubature Kalamn filter is combined. With such method, it is shown that the deviation of the set point and the system's output could be amended by the ...
Shuang Zhang, Juan Chen, Yakun Yu
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Detection of weak signals in non-Gaussian noise

IEEE Transactions on Information Theory, 1981
A locally optimum detector structure is derived for the detection of weak signals in non-Gaussian environments. Optimum performance is obtained by employing a zero-memory nonlinearity prior to the matched filter that would be optimum for detecting the signal were the noise Gaussian.
Ning Hsing Lu, Bruce A. Eisenstein
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Arrival angle estimation in non-Gaussian noise

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, 2002
This paper considers the problem of estimating the direction of arrival of plane waves from sensor array data. It is shown through a multivariate generalization of Whittle's inequality that the least squares estimator and its subspace-based approximations are inefficient when the noise distribution is non-Gaussian.
Debasis Sengupta, Sarbani Palit
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Polarimetric adaptive detection in non-Gaussian noise

Signal Processing, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Antonio De Maio, Giuseppa Alfano
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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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Adaptive Filtering of Non-Gaussian Flicker Noise

2020 9th Mediterranean Conference on Embedded Computing (MECO), 2020
The article deals with ultra-low-power signals and methods of its receiving and processing in the information transmission systems applied to the systems of the Internet of things (IoT). High requirements for energy efficiency in IoT systems and a low information transmission rate leads to implementation of ultra-narrow band signals, which may be ...
Alexander Parshin, Yuri Parshin
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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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Local spectrum sensing in non-Gaussian noise

2010 17th International Conference on Telecommunications, 2010
This paper deals with the local spectrum sensing problem in non-Gaussian noise. It is of great importance to reliably detect the presence of licensed users in cognitive radio. This task highly depends on the noise distribution, thus it is important to characterize the noise behavior as best as possible.
Sayed Jalal Zahabi, AliAkbar Tadaion
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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.
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Robust subspace estimation in non-Gaussian noise

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002
Subspace methods are common in array processing, but standard schemes typically perform poorly when the noise is non-Gaussian and/or impulsive. Zero-memory nonlinear (ZMNL) functions may be applied to limit the influence of impulsive noise, but ZMNL pre-processing generally destroys the low-rank signal subspace.
Richard J. Kozick, Brian M. Sadler
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