Non-Gaussian random vector identification using spherically invariant random processes

The questions connected with mathematical modeling of transformation of non-Gaussian random processes, signals and noise in linear and nonlinear systems are considered and analyzed. The mathematical transformation of random processes in linear inertial systems consisting of both series and parallel connected links, as well as positive and negative feedback is analyzed. The mathematical transformation of random processes with polygamous density of probability distribution during their passage through such systems is considered. Nonlinear inertial and non-linear systems are analyzed.

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Transformation of non-Gaussian random processes, signals and noise in the nonlinear differential systems by the method of cumulative functions

Informacionno-technologicheskij vestnik ◽

10.21499/2409-1650-2018-1-84-93 ◽

2018 ◽

Vol 15 (1) ◽

pp. 84-93

Author(s):

V. I. Volovach ◽

V. M. Artyushenko

Keyword(s):

Spectral Characteristics ◽

Random Processes ◽

Spectral Densities ◽

Differential Systems ◽

Non Gaussian ◽

Nonlinear Differential Systems

Reviewed and analyzed the issues linked with the torque and naguszewski cumulant description of random processes. It is shown that if non-Gaussian random processes are given by both instantaneous and cumulative functions, it is assumed that such processes are fully specified. Spectral characteristics of non-Gaussian random processes are considered. It is shown that higher spectral densities exist only for non-Gaussian random processes.

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Random filters which preserve the stability of random inputs

Advances in Applied Probability ◽

10.2307/1427390 ◽

1988 ◽

Vol 20 (2) ◽

pp. 275-294 ◽

Cited By ~ 1

Author(s):

Stamatis Cambanis

Keyword(s):

Transfer Function ◽

Moving Average ◽

Random Processes ◽

Time Shift ◽

Linear Filter ◽

Random Inputs ◽

The Stability ◽

Non Gaussian ◽

Random Gain

A stationary stable random processes goes through an independently distributed random linear filter. It is shown that when the input is Gaussian or harmonizable stable, then the output is also stable provided the filter&s transfer function has non-random gain. In contrast, when the input is a non-Gaussian stable moving average, then the output is stable provided the filter&s randomness is due only to a random global sign and time shift.

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Analysis and Simulation of Radar Clutter Using Spherically Invariant Random Processe

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.431 ◽

2013 ◽

Vol 765-767 ◽

pp. 431-435

Author(s):

Hong Sen Xie ◽

Jin Bo Shi ◽

Bao Kuan Luan ◽

Hua Ming Tian ◽

Peng Zhou

Keyword(s):

Correlation Function ◽

Probability Function ◽

Temporal Correlation ◽

Random Processes ◽

Spatial Correlation Function ◽

Radar Clutter ◽

Gaussian Probability Distribution ◽

Non Gaussian ◽

Simulation And Validation ◽

Temporal Correlation Function

Non-Gaussian probability distribution radar clutter not only is temporal correlated between different pulses, but also is spatial correlated between different range bins. In this paper, the method of simulation and validation of radar clutter is proposed using spherically invariant random processes (SIRP). The amplitude probability function and temporal correlation function of radar clutter can be controlled respectively, and the spatial correlation function can be also specified. The computer simulation of K-distribution and CHI-distribution radar clutter is used to validate the method, and is to validate the amplitude probability function, temporal-spatial 2D correlation function.

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