Investigation on MNLT Method for 3-D Correlated Map Simulation of Sea-Surface Scattering

In simulations of different kinds of radar clutter or scattering intensity distribution of terrain or sea surroundings, the memoryless nonlinear transform (MNLT) method is one of the most widely used approach for the simulation of non-Gaussian stochastic process or high-dimensional field, especially...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2018-10, Vol.15 (10), p.1595-1599
Main Authors: Wei, Peng-Bo, Zhang, Min, Jiang, Wang-Qiang, Jiao, Yong-Chang
Format: Article
Language:English
Subjects:
Clutter
Computational modeling
Correlation
Data models
Data simulation
Estimation
Gaussian process
Interpolation
Memoryless nonlinear transform (MNLT) method
Methods
Predictive models
Radar
Radar clutter
Remote sensing
Scattering
sea scattering map
Simulation
Solid modeling
space-time correlation
Spectra
Stochastic processes
Stochasticity
Temperature (air-sea)
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Summary:In simulations of different kinds of radar clutter or scattering intensity distribution of terrain or sea surroundings, the memoryless nonlinear transform (MNLT) method is one of the most widely used approach for the simulation of non-Gaussian stochastic process or high-dimensional field, especially for the requirement of high-efficiency purpose. Concerning the MNLT method, the estimation of power spectral function usually exerts a strong impact on the quality of the implementation, which is not discussed in the previous literatures. This letter gives a study on the comparison of the 3-D correlated map simulations of sea-surface scattering using different ways of power spectral function estimation in the small to large sea scattering data simulation. From comparisons of the simulation results, it is observed that the interpolation-based expanding method is the optimal way to conduct power spectral function estimation, which can make a significant contribution to the scattering predictions and remote sensing simulations concerning the large maritime scenes.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2851373