A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling
In this paper, a novel time series heteroskedastic model is proposed for sea clutter modeling application. In the light of characteristics of the practical clutter at low grazing angle, the original generalized autoregressive conditional heteroskedasticity process, which has been widely used in vari...
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doaj-95cb09e770d3415d96c859bca7939c372021-03-29T20:39:57ZengIEEEIEEE Access2169-35362018-01-016118881189610.1109/ACCESS.2018.28053068290720A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter ModelingYunjian Zhang0https://orcid.org/0000-0002-8519-3985Hui Liu1Yanan Huang2Zhenmiao Deng3School of Information Science and Engineering, Xiamen University, Xiamen, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen, ChinaIn this paper, a novel time series heteroskedastic model is proposed for sea clutter modeling application. In the light of characteristics of the practical clutter at low grazing angle, the original generalized autoregressive conditional heteroskedasticity process, which has been widely used in various fields of economics, is extended from three aspects. First, the autoregressive moving-average terms are introduced for modeling the temporal correlation of both clutter returns and innovations. Second, the exponential of the conditional variance is generalized from one to arbitrary positive value, to capture the nonlinearity existing in the practical clutter. Third, the traditional Gaussian innovation is replaced by the Johnson Su random variable, which is a monotonic transformation of the Gaussian random variable and is capable of modeling the skewness and kurtosis. By systematically analyzing a large number of practical sea clutter data sets, we show that the proposed time series model fits the data better than some commonly used statistic-based distributions, such as the Weibull and compound Gaussian distributions.https://ieeexplore.ieee.org/document/8290720/Radarsea clutter modelingARMAGARCHJohnson <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Su</italic> distribution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yunjian Zhang Hui Liu Yanan Huang Zhenmiao Deng |
spellingShingle |
Yunjian Zhang Hui Liu Yanan Huang Zhenmiao Deng A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling IEEE Access Radar sea clutter modeling ARMA GARCH Johnson <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Su</italic> distribution |
author_facet |
Yunjian Zhang Hui Liu Yanan Huang Zhenmiao Deng |
author_sort |
Yunjian Zhang |
title |
A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling |
title_short |
A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling |
title_full |
A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling |
title_fullStr |
A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling |
title_full_unstemmed |
A Nonlinear ARMA-GARCH Model With Johnson $S_u$ Innovations and Its Application to Sea Clutter Modeling |
title_sort |
nonlinear arma-garch model with johnson $s_u$ innovations and its application to sea clutter modeling |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
In this paper, a novel time series heteroskedastic model is proposed for sea clutter modeling application. In the light of characteristics of the practical clutter at low grazing angle, the original generalized autoregressive conditional heteroskedasticity process, which has been widely used in various fields of economics, is extended from three aspects. First, the autoregressive moving-average terms are introduced for modeling the temporal correlation of both clutter returns and innovations. Second, the exponential of the conditional variance is generalized from one to arbitrary positive value, to capture the nonlinearity existing in the practical clutter. Third, the traditional Gaussian innovation is replaced by the Johnson Su random variable, which is a monotonic transformation of the Gaussian random variable and is capable of modeling the skewness and kurtosis. By systematically analyzing a large number of practical sea clutter data sets, we show that the proposed time series model fits the data better than some commonly used statistic-based distributions, such as the Weibull and compound Gaussian distributions. |
topic |
Radar sea clutter modeling ARMA GARCH Johnson <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Su</italic> distribution |
url |
https://ieeexplore.ieee.org/document/8290720/ |
work_keys_str_mv |
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