Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing Angles
In this paper, a compound-Gaussian model (CGM) with the Nakagami-distributed textures (CGNG) is proposed to model sea clutter at medium/high grazing angles. The corresponding amplitude distributions are referred to as the CGNG distributions. The analysis of measured data shows that the CGNG distribu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/1/195 |
| _version_ | 1797358173420519424 |
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| author | Guanbao Yang Xiaojun Zhang Pengjia Zou Penglang Shui |
| author_facet | Guanbao Yang Xiaojun Zhang Pengjia Zou Penglang Shui |
| author_sort | Guanbao Yang |
| collection | DOAJ |
| description | In this paper, a compound-Gaussian model (CGM) with the Nakagami-distributed textures (CGNG) is proposed to model sea clutter at medium/high grazing angles. The corresponding amplitude distributions are referred to as the CGNG distributions. The analysis of measured data shows that the CGNG distributions can provide better goodness-of-the-fit to sea clutter at medium/high grazing angles than the four types of commonly used biparametric distributions. As a new type of amplitude distribution, its parameter estimation is important for modelling sea clutter. The estimators from the method of moments (MoM) and the [zlog(z)] estimator from the method of generalized moments are first given for the CGNG distributions. However, these estimators are sensitive to sporadic outliers of large amplitude in the data. As the second contribution of the paper, outlier-robust tri-percentile estimators of the CGNG distributions are proposed. Moreover, experimental results using simulated and measured sea clutter data are reported to show the suitability of the CGNG amplitude distributions and outlier-robustness of the proposed tri-percentile estimators. |
| first_indexed | 2024-03-08T14:58:03Z |
| format | Article |
| id | doaj.art-1a7727d514e74513907c8d112aec434b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T14:58:03Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1a7727d514e74513907c8d112aec434b2024-01-10T15:07:56ZengMDPI AGRemote Sensing2072-42922024-01-0116119510.3390/rs16010195Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing AnglesGuanbao Yang0Xiaojun Zhang1Pengjia Zou2Penglang Shui3National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaIn this paper, a compound-Gaussian model (CGM) with the Nakagami-distributed textures (CGNG) is proposed to model sea clutter at medium/high grazing angles. The corresponding amplitude distributions are referred to as the CGNG distributions. The analysis of measured data shows that the CGNG distributions can provide better goodness-of-the-fit to sea clutter at medium/high grazing angles than the four types of commonly used biparametric distributions. As a new type of amplitude distribution, its parameter estimation is important for modelling sea clutter. The estimators from the method of moments (MoM) and the [zlog(z)] estimator from the method of generalized moments are first given for the CGNG distributions. However, these estimators are sensitive to sporadic outliers of large amplitude in the data. As the second contribution of the paper, outlier-robust tri-percentile estimators of the CGNG distributions are proposed. Moreover, experimental results using simulated and measured sea clutter data are reported to show the suitability of the CGNG amplitude distributions and outlier-robustness of the proposed tri-percentile estimators.https://www.mdpi.com/2072-4292/16/1/195sea cluttercompound-Gaussian model with Nakagami-distributed textures (CGNG)CGNG distributionsmedium/high grazing anglesoutlier-robust tri-percentile estimators |
| spellingShingle | Guanbao Yang Xiaojun Zhang Pengjia Zou Penglang Shui Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing Angles Remote Sensing sea clutter compound-Gaussian model with Nakagami-distributed textures (CGNG) CGNG distributions medium/high grazing angles outlier-robust tri-percentile estimators |
| title | Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing Angles |
| title_full | Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing Angles |
| title_fullStr | Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing Angles |
| title_full_unstemmed | Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing Angles |
| title_short | Compound-Gaussian Model with Nakagami-Distributed Textures for High-Resolution Sea Clutter at Medium/High Grazing Angles |
| title_sort | compound gaussian model with nakagami distributed textures for high resolution sea clutter at medium high grazing angles |
| topic | sea clutter compound-Gaussian model with Nakagami-distributed textures (CGNG) CGNG distributions medium/high grazing angles outlier-robust tri-percentile estimators |
| url | https://www.mdpi.com/2072-4292/16/1/195 |
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