Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea Clutter
The non-Gaussian nature of radar-observed clutter echoes induces performance degradation in the context of remote sensing target detection when using conventional Gaussian detectors. To enhance target detection performance, this study addresses the issue of adaptive detection in nonzero-mean non-Gau...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1696 |
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| author | Haoqi Wu Hongzhi Guo Zhihang Wang Zishu He |
| author_facet | Haoqi Wu Hongzhi Guo Zhihang Wang Zishu He |
| author_sort | Haoqi Wu |
| collection | DOAJ |
| description | The non-Gaussian nature of radar-observed clutter echoes induces performance degradation in the context of remote sensing target detection when using conventional Gaussian detectors. To enhance target detection performance, this study addresses the issue of adaptive detection in nonzero-mean non-Gaussian sea clutter environments. The nonzero-mean compound Gaussian model, composed of the texture and complex Gaussian speckle, is utilized to capture the sea clutter. Further, we adopt the inverse Gamma, Gamma, and inverse Gaussian distributions to characterize the texture component. Novel adaptive detectors based on the two-step Rao and Wald tests, taking advantage of the maximum a posteriori (MAP) method to estimate textures, are designed. More specifically, test statistics of the proposed Rao- and Wald-based detectors are derived by assuming the speckle covariance matrix (CM), mean vector (MV), and clutter texture in the first step. Then, the sea clutter parameters assumed to be known are replaced with their estimations, and fully adaptive detectors are obtained. The Monte Carlo performance evaluation experiments using both simulated and measured sea clutter data are conducted, and numerical results validate the constant false alarm rate (CFAR) properties and detection performance of the proposed nonzero-mean detectors. Additionally, the proposed Rao and Wald detectors, respectively, show strong robustness and good selectivity for mismatch signals. |
| format | Article |
| id | doaj-art-dc076ed885e641ea87852e22433210b8 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dc076ed885e641ea87852e22433210b82025-08-20T01:56:42ZengMDPI AGRemote Sensing2072-42922025-05-011710169610.3390/rs17101696Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea ClutterHaoqi Wu0Hongzhi Guo1Zhihang Wang2Zishu He3The School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe non-Gaussian nature of radar-observed clutter echoes induces performance degradation in the context of remote sensing target detection when using conventional Gaussian detectors. To enhance target detection performance, this study addresses the issue of adaptive detection in nonzero-mean non-Gaussian sea clutter environments. The nonzero-mean compound Gaussian model, composed of the texture and complex Gaussian speckle, is utilized to capture the sea clutter. Further, we adopt the inverse Gamma, Gamma, and inverse Gaussian distributions to characterize the texture component. Novel adaptive detectors based on the two-step Rao and Wald tests, taking advantage of the maximum a posteriori (MAP) method to estimate textures, are designed. More specifically, test statistics of the proposed Rao- and Wald-based detectors are derived by assuming the speckle covariance matrix (CM), mean vector (MV), and clutter texture in the first step. Then, the sea clutter parameters assumed to be known are replaced with their estimations, and fully adaptive detectors are obtained. The Monte Carlo performance evaluation experiments using both simulated and measured sea clutter data are conducted, and numerical results validate the constant false alarm rate (CFAR) properties and detection performance of the proposed nonzero-mean detectors. Additionally, the proposed Rao and Wald detectors, respectively, show strong robustness and good selectivity for mismatch signals.https://www.mdpi.com/2072-4292/17/10/1696compound Gaussian distributionnonzero-meanRao testtarget detectionWald test |
| spellingShingle | Haoqi Wu Hongzhi Guo Zhihang Wang Zishu He Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea Clutter Remote Sensing compound Gaussian distribution nonzero-mean Rao test target detection Wald test |
| title | Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea Clutter |
| title_full | Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea Clutter |
| title_fullStr | Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea Clutter |
| title_full_unstemmed | Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea Clutter |
| title_short | Rao and Wald Tests in Nonzero-Mean Non–Gaussian Sea Clutter |
| title_sort | rao and wald tests in nonzero mean non gaussian sea clutter |
| topic | compound Gaussian distribution nonzero-mean Rao test target detection Wald test |
| url | https://www.mdpi.com/2072-4292/17/10/1696 |
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