基于复合高斯杂波纹理结构的相干检测

施赛楠; 水鹏朗; 刘明

doi:10.11999/JEIT151194

基于复合高斯杂波纹理结构的相干检测

doi: 10.11999/JEIT151194

基金项目:

国家自然科学基金(61201296)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-10-29
- 修回日期: 2016-02-17
- 刊出日期: 2016-08-19

Coherent Detection Based on Texture Structure in Compound-Gaussian Clutter

Funds:

The National Natural Science Foundation of China (61201296)

摘要

摘要: 传统的自适应检测器大多是在独立同分布纹理的前提下推导出的。然而，实测海杂波数据中各个距离单元的纹理具有相关性。该文将这一纹理相关性的信息加入到似然比检测中，提出一种基于纹理结构的相干检测器。基于涌浪调制在距离上产生纹理相关性的先验知识，确定与待检测单元纹理相关的距离单元数目，据此可以提供待测单元的纹理信息。实测数据实验表明，该检测器相对于逆伽马纹理复合高斯杂波下最优检测器具有一定的性能提升。
- 海杂波 /
- 复合高斯模型 /
- 纹理相关性 /
- 自适应检测
Abstract: Traditional adaptive detectors are mostly derived under the assumption of independent and identically distributed texture. However, the texture correlation along the range cell exists in real sea clutter datasets. A new coherent detector based on texture structure is proposed by adding the information of texture correlation into the likelihood ratio test. Based on the prior knowledge that the texture correlation along range is generated by the swell modulation, the number of range cells related to the texture of the Cell Under Test (CUT) is determined, and this number provides the information for the texture of CUT. Experimental results using real datasets show that the proposed detector has better performance in comparison with the optimal detector in compound-Gaussian clutter with inverse gamma texture.
- Sea clutter /
- Compound-Gaussian model /
- Texture correlation /
- Adaptive detection

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参考文献(21)

WARD K D, TOUG R J A, and WATTS S. Sea Clutter: Scattering, the K Distribution and Radar Performance[M]. London, United Kingdom: The Institution of Engineering and Technology, 2006: 45-142.

张玉石, 许心瑜, 尹雅磊, 等. L波段小擦地角海杂波幅度统计特性研究[J]. 电子与信息学报, 2014, 36(5): 1044-1048. doi: 10.3724/SP.J.1146.2013.01139.

ZHANG Yushi, XU Xinyu, YIN Yalei, et al. Research on amplitude statistics of L-band low grazing angle sea clutter[J]. Journal of Electronics Information Technology, 2014, 36(5): 1044-1048. doi: 10.3724/SP.J.1146.2013.01139.

SANGSTON K J, GINI F, GERCO M, et al. Structures of radar detection in compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 445-458. doi: 10.1109/7.766928.

邹鲲, 廖桂生, 李军, 等. 基于Bayes框架的复合高斯杂波下稳健检测[J]. 电子与信息学报, 2013, 35(7): 1555-1561. doi: 10.3724/SP.J.1146.2012.01333.

ZOU Kun, LIAO Guisheng, LI Jun, et al. Robust detection in compound Gaussian clutter based on Bayesian framework[J]. Journal of Electronics Information Technology, 2013, 35(7): 1555-1561. doi: 10.3724/SP.J.1146.2012.01333.

KELLY E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22(1): 115-127. doi: 10.1109/TAES.1986.310745.

ROBEY F C, FUHRMANN D R, KELLY E J, et al. A CFAR adaptive matched filter detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208-216. doi: 10.1109/7.135446.

闫亮, 孙培林, 易磊, 等. 基于逆高斯分布的复合高斯海杂波建模研究[J]. 雷达学报, 2013, 2(4): 461-465. doi: 10.3724/ SP.J.1300.2013.13083.

YAN Liang, SUN Peilin, YI Lei, et al. Modeling of compound-Gaussian sea clutter based on an inverse Gaussian distribution[J]. Journal of Radars, 2013, 2(4): 461-465. doi: 10.3724/SP.J.1300.2013.13083.

DONG Y. Optimal coherent radar detection in a K-distributed clutter environment[J]. IET Radar, Sonar Navigation, 2012, 6(5): 283-292. doi: 10.1049/iet-rsn.2011. 0273.

BALLERI A, NEHORAI A, and WANG J. Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2): 775-779. doi: 10.1109/ TAES.2007.4285370.

SANGSTON K J, GINI F, and GRECO M S. Coherent radar target detection in heavy-tailed compound-Gaussian clutter　[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64-77. doi: 10.1109/TAES.2012.6129621.

DE MAIO A, FARINA A, and FOGLIA G. Knowledge-aided Bayesian radar detectors their application to live data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 170-183. doi: 10.1109/TAES.2010.5417154.

邹鲲, 廖桂生, 李军, 等. 非高斯杂波下知识辅助检测器敏感性分析[J]. 电子与信息学报, 2014, 36(1): 181-186. doi: 10. 3724/SP.J.1146.2013.00320.

ZOU Kun, LIAO Guisheng, LI Jun, et al. Sensitivity analysis of knowledge aided detector in non-Gaussian clutter[J]. Journal of Electronics Information Technology, 2014, 36(1): 181-186. doi: 10.3724/SP.J.1146.2013.00320.

CONTE E and DE MAIO A. Mitigation techniques for non-Gaussian sea clutter[J]. IEEE Journal of Oceanic Engineering, 2004, 29(2): 284-302. doi: 10.1109/JOE.2004. 826901.

GINI F, GRECO M V, and VERRAZZANI L. Detection problem in mixed clutter environment as a Gaussian problem by adaptive preprocessing[J]. Electronics Letters, 1995, 31(14): 1189-1190. doi: 10.1049/el:19950808.

关键, 丁昊, 黄勇, 等. 实测海杂波数据空间相关性研究[J]. 电波科学学报, 2012, 27(5): 943-953. doi: 10.13443/j.cjors. 2012.05.026.

GUAN Jian, DING Hao, HUANG Yong, et al. Spatial correlation property with measured sea clutter data[J]. Chinese Journal of Radio Science, 2012, 27(5): 943-953. doi: 10.13443/j.cjors.2012.05.026.

JAVIER C M, JAVIER G M, ALVARO B C, et al. Statistical analysis of a high-resolution sea-clutter database[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 2024-2037. doi: 10.1109/TGRS.2009.2033193.

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