海杂波背景下的组合自适应GLRT-LTD

刘明; 水鹏朗

doi:10.11999/JEIT150588

海杂波背景下的组合自适应GLRT-LTD

doi: 10.11999/JEIT150588

刘明¹,
水鹏朗¹

基金项目:

国家自然科学基金(61271295)

计量
- 文章访问数: 1307
- HTML全文浏览量: 95
- PDF下载量: 587
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-18
- 修回日期: 2015-09-02
- 刊出日期: 2015-12-19

Combined Adaptive GLRT-LTD against Sea Clutter

Liu Ming¹,
Shui Peng-lang¹

Funds:

The National Natural Science Foundation of China (61271295)

摘要

摘要: 为了检测海杂波背景下的微弱运动目标，相干检测器通常需要作长时间的累积。然而，长时累积条件下的目标多普勒频率的扩散和幅度的起伏以及海杂波空间非均匀性对参考单元数目的限制导致传统的自适应检测器没法工作。注意到逆伽马(IG)纹理的复合高斯分布(CGD)可以很好地描述海杂波和目标的瞬时频率是时间的慢变函数，该文提出一种组合自适应检测器，即组合自适应广义似然比线性门限检测器(CA-GLRT-LTD)，它由自适应GLRT-LTD在几个连续的短的累积间隔上的最大响应的乘积的构成。由于GLRT-LTD对IG纹理的复合高斯杂波的最优性，该检测器相比组合自适应归一化匹配滤波(CANMF)检测器具有更好的检测性能。
- 目标检测 /
- 长时累积 /
- 逆伽马纹理 /
- 海杂波 /
- 组合自适应广义似然比线性门限检测器
Abstract: Long integration is often required to detect weak moving target in sea clutter. However, the Doppler frequency spread and amplitude fluctuation in long integration and limited reference cells resulting from spatial non-homogeneity of sea clutter make the traditional adaptive detector work badly. By observing that the Compound Gaussian Distribution (CGD) with Inverse Gamma (IG) texture gives a good fit to sea clutter and the instantaneous frequency is slowly varying, a combined adaptive detector, namely the Combined Adaptive Generalized Likelihood Ratio Test-Linear Threshold Detector (CA-GLRT-LTD), is proposed in the paper, which consists of the product of the maximal response of the adaptive GLRT-LTD in several continuous short integration intervals. Owing to the optimality of the GLRT-LTD for CG clutter with IG texture, the proposed detector obtains better performance than the Combined Adaptive Normalized Matched Filter (CANMF) detector.
- Target detection /
- Long integration /
- Inverse Gamma (IG) texture /
- Sea clutter /
- Combined Adaptive Generalized Likelihood Ratio Test-Linear Threshold Detector (CA-GLRT-LTD)

HTML全文

参考文献(20)

Moya J C, Menoyo J G, Lopez A A, et al.. Small target detection in high-resolution heterogeneous sea-clutter: an empirical analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 1880-1898.

Chen Xiao-long, Cai Yong, and Cai Fu-qing. Application of the sparse decomposition to micro-motion target detection embedded in sea clutter[C]. Proceedings of the 2013 International Conference on Radar, Adelaide, Canada, 2013: 163-166.

Chen Si-jia, Kong Ling-jiang, and Yang Jian-yu. Adaptive detection in compound-Gaussian clutter with partially correlated texture[C]. Proceedings of the 2013 IEEE Radar Conference, Ottawa, Canada, 2013: 1-5.

Chen Xiao-long, Guan Jian, He You, et al.. Detection and extraction of target with micro-motion in spiky sea clutter via short-time fractional Fourier transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1002-1018.

Ward K D, Tough 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: 230-266.

Sangston K J and Gerlach K R. Coherent detection of radar targets in a non-Gaussian background[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208-216.

Greco M V, Gini F, and Rangaswamy M. Statistical analysis of measured polarimetric clutter data at different resolutions [J]. IEE Proceedings-Radar, Sonar and Navigation, 2006, 153(6): 473-481.

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.

Zhang Yu-shi, Xu Xin-yu, Yin Ya-lei, 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.

Moya J C, Menoyo J G, Lopez A A, et al.. Statistical analysis of a high-resolution sea-clutter database[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 2024-2037.

Rangaswamy M, Weiner D, and Ozturk A. Computer generation of correlated non-Gaussian radar clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(1): 106-116.

Gini F, Greco M V, Farina A, et al.. Optimum and mismatched detection against K-distributed plus Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3): 860-876.

Yang D. Optimal coherent radar detection in a K-distributed clutter environment[J]. IEE Proceedings-Radar, Sonar and Navigation, 2012, 6(5): 283-292.

Sangston K J, Gini F, and Greco M V. Coherent radar target detection in heavy-tailed compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64-77.

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): 775-780.

Greco M V and Gini F. Cramer-Rao lower bounds for covariance matrix estimation of complex elliptically symmetric distributions[J]. IEEE Transactions on Signal Processing, 2013, 61(24): 6401-6409.

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.

Zou Kun, Liao Gui-sheng, 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.

Zhao Yi-nan, Pang Xiao-yu, and Yin Bin. Adaptive radar detection for targets in compound-Gaussian clutter with inverse gamma texture[C]. Proceedings of the IET International Radar Conference, Xian, China, 2013: 1-4.

Shui Peng-lang, Li Dong-chen, and Xu Shu-wen. Tri-feature- based detection of floating targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416-1430.

施引文献

资源附件(0)

访问统计