基于知识的海杂波背景下距离扩展目标检测

许述文; 薛健; 水鹏朗

doi:10.11999/JEIT160905

基于知识的海杂波背景下距离扩展目标检测

doi: 10.11999/JEIT160905

基金项目:

国家自然科学基金(61201296, 61372136)，中央高校基本科研业务费(JB160224)，陕西省科协青年人才托举计划(20160205)

计量
- 文章访问数: 1171
- HTML全文浏览量: 76
- PDF下载量: 326
- 被引次数: 0
出版历程
- 收稿日期: 2016-09-07
- 修回日期: 2016-11-09
- 刊出日期: 2016-12-19

Adaptive Detection of Range-spread Targets Based on Knowledge in Sea Clutter Background

Funds:

The National Natural Science Foundation of China (61201296, 61372136), The Fundamental Research Funds for the Central Universities (JB160224), Young Talent Fund of University Association for Science and Technology in Shaanxi Province (20160205)

摘要

摘要: 该文针对在辅助数据有限的情况下自适应检测器出现检测性能损失，提出基于杂波的先验知识分布的距离扩展目标自适应检测算法。复合高斯杂波的纹理和散斑的协方差矩阵分别被建模为服从逆伽玛分布的随机变量和逆复Wishart分布的随机矩阵。利用先验知识推导了纹理分量的最大后验估计，并结合广义似然比检验设计了不依赖辅助数据的距离扩展目标自适应检测器。仿真结果表明，提出的检测器在参数失配条件下具有好的鲁棒性，而且在辅助数据有限情况下检测性能优于传统的广义似然比检测器的检测性能。
- 距离扩展目标 /
- 先验知识 /
- 复合高斯杂波 /
- 逆伽玛分布 /
- 逆复Wishart分布
Abstract: For the problem of detection performance loss of adaptive detectors on the condition that the secondary data are limited, the adaptive detection method of range-spread target based on the prior knowledge of clutter is proposed. The texture and the covariance matrix of speckle of clutter are respectively modeled as the random variable which follows the inverse Gamma distribution and the random matrix which follows the inverse complex Wishart distribution. Based on the prior knowledge, the Maximum A Posteriori (MAP) estimation of texture component is obtained and the adaptive detector of range spread target which does not need the secondary data is designed via utilizing the generalized likelihood ratio test. Finally, the detection performances of the proposed detector are evaluated and the experimental results illustrate that the proposed detector is robust in parameters mismatched situation and outperforms the conventional generalized likelihood ratio test detector for range-spread target in limited secondary data scenarios.
- Range-spread target /
- Prior knowledge /
- Compound Gaussian clutter /
- Inverse Gamma distribution /
- Inverse complex Wishart distribution

HTML全文

参考文献(21)

SHANG Xiuqin, SONG Hongjun, WANG Yu, et al. Adaptive detection of distributed targets in compound-Gaussian clutter with inverse Gamma texture[J]. Digital Signal Processing, 2012, 22(6): 1024-1030. doi: 10.1016/j.dsp.2012. 05.002.

YANG Xiaoliang, WEN Gongjian, MA Conghui, et al. CFAR detection of moving range-spread target in white Gaussian noise using waveform contrast[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 282-286. doi: 10.1109/ LGRS.2015.2511060.

KEITH I, ORELANDE M R, SUVOROVA S, et al. Parametric texture estimation and prediction using measured sea clutter data[J]. IET Radar, Sonar Navigation, 2016, 10(3): 449-458. doi: 10.1049/iet-rsn.2015.0098.

SANGSTON K J, GINI F, and GRECO M S. Adaptive detection of radar targets in compound-Gaussian clutter[C]. 2015 IEEE Radar Conference (RadarCon), Johannesburg, South Africa, 2015: 0587-0592. doi: 10.1109/RADAR.2015. 7131066.

CONTE E, BISCEGLIE M D, GALDI C, et al. A procedure for measuring the coherence length of the sea texture[J]. IEEE Transactions on Instrumentation and Measurement, 1997, 46(4): 836-841. doi: 10.1109/19.650784.

YI Lei, YAN Liang, and HAN Ning. Simulation of inverse Gaussian compound Gaussian distribution sea clutter based on SIRP[C]. Advanced Research and Technology in Industry Applications (WARTIA) of IEEE Workshop, 2014: 1026-1029. doi: 10.1109/WARTIA.2014.6976451.

ROSENBERG L and BOCQUET S. Application of the Pareto plus noise distribution to medium grazing angle sea- clutter[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(1): 255-261. doi: 10.1109/JSTARS.2014.2347957.

BOCQUET S, ROSENBERG L, and WATTS S. Simulation of coherent sea clutter with inverse Gamma texture[C]. 2014 International Radar Conference, Lille, France, 2014: 1-6. doi: 10.1109/RADAR.2014.7060347.

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.

STINCO P, GRECO M, and GINI F. Adaptive detection in compound-Gaussian clutter with inverse-gamma texture[C]. Proceedings of 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 434-437. doi: 10.1109/CIE- Radar.2011.6159570.

RICHMOND C D. Performance of a class of adaptive detection algorithms in nonhomogeneous environments[J]. IEEE Transactions on Signal Processing, 2000, 48(5): 1248-1262.doi: 10.1109/78.839973.

SVENSSON L and LUNDBERG M. Analytical expression for the posterior distribution of signals in colored Gaussian noise[C]. Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2002: 1767-1771. doi: 10.1109/ACSSC.2002. 1197078.

SVENSSON L and LUNDBERG M. On posterior distributions for signals in Gaussian noise with unknown covariance matrix[J]. IEEE Transactions on Signal Processing, 2005, 53(9): 3554-3571. doi: 10.1109/TSP.2005. 853102. doi: 10.1109/TSP.2005.853102.

谢洪森, 邹鲲. 一种非均匀场景复合高斯杂波下的自适应检测器[J]. 电子与信息学报, 2011, 33(10): 2433-2437. doi: 10.3724/SP.J.1146.2010.01412.

XIE Hongsen and ZOU Kun. Adaptive detector in compound Gaussian clutter of nonhomogenous environments[J]. Journal of Electronics Information Technology, 2011, 33(10): 2433-2437. doi: 10.3724/SP.J.1146.2010.01412.

KONG Lingjiang, LI Na, CUI Guolong, et al. Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture[J]. IET Radar, Sonar Navigation, 2016, 10(4): 689-698. doi: 10.1049/iet-rsn.2015.0241.

LI Na, CUI Guolong, YANG Haining, et al. Knowledge-aided Bayesian detection with MIMO radar in compound-Gaussian clutter[C]. IET International Radar Conference, Hangzhou, 2015: 1-6. doi: 10.1049/cp.2015.1118.

SHUAI X F, KONG L J, and YANG J Y. Performance analysis of GLRT-based adaptive detector for distributed targets in compound-Gaussian clutter[J]. Signal Processing, 2010, 90(1): 16-23. doi: 10.1016/j.sigpro.2009.05.008.

XU Shuwen, Shui Penglang, and CAO Yunhe. Adaptive range-spread maneuvering target detection in compound- Gaussian clutter[J]. Digital Signal Processing, 2015, 36(1): 46-56. doi: 10.1016/j.dsp.2014.09.010.

BENEDETTO A, BENEDETTO F, DE BLASIIC M R, et al. Reliability of signal processing technique for pavement damages detection and classification using ground penetrating radar[J]. IEEE Sensors Journal, 2005, 5(3): 471-480. doi: 10.1109/JSEN.2005.846176.

HERSELMAN P L and BAKER C J. Analysis of calibrated sea clutter and boat reflectivity data at C- and X-band in South African coastal waters[C]. IET International Conference on Radar Systems, Edinburgh, UK, 2007: 1-5. doi: 10.1049/cp:20070616.

施引文献

资源附件(0)

访问统计