基于统计区分度的SAR图像干扰评估方法

李江源王建国

doi:10.3724/SP.J.1146.2007.00903

基于统计区分度的SAR图像干扰评估方法

doi: 10.3724/SP.J.1146.2007.00903

李江源王建国

计量
- 文章访问数: 3668
- HTML全文浏览量: 92
- PDF下载量: 881
- 被引次数: 0
出版历程
- 收稿日期: 2007-06-09
- 修回日期: 2008-01-10
- 刊出日期: 2008-12-19

The Method to Evaluate SAR Jamming Based on the Statistical Difference

Li Jiang-yuan Wang Jian-guo

摘要

摘要: 针对干扰信号和目标回波信号在图像上的统计特性差异，该文提出了基于统计区分度的SAR干扰评估方法。借助于独立分量分析(ICA)，把SAR图像域上的干扰抑制问题转化为一种盲源分离问题。分别对高斯噪声干扰和类杂波干扰SAR图像进行ICA处理，并采用峭度准则进行干扰基图像分离。由于类杂波干扰信号具有和SAR回波信号类似的统计特征，相对于高斯噪声干扰的干扰抑制效果降低。理论分析和仿真验证了基于目标回波信号特征的类杂波干扰方法的有效性。
- 合成孔径雷达；独立分量分析；峭度
Abstract: In this paper, a new criterion which is based on the statistical distinction to evaluate SAR jamming effect is proposed, according to the difference of the jamming signal with the target signal. Recurring to Independent Component Analysis (ICA) method, the jamming remove issue can be translated into the blind separation problem. The white noise jamming and clutter jamming SAR images is used as the input to the ICA algorithm respectively, and make use of kurtosis to classify the basis images which span into two different signal subspaces. Because of the clutter jamming signal is similar to the target returned signal on the statistical characteristic, the effect on the jamming remove is degraded contrasted to the white noise jamming. Theory analysis and simulation validate that the jamming method based on the target signal characteristic is availability.

HTML全文

参考文献(1)

Di Bisceglie M and Galdi C. CFAR detection of extendedobjects in high-resolution SAR images [J].IEEE Trans. onGeoscience and Remote Sensing.2005, 43(4):833-843[2]Greco M S and Gini F. Statistical analysis of high-resolutionSAR ground clutter data [J].IEEE Trans. on Geoscience andRemote Sensing.2007, 45(3):566-575[3]李江源, 王建国, 杨建宇. 基于数字储频式的对SAR 类杂波干扰[J]. 电子科技大学学报, 2005, 34(6): 739-742.Li Jiang-yuan, Wang Jian-guo, and Yang Jian-yu. The similarclutters jamming to SAR based on digital frequencymemorizer [J]. Journal of the University of Electronic Scienceand Technology of China, 2005, 34(6): 739-742.[4]李江源, 王建国, 杨建宇. 基于参数引导的对宽带SAR 的类杂波干扰[J].电子与信息学报.2006, 28(10):1812-1816浏览[5]李江源, 王建国. 三种合成孔径雷达干扰方法的性能评估[J].电子与信息学报.2007, 29(11):2729-2733浏览[6]Comon P. Independent component analysisA new concept[J].Signal Processing.1994, 36(1):287-314[7]Delfosse N and Loubaton P. Adaptive blind separation ofindependent source: A deflation approach[J]. SignalProcessing, 1995, 45(1): 59-83.[8]Conte E, De Maio A, and Galdi C. Statistical analysis of realclutter at different range resolutions[J].IEEE Trans. onAerospace and Electronic Systems.2004, 40(3):903-918[9]DAgostino R B and Stephens M A. Goodness of FitTechniques. New York: Marcel Dekker [M], 1986: 321-342.[10]Cichocki A and Unbehauen R. Robust neural networks withon-line learning for blind identification and blind separationof sources [J].IEEE Trans. on Circuits and Systems I:Fundamentals Theory and Applications.1996, 43(11):894-906[11]Cichocki A and Unbehauen R. Robust learning algorithm forblind separation of signals [J].Electronics Letters.1994,30(17):1386-1387[12]Amari S, Chen T P, and Cichocki A. Stability analysis ofadaptive blind source separation[J].Neural Networks.1997,10(8):1345-1351[13]马建仓, 牛奕龙, 陈海洋. 盲信号处理[M]. 北京国防工业出版社, 2006: 93-115.

施引文献

资源附件(0)

访问统计