防空雷达飞机回波的扩展分形特性分析与目标分类研究  

Research on Analysis of Extending Fractal Characteristics of Aircraft Echoes and Classification of Targets in Surveillance Radars

作　　者：李秋生[1,2] 谢维信[1] 黄敬雄[1,3] 

机构地区：[1]深圳大学ATR国防科技重点实验室,广东深圳518060 [2]赣南师范学院物理与电子信息学院,江西赣州341000 [3]防空兵指挥学院,河南郑州450052

出　　处：《信号处理》2013年第9期1091-1097,共7页Journal of Signal Processing

基　　金：国家自然科学基金资助项目(61271107);国防科技预研基金资助项目(9140C800102130C8××××);高等学校博士学科点专项科研基金(博导类)资助项目(20124408110002)

摘　　要：低分辨雷达飞机回波的扩展分形特性提供了对目标回波在不同尺度下的粗糙度的精细描述,为防空雷达飞机目标的分类和识别提供了一种新的途径。该文在介绍扩展分形理论的基础上,首先利用扩展分形分析手段,对从某VHF波段防空警戒雷达上录取的飞机目标回波数据的扩展分形特性进行了分析,然后从模式识别的角度出发,提出了基于扩展分形特征的防空雷达飞机目标分类方法,最后采用不同类型飞机目标的实测回波数据进行分类识别实验,对该方法的性能进行了对比和分析。分类识别实验的结果表明,广义Hurst指数等扩展分形特征参数可以作为飞机目标分类和识别的有效特征,所提出的方法具有良好的分类识别性能,是一种有效的目标分类方法。The extending fractal characteristics of aircraft echoes from low-resolution radars offer a description of echo roughness with different scales, therefore they can provide a new way for aircraft target classification and recognition with surveillance radars. Firstly, on basis of introducing extending fractal theory, the paper analyzes the extending fractal char- acteristics of echoes from aircraft targets in a VHF-band surveillance radar by means of the extending fractal analysis. Sec- ondly, it puts forward an extending-fractal-feature-based classification method for aircraft targets with low-resolution surveil- lance radars from the viewpoint of pattern recognition. Finally, it does classification experiments with real recorded echo da- ta from different types of aircraft targets, and analyzes the performance of the proposed method. The experimental results show that the extending fractal characteristic parameters such as the generalized Hurst exponents can be used as effective features for aircraft target classification and recognition, the proposed method has a nicer classification performance, and it is an effective classification method.

关 键 词：低分辨雷达 目标分类 扩展分形 广义HURST指数 

分 类 号：TN957.51[电子电信—信号与信息处理]