基于相关系数的雷达高分辨距离像分帧方法

刘敬; 张军英; 杜兰

doi:10.3724/SP.J.1146.2007.00072

基于相关系数的雷达高分辨距离像分帧方法

doi: 10.3724/SP.J.1146.2007.00072

刘敬^{① 张军英,},
张军英,
杜兰

基金项目:

国家自然科学基金(60574039，60371044)资助课题

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- 文章访问数: 3299
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- 被引次数: 0
出版历程
- 收稿日期: 2007-01-11
- 修回日期: 2007-07-18
- 刊出日期: 2008-09-19

A Frame Segmentation Method for Radar HRRPs Based on Correlation Coefficient

Liu Jing^{① 张军英
,},
Zhang Jun-Ying,
Du Lan

摘要

摘要: 高分辨距离像(HRRP)的方位敏感性造成HRRP数据是很长的统计特性连续变化的序列。从HRRP序列的统计特性出发，提出一种基于相关系数的HRRP分帧方法。外场实测数据的分帧结果表明，该方法可准确划分HRRP统计特性的变化，分帧结果与实际飞行轨迹符合；且采用时域模板匹配法(TMM)、谱域最短距离分类器和支撑向量机(SVM)分类器的识别结果均表明，相比目前基于散射点模型的均匀分帧方法，本文方法可有效提高识别率。
- 雷达自动目标识别 /
- 相关系数 /
- 方位敏感性 /
- 高分辨距离像
Abstract: Target-aspect sensitivity makes High Resolution Range Profiles (HRRP) become a long sequence with statistical characteristic changing continuously. A novel frame segmentation method based on correlation coefficient is presented according to the statistical characteristic changes of HRRP sequence. Experimental results for measured data show that the presented method can exactly divide the statistical characteristic changes of HRRPs and the resulting frames are coincident with flying trajectory. Template Matching Method (TMM) classifier in time domain, shortest distance classifier and Support Vector Machine (SVM) classifier in frequency domain are used to evaluate recognition performances. Comparing with the current uniform frame segmentation method based on scattering center model, the presented method efficiently improves the recognition rates.
- R /
- a /
- d

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参考文献(1)

[1] Webb A R. Gamma mixture models for target recognition[J].Pattern Recognition.2000, 33(12):2045-2054 [2] Copsey K and Webb A R. Bayesian Gamma mixture modelapproach to radar target recognition. IEEE Trans. on AES,2003, 39(4): 1201-1217. [3] Jacobs S P and Osollivan J A. Automatic target recognitionusing high-resolution radar range profiles. [Ph.D.], St. Louis:Washington University, 1999. [4] Van der Heiden R and Groen F C A. The box-cox metric fornearest neighbour classification improvement. PatternRecognition, 1997, 30(2): 273-279. [5] Xing Mengdao and Bao Zheng. The properties of rangeprofiles of aircraft. Chinese Journal of Electronics, 2002,11(1): 1-6. [6] Rothwell E J, Cheng K M, and Nyquist D P. Anadaptive-window-width short time Fourier transform forvisualization of radar target substructure resonances[J].IEEETrans. on Antenna and Propagation.1998, 46(9):1393-1395 [7] 廖学军. 基于高分辨距离像的雷达目标识别. [博士论文], 西安: 西安电子科技大学, 1999.Liao Xue-jun. Radar target recognition base on highresolution range profiles. [Ph.D.], Xian: Xidian university,1999. [8] Zyweck A and Bogner R E. Radar target classification ofcommercial aircraft. IEEE Trans. on AES, 1996, 32(2):598-606. [9] 裴炳南. 高分辨雷达自动目标识别方法研究. [博士论文], 西安: 西安电子科技大学, 2002.Pei Bing-nan. Study of high resolution radar automatic targetrecognition method. [Ph.D.], Xian: Xidian university, 2002. [10] 保铮，邢孟道，王彤. 雷达成像技术. 北京: 电子工业出版社,2005: 19-45.Bao Zheng, Xing Meng-dao, and Wang Tong. Radar ImagingTechnique. Beijing: Pulishing House of Electronics Inustry,2005: 19-45. [11] 杜兰，刘宏伟，保铮，等. 一种利用目标雷达高分辨距离像幅度起伏特性的特征提取新方法[J]. 电子学报，2005, 3(33):411-415.Du Lan, Liu Hong-wei, and Bao Zheng, et al.. A new featureextraction method using the amplitude fluctuation propertyof target HRRPs for radar automatic target recognition. ActaElectonica Sinica, 2005, 3(33): 411-415.

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