全极化高分辨雷达距离像统计识别方法

冯德军; 王雪松; 肖顺平; 王国玉

全极化高分辨雷达距离像统计识别方法

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出版历程
- 收稿日期: 2004-08-10
- 修回日期: 2004-12-20
- 刊出日期: 2006-03-19

A Statistical Identification Method of Polarimetric High Resolution Rangeprofile

摘要

摘要: 在全极化、高分辨雷达体制背景下，提出一种将极化信息与高分辨距离像相结合的雷达目标统计识别方法。采用物理统计非高斯模型对全极化高分辨距离像建模，用模型参数分别表征具有较强散射强度分辨单元的多少和强弱，将全极化下的模型参数直接作为特征矢量，并采用Parzen窗法估计模型参数的概率分布密度，用贝叶斯分类器进行分类识别。对实测的导弹类目标识别结果表明，该方法能有效克服距离像特征依赖于方位信息的缺点，达到令人满意的识别结果。
- 全极化; 一维距离像;雷达目标识别; 统计
Abstract: For the polarimetric and high-resolution radar system, a novel method is proposed, where the polarization and high resolution range profile are integrated for radar target recognition. The physical-statistical non-Gaussian model is used to model for polarimetric high resolution range profile. The model parameters describe the scattering physical characteristics of radar target and are adopted as feature vector for radar target recognition. Furthermore, Parzen window is used to estimate the probability distribution function of model parameter and the classifier is designed via Bayes theorem. The method is applied to the classification of several kinds of missile. Numerical eresults have shown that the method can extract the features which are independent to target orientation and are effective to radar target recognition.

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参考文献(1)

Li H, Yang S H. Using rangeprofiles as feature vectors to identify aerospace objects. IEEE Trans. on AP, 1993, AP-41(3): 261268. .[2]姜卫东，庄钊文，陈曾平. 基于一维距离像的目标识别方法. 现代雷达，1992, 21(1): 1922. .[3]Rothwell E J, Nyquist D P, Chen K M．A radar target discrimination scheme using the discrete wavelet transform for reduced data storage. IEEE Trans. on AP, 1994，AP-42(7): 10331037. .[4]Hurst M P, Mittra R. Scattering center analysis via Pronys method. IEEE Trans. on AP, 1987, AP-35(8): 986988. .[5]Zyweck Anthony, Bogner Robert. Rader target classification of commercial aircraft. IEEE Trans. on AES, 1996, AES-32(2): 598606. .[6]Jacobs S P, Osullivan J A. Automatic target recognition using sequences of high resolution radar range-profiles. IEEE Trans. on AES, 2000, AES-36(2): 364.381.[7]Mitchell R A.[J].Westerkamp J J. Statistical feature based HRR radar classification. the RTO SCI Symposium on Non-cooperative Air Target Identification using Radar, Mannheim, Germany.1998,:-Webb Andrew R. Gamma mixture models for target recognition. the RTO SCI Symposium on Non-cooperative Air Target Identification using Radar, Mannheim, Germany, 1998: 20-1.20-10.[8]Zebker H A, Vanzyl J J. Imaging radar polarimetry: a review. Proc. IEEE, 1991, 79(1): 15831606. .[9]许小剑, 黄培康. 防空雷达中的目标识别技术. 系统工程与电子技术，1996,18(5): 4861.[10]Steedly W M, Moses R L. High resolution exponential modeling of fully polarized radar returns. IEEE Trans. on AES, 1991, AES-27(5): 459469.[11]Middleton David. Statistical-physical models of electromagnetic interference. IEEE Trans. on EMC, 1977, EMC-19(3):106.126.[12]Berry L A. Understanding Middletons canonical formula for class noise. IEEE Trans. on EMC, 1981, EMC-23(4): 337.344.[13]Zabin S M, Poor H V. Parameter estimation for Middleton class A interference processes. IEEE Trans. on Comm, 1989, COM-37 (10): 1042.1051.[14]Zabin S M, Poor H V. Efficient estimation of class A noise parameters via the EM algorithm, IEEE Trans. on Info. Theory,1991 37 (1): 60.72.[15]Zabin S M, Poor H V. Recursive algorithms for identification of impulsive noise channels[J].IEEE Trans. on Info. Theory.1990, 36(3):559-[16]Shirman Y D, Gorshkov S A, Leshchenko S P, et al.. Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection, and Tracking. London: Aretch house, 2002: 127.179.

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