FEATURE EXTRACTION AND RECOGNITION FOR ECHOES OF HRR RADAR  

作　　者：Xie Deguang Zhang Xianda 

机构地区：[1]Department of Automation, Tsinghua University, Beijing 100084, China

出　　处：《Journal of Electronics(China)》2009年第6期788-793,共6页电子科学学刊（英文版）

基　　金：Supported by the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology (TNList);the Major Program of the National Natural Science Foundation of Foundation of China (No. 60496311)

摘　　要：This paper describes a novel target recognition scheme using High Range Resolution (HRR) radar signatures. AutoRegressive (AR) method is used to extract features from HRR radar echoes based on scattering center model of target. The optimal linear transformation based on Euclidian distribution distance criterion is performed on AR model parameter vectors to reduce dimension of feature vectors further and improve the class discrimination capability of feature vectors. The optimization algorithm is designed utilizing the quadratic property of criterion function and Gaussian kernel based Parzen window density function estimator. The concept of Stochastic Information Gradient (SIG) is incorporated into the gradient of cost function to decrease the computational complexity of the algorithm. Simulation results using three real airplanes,data show the effectiveness of the proposed method.This paper describes a novel target recognition scheme using High Range Resolution （HRR） radar signatures. AutoRegressive （AR） method is used to extract features from HRR radar echoes based on scattering center model of target. The optimal linear transformation based on Euclidian distribution distance criterion is performed on AR model parameter vectors to reduce dimension of feature vectors further and improve the class discrimination capability of feature vectors. The opti- mization algorithm is designed utilizing the quadratic property of criterion function and Gaussian kernel based Parzen window density function estimator. The concept of Stochastic Information Gradient （SIG） is incorporated into the gradient of cost function to decrease the computational complexity of the algorithm. Simulation results using three real airplanes, data show the effectiveness of the proposed method.

关 键 词：Radar target recognition Feature extraction AutoregRessive （AR） model Densityfunction estimation Stochastic Information Gradient （SIG） 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] P458.121.1[自动化与计算机技术—计算机科学与技术]