ISAR target recognition based on non-negative sparse coding  

作　　者：Ning Tang Xunzhang Gao Xiang Li 

机构地区：[1]Institute of Space Electronic Technology, National University of Defense Technology, Changsha 410073, E R. China

出　　处：《Journal of Systems Engineering and Electronics》2012年第6期849-857,共9页系统工程与电子技术（英文版）

基　　金：supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)

摘　　要：Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.

关 键 词：inverse synthetic aperture radar （ISAR） PRE-PROCESSING non-negative sparse coding （NNSC） visual percep-tion target recognition. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN958[自动化与计算机技术—计算机科学与技术]