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所属单位：自动化学院
发表刊物：JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
关键字：high resolution range profile (HRRP) target recognition small sample problem feature extraction dimension reduction
摘要：With the improvement of radar resolution, the dimension of the high resolution range profile (HRRP) has increased. In order to solve the small sample problem caused by the increase of HRRP dimension, an algorithm based on kernel joint discriminant analysis (KJDA) is proposed. Compared with the traditional feature extraction methods, KJDA possesses stronger discriminative ability in the kernel feature space. K-nearest neighbor (KNN) and kernel support vector machine (KSVM) are applied as feature classifiers to verify the classification effect. Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality, and improve target recognition performance.
ISSN号：1004-4132
是否译文：否
发表时间：2019-08-01
合写作者：Yuan Jiawen,张弓,何申强
通讯作者：刘文波