Affiliation of Author(s):自动化学院
Journal:JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
Key Words:high resolution range profile (HRRP) target recognition small sample problem feature extraction dimension reduction
Abstract: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 No.:1004-4132
Translation or Not:no
Date of Publication:2019-08-01
Co-author:Yuan Jiawen,ZHANG Gong,heshenqiang
Correspondence Author:lwb
Date of Publication:2019-08-01