A new manifold learning algorithm named adaptive neighborhood preserving discriminant
projection method is proposed for the feature extraction of high-range resolution profile …

High range resolution profile (HRRP) contains important target structure signatures and is
easy to be acquired, which makes HRRP target recognition keep drawing great attention …

In this paper, a novel target recognition method, namely orthogonal maximum margin
projection subspace (OMMPS), is proposed for radar target recognition using high …

For radar automatic target recognition, the features should be extracted with sufficient target
information, high discrimination, noise robustness and low feature vector dimension. In this …

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 …

Extracting valuable and discriminative features is one of the crucial issues for target
recognition in synthetic aperture radar (SAR) images. In this study, a feature extraction …

A kernel optimization method based on fusion kernel for high-resolution range profile
(HRRP) is proposed in this paper. Based on the fusion of-norm and-norm Gaussian kernels …

Dimensionality reduction is a crucial step for pattern recognition tasks and finding a suitable
low-dimensional subspace has an important effect on recognition performance. Recently …

High-resolution range profile (HRRP) has attracted intensive attention from radar community
because it is easy to acquire and analyze. However, most of the conventional algorithms …

A new manifold learning algorithm, called kernel uncorrelated neighbourhood discriminative
embedding (KUNDE), is presented for radar target recognition. The purpose of KUNDE is to …