Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Kernel class-wise locality preserving projection
Information Sciences: an International Journal
Modeling recognizing behavior of radar high resolution range profile using multi-agent system
WSEAS Transactions on Information Science and Applications
IEEE Transactions on Signal Processing
A Gabor atom network for signal classification with application inradar target recognition
IEEE Transactions on Signal Processing
A two-distribution compounded statistical model for Radar HRRP target recognition
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
Iterated wavelet transformation and signal discrimination for HRR radar target recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
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It is known that the subspace method is effective for radar target recognition. Its key step is to find a suitable low-dimensional subspace. In this paper, a novel subspace method, namely orthogonal kernel projecting plane (OKPP), is proposed for radar target recognition using high-resolution range profile (HRRP). The goal of OKPP is to maximize the between-class distance while minimizing the within-class distance. By introducing an orthogonality constraint into the objective function, we obtain the orthogonal basis vectors of OKPP. Comparing with the conventional kernel-based subspace methods, such as kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFDA), the nonlinear features extracted by OKPP reduce redundancy and improve the target recognition performance. The explicit expressions of the basis vectors of OKPP can be solved without using singular value decomposition (SVD) process, and thus reduces the computation complexity. The experimental results using measured data show that the proposed method has an encouraging performance.