A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Radar HRRP statistical recognition based on hypersphere model
Signal Processing
A Novel Feature Vector Using Complex HRRP for Radar Target Recognition
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Complex Permittivity Estimation by Bio-inspired Algorithms for Target Identification Improvement
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Large margin nearest local mean classifier
Signal Processing
Generalized re-weighting local sampling mean discriminant analysis
Pattern Recognition
Modeling recognizing behavior of radar high resolution range profile using multi-agent system
WSEAS Transactions on Information Science and Applications
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
Density-induced margin support vector machines
Pattern Recognition
Radar target recognition based on fuzzy optimal transformation using high-resolution range profile
Pattern Recognition Letters
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In the statistical target recognition based on radar high-resolution range profile (HRRP), two challenging tasks are how to deal with the target-aspect, time-shift, and amplitude-scale sensitivity of HRRP and how to accurately describe HRRPs statistical characteristics. In this paper, based on the scattering center model, range cells are classified, in accordance with the number of predominant scatterers in each cell, into three statistical types. After resolving the three sensitivity problems, this paper develops a statistical model comprising two distribution forms, i.e., Gamma distribution and Gaussian mixture distribution, to model echoes of different types of range cells as the corresponding distribution forms. Determination of the type of a range cell is achieved by using the rival penalized competitive learning (RPCL) algorithm, while estimation for the parameters of Gamma distribution and Gaussian mixture distribution by the maximum likelihood (ML) method and the expectation-maximization (EM) algorithm, respectively. Experimental results for measured data show that the proposed statistical model not only has better recognition performance but also is more robust to noises than the two existing statistical models, i.e., Gaussian model and Gamma model.