Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Automatic target recognition using high-resolution radar range-profiles
Automatic target recognition using high-resolution radar range-profiles
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
Radar HRRP target recognition based on higher order spectra
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
Radar high range resolution profiles feature extraction based on kernel PCA and kernel ICA
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Radar HRRP recognition based on discriminant information analysis
WSEAS Transactions on Information Science and Applications
Radar target recognition based on fuzzy optimal transformation using high-resolution range profile
Pattern Recognition Letters
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The theoretical analysis and experimental results in this paper show that the independence assumption regarding elements in a radar high-resolution range profile (HRRP) sample, under which some statistical recognition methods were proposed, is not true. In addition, geometrically speaking, target HRRP samples spread on a unit hypersphere, since L"2-normalized HRRP samples are applied to radar automatic target recognition (RATR) to deal with the amplitude-scale sensitivity problem. Therefore, this paper considers the two issues and proposes a novel statistical recognition method based on hypersphere model for power transformed HRRP samples under the jointly multivariate Gaussian distribution hypothesis. Compared with the conventional principal components analysis (PCA)-based subspace statistical recognition method, the hyperspherical spread of HRRP samples and the effectively discriminating information contained in the noise subspace can be fully utilized in this method without increasing computation complexity. The experimental results based on measured data show that our proposed method can greatly improve the recognition performance.