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25 May 2005 Wavelet feature extraction of HRR radar profiles using generalized Gaussian distributions for automatic target recognition
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Abstract
We propose a WAVELET-based signal classification technique using Generalized Gaussian Distributions for obtaining the smallest set of wavelet bases required to discriminate a set of High Range Resolution (HRR) target class signals. The HRR target recognition approach utilizes a best-bases algorithm that relies on wavelets and local discriminants for feature extraction and for minimizing the dimension of the original target signal. The feature vectors are wavelet bases that capture unique information on each set of target class signals; we examined six target classes in this study. To reduce the feature space and to obtain the most salient features for classification, we used a wavelet-based feature extraction method that relies on generalized Gaussians and Principle Component Analysis techniques. The method involved accurately models the marginal distributions of the wavelet coefficients using a generalized Gaussian density (GGD). Principle Component Analysis was used to obtain the best set of super-wavelet coefficients/features comprising each target class.
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Hedley C. Morris and Monica M. De Pass "Wavelet feature extraction of HRR radar profiles using generalized Gaussian distributions for automatic target recognition", Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); https://doi.org/10.1117/12.603998
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