Wavelet transformation and signal discrimination for HRR radar target recognition

This paper explores the use of wavelets to improve the selection of discriminant features in the target recognition problem using High Range Resolution (HRR) radar signals in an air to air scenario. We show that there is statistically no difference between four different wavelet families in extracting discriminatory features. Since similar results can be obtained from any of the four wavelet families and wavelets within the families, the simplest wavelet (Haar) should be used. We further show that a simple box classifier can be constructed from the extracted features and that any feature that classifies four or less training signals can be removed from the classifier without a statistically significant difference in the classifier performance. We use the box classifier to select the 128 most salient pseudo range bins and then apply the wavelet transform to this reduced set of bins. We show that by iteratively applying this approach, classifier performance is improved. The number of times the feature reduction and transformation can be performed while producing improved classifier performance is small and the transformed features are shown to quickly cause the performance to approach an asymptote.