Adaptive wavelet classification of acoustic backscatter

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Abstract
An adaptive wavelet classifier algorithm is detailed and tested on a data set of acoustic backscatter from a metallic man-made object and from natural and synthetic specular clutter with reverberation noise. The classifier computes the locations, sizes and weights of Gaussian patches in time-scale space that contain the most discriminatory information. This new approach is shown to give higher classification rates than commonly used power spectral features. The new approach also reduces the number of free parameters in the classifier based on all wavelet features, which leads to simpler implementation for applications.

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