Feature extraction and fusion of wideband backscattered signals
Good discrimination result have been obtained with an active backscatter data set of mine-like objects, where the task as to distinguish between man-made and non-man-made objects. In this work we introduce a novel method for constructing best basis for discrimination from wavelet packets, and demonstrate the superiority of multiple ensembles of predictors. We achieve far better discrimination results using a wide band FM sweep of 80kHz compared with the earlier work that used a 40kHz FM sweep. We further show improved results by combining several discriminative methods with several wavelet packet representations.