Feature extraction and fusion of wide-band backscattered signals
Good discrimination results have been obtained with an active backscatter data set of minelike objects [1], where the task was 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 combing several discriminative methods with several wavelet packet representations.