Information theory based feature selection for human activity classification with radar

The micro-Doppler signatures resulting from vibration or rotation of parts of a moving target can be used to not just classify different targets like vehicles, tanks, helicopters, or even animals, but also to classify and recognize different activities. For this purpose, a plethora of features have been extracted and proposed in the literature for classification of micro-Doppler signatures. Yet, use of all features does not guarantee the optimal classification performance. A high classification performance or success rate can be better obtained using a subset of features, which are selected among all possible features. In this thesis, this situation is demonstrated by comparing results on classification performance obtained with not only a selected subset of features, but also using all features for human activity classification. More specifically, information theory based feature selection algorithms are examined and compared for selecting features having optimal classification performance. Feature selection is considered for changing radar-target geometries aspect angle as well. Results Show that when the aspect angle is known mRMR algorithm yields higher correct classification rates, while for unknown angles MIFS-U algorithm performs better. When feature selection is used %1,1 improvement is achived over when all features are used. Work provides basis for adaptive selection of features under varying operational conditions.