Target detection and classification by measuring and processing bistatic UWB radar signal

This paper presents a novel method to detect and classify targets obscured by foliage based on real data collected by a bistatic ultra-wideband (UWB) radar system. The type of the target which passes between the transmitter and receiver can have significant effects on the shape of the received waveform. The signal measured by the bistatic UWB radar is related to the type of the target. From these received signals, we extract features that are representative of the target types. Then, we develop target type classification and recognition algorithm based on machine learning techniques. An improved support vector machine (SVM) classifier is developed to perform target types classification and recognition. A novel chaotic differential evolution (CDE) optimization approach using tent map is adopted to determine the parameters of SVM. The effectiveness of the proposed approach is verified by experiments taken in the forest.