Target identification in foliage environment using UWB radar with hybrid wavelet–ICA and SVM method

Abstract

This paper proposes a method of target identification in foliage environment, specifically to identify the number of people present in foliage environment. This method is based on the ultra-wideband (UWB) radar sensor networks (RSNs) model. UWB technology can be used for target identification and intrusion detection, we can integrate UWB-IR technology and passive radar theory directly into target detection and identification in the foliage environment through analyzing and processing of the received UWB signal. Firstly, extensive Wideband Impulse Radio (UWB-IR) was sent and received within RSNs in foliage environment. Subsequently, we used hybrid Wavelet Analysis (WA) and Independent Component Analysis (ICA) method to extract features that can reflect the scenario characteristic. Finally, those extracted features are used to train Support Vector Machine (SVM) and to classify the number of people in foliage environment. The result with an average identification rate of no less than 95% shows the effectiveness of the proposed method.