Neural network target classification for Concealed Weapon radar detection

The concept of Concealed Weapon and Explosive (CWE) detection by the analysis of the Late Time Response (LTR) of the complex human-CWE object in UWB Radar, has been presented in [1,2]. As the overall reflected human signal depends on the human stance and orientation with respect to the radar system, this paper investigates whether the resonant frequencies can be classified according to the illuminated simple i.e. human or complex i.e. human-CWE object. This classification yields that the human frequencies do not overlap with the CWE signature frequencies therefore the CWE frequencies can be obtained and the body-worn CWE detection is realised. The resonant frequency classification is achieved via a Learning Vector Quantization (LVQ) network.