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In this paper the cardio-respiratory signatures of human beings were studied using both an ultra-wide band (UWB) impulse radar system in a laboratory through-wall experiment and a numerical simulation using the finite difference time domain (FDTD) method. Signals from both the physical experiment and numerical simulation are processed with the Hilbert-Huang Transform (HHT), a novel signal processing approach for nonlinear and non-stationary data analysis. The results show that by using the HHT, human respiration characteristics can be successfully identified and differentiated for different subjects and a variety of respiratory statuses. However, reliable detection of cardiologic signatures requires a radar system with higher central frequency. Our results demonstrate that this combination of UWB impulse radar and HHT data processing has potential for through-wall life detection and possibly other applications.