Hilbert-Huang Transformation (HHT) is a powerful tool for nonlinear and nonstationary data analysis. In this paper, a dataset using an ultra-wide (UWB) impulse radar system with central frequency of 1 GHz was collected for life motion detection behind a cinder block wall. To extract the information of life motions such as breathing and heartbeats from the raw data, we first applied the empirical mode decomposition (EMD), the first step of HHT to decompose the signal (background signal included)

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... into a family of the intrinsic mode functions (IMFs). We then apply Hilbert spectral analysis (HSA) to get the frequency spectra of different IMFs. After dividing by the spectrum of the background radar record (equivalent to de-convolving the background record in the time domain), we found that breathing appear as a spectral peak at 0.2-0.4 Hz and heart beating appears as 1.0-1.2 Hz. This is coinciding with real condition. Our preliminary results show that the HHT technique provides significant assistance in signal processing for the detection of human targets behind opaque obstacles.