Various parts of the human body have different movements when a person is performing different physical activities. There is a need to remotely detect human heartbeat and breathing for applications involving anti-terrorism and search-and-rescue. Ultrawideband noise radar systems are attractive because they are covert and immune from interference. The conventional time-frequency analyses of human activity are not generally applicable to nonlinear and nonstationary signals. If one can decompose the noisy baseband reflected signal and extract only the human-induced Doppler from it, the identification of various human activities becomes easier. We propose a nonstationary model to describe human motion and apply the Hilbert-Huang transform (HHT), which is adaptive to nonlinear and nonstationary signals, in order to analyze frequency characteristics of the baseband signal. When used with noise-like radar data, it is useful covertly identify specific human movement.