Different parts of the human body have different movements when a person is performing different physical activities. Also, there is great interest 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 (usually including the short time Fourier transform (STFT), Wigner-Ville distribution (WVD), and wavelet analysis) are not generally adaptive to nonlinear and nonstationary signals. If one can decompose the noisy baseband signal containing human Doppler information and extract only the human-induced Doppler from it, the identification of various human activities becomes easier. We therefore propose to use a recently developed method, the Hilbert-Huang transform (HHT), since it is adaptive to nonlinear and nonstationary signals. When used with noise-like radar data, it is useful for covert detection of human movement. The HHT based signal processing can effectively improve pattern recognition and reject unwanted uncorrelated noise.