Dismount modeling and detection from small aperture moving radar platforms

Future advanced radar systems must detect targets of diminishing radar cross section (RCS) at low radial velocity, in demanding clutter and interference environments. Presently, a deficiency in radar detection performance exists between the capabilities of synthetic aperture radar (SAR) for fixed target indication and space-time adaptive processing (STAP) for ground moving target indication (GMTI) of targets with low ground track velocity. Dismounts, individuals or groups running, walking, or crawling, constitute a class of targets that falls into this netherworld between SAR and STAP. While possessing low RCS levels and radial velocities, dismount detection is rendered even more challenging due to their complicated non-linear phase histories that give rise to significant micro-Doppler energies. In this paper we develop a physiological human-gait model for multi-channel moving radar platforms. We characterize the dismount detection performance of a notational UAV system using linear phase, quadratic phase and sinusoidal phase filters. Finally, we summarize our results and present areas of future work.