Meta level tracking with multimode space-time adaptive processing of GMTI data

Ground surveillance of the battlefield provides military analysts with information that is critical to the success of a mission; the type of the information includes the enemy force structure, enemy offensive combat formation, and maneuvering events. The conventional approach uses mainly the synthetic aperture radar (SAR) and electro-optical (EO) sensors to perform detection and identification of stationary targets on the battlefield. Ground moving target indicator (GMTI) radar with space-time adaptive processing (STAP), on the other hand, allows a more complete perception of the battlefield by adding the capability to detect moving objects over a large area. In particular, the simultaneous detection and estimation of angular location of a ground moving target via adaptive cancellation of ground clutter is demonstrated, where a single reflector antenna with a multimode feedhorn is used in a GMTI radar. Based on the GMTI radar output, we illustrate the use of stochastic parsing algorithm with stochastic context free grammar (SCFG) as an unifying framework for data association, target tracking, and situation awareness.