Elimination of Ground Moving Target Returns from the Training Data Set in Adaptive Radars

Space time adaptive processing (STAP) plays a very important role in theeffectiveness of GMTI radar. STAP offers the potential to detect slow movingground vehicles that would otherwise be obscured by strong background clutterfor systems employing conventional beamforming. In high target densityenvironments, however, the radar data used to train the STAP algorithm willgenerally contain returns from ground vehicles causing the adaptive algorithmto cancel targets of interest as well as the ground clutter. The focus ofthe proposed research is to develop STAP algorithms that are robust to theproblems associated with vehicles in the training data. The proposed algorithmswill be tested using ISL's site-specific radar simulation capabilitieswhich capture the effects of realistic terrain and ground traffic.Successful completion of this program will result in a suite of STAP algorithmswith improved robustness to the problems associated with GMTI radar operationin high target density environments. The ability to detect, track, and engageground vehicles in these environments will significantly enhance the effectivenessof U.S. forces on the battlefield of the future. It is envisioned that algorithmsenabling this capability will have broad application and commercializationpotential within the various DoD programs that are attempting to deliver thesecapabilities to the force. For example, opportunities lie in the existing andplanned GMTI radar programs such as JSTARS-RTIP, Global Hawk, and U2-AIP.