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 the effectiveness of GMTI radar. STAP offers the potential to detect slow moving ground vehicles that would otherwise be obscured by strong background clutter for systems employingconventional beamforming. In high target density environments, however, the radar data used to train the STAP algorithm will generally contain returns from ground vehicles causing the adaptive algorithm to cancel targets of interest as well as the groundclutter. The focus of the proposed research is to develop STAP algorithms that are robust to the problems associated with vehicles in the training data. The proposed algorithms will be tested using both ISL's site-specific radar simulation capabilitiesas well as experimental GMTI radar data. Implementation issues including computational complexity will be addressed under the proposed program."