Radar target identification using various nearest neighbor techniques

Radar target identification using decision-theoretic distance based methods have long been used for classifying unknown non-cooperative radar targets using their Radar Cross Section (RCS). This study revisits this subj ect using the recently developed Large Margin Nearest Neighbor (LMNN) technique in addition to other traditional nearest neighbor methods. Radar target recognition has been defined by two performance limiting issues namely I) azimuth ambiguity (and/or erroneous estimation of target azimuth) and 2) presence of extraneous scatterers along the target. This study examines these different scenarios and highlights any of the benefits that LMNN may add to the radar target classification problem.