Improving STAP performance in target rich environments through adaptive illumination

Targets in the Training Data (TTD) represent a significant source of clutter heterogeneity which reduces the Minimum Discernable Velocity (MDV) in Ground Moving Target Indication (GMTI) radar. Most techniques for dealing with TTD rely on identifying and removing heterogeneous samples (range gates) from the Training Data (TD). In target rich environments, this process can result in a drastic reduction in training data quantity and impede performance. This paper presents an alternative to TTD using a modern planar Active Electronically Scanned Array (AESA) to adaptively shape transmit antenna patterns such that transmit nulls are placed along road locations in the radar scene. It is shown that this technique effectively reduces TTD reflections and restores TD that would otherwise be removed; both the quality and quantity of available TD increases.