Demonstration of knowledge aided STAP using measured airborne data

This paper addresses the design and analysis of a Knowledge Aided (KA) detector for airborne Space Time Adaptive Processing (STAP) applications. This processor is composed of a training data selector, which chooses secondary cells best representing the clutter statistics in the cell under test, and an adaptive processor for detection processing. The data selector is a hybrid algorithm, which pre-screens training data through the use of terrain information from the United States Geological Survey (USGS). Then, in the second stage, a data driven selector attempts to eliminate residual nonhomogeneities. The performance of this new approach is analyzed using measured airborne radar data, obtained from the Multi-Channel Airborne Radar Measurements (MCARM) program, and is compared with alternative STAP detectors proposed in the open literature.