Demonstration of knowledge-aided space-time adaptive processing using measured airborne data

The design and analysis of a knowledge-aided detector for airborne space-time adaptive processing (STAP) applications are addressed. The proposed 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. Then, in the second stage, a data-driven selector attempts to eliminate residual non-homogeneities. The performance of this new approach is analysed using measured airborne radar data, obtained from the multi-channel airborne radar measurements program, and is compared with alternative STAP detectors proposed in the open literature.