A Priori Knowledge-Based Post-Doppler STAP for Traffic Monitoring with Airborne Radar

Abstract

This doctoral thesis includes an efficient data calibration algorithm that corrects the channel imbalances and the Doppler centroid variations over range and azimuth. In addition, novel and automatic training data selection strategies are presented for the clutter covariance matrix estimation, which is essential for the PD STAP processor, since it impacts directly its clutter cancellation and target detection capabilities. The novel PD STAP processor has three operational modes designed for military and civilian applications, including a fast processing mode that paves the way for real-time traffic monitoring. All modes were tested on multi-channel data acquired by DLRs airborne system F-SAR, containing scenarios with controlled vehicles and real-traffic.