Training Data Selection and Update Strategies for Airborne Post-Doppler STAP

Abstract

Space-time adaptive processing (STAP) of multi-channel radar data is an established and powerful method for detecting ground moving targets, as well as for estimating their geographical positions and line-of-sight velocities. Crucial steps for practical applications are 1) the appropriate and automatic selection of the training data, and 2) the periodic update of these data to take into account the change of the clutter statistics over space and time. Improper training data and contamination by moving target signals may result in a decreased clutter suppression performance, an incorrect constant false alarm rate (CFAR) threshold and target cancellation by self-whitening. In this paper two conventional and two novel methods for training data selection are evaluated and compared using real 4-channel X-band radar data acquired with DLR’s airborne sensor F-SAR. In addition, a module for rejecting potential moving target signals and strong scatterers from the training data is proposed and discussed. All methods are evaluated for a conventional post-Doppler (PD) STAP processor and for a particular PD STAP that uses an a priori known road map.