Training Data Selection and Update for Airborne Post-Doppler Space-Time Adaptive Processing

Kurzfassung

The training data selection and update are crucial steps for the space-time adaptive processing (STAP) operation, since contaminated training data result in a decreased clutter suppression performance, an incorrect constant false alarm rate (CFAR) threshold and target cancellation by self-whitening. This paper presents a promising algorithm that selects the training data by applying a moving window, taking into account the changes of the clutter statistics over space and time. The goal is to improve the clutter suppression capability and, thus, to increase the number of true detections. The benefits of the proposed algorithm are verified using real 4-channel X-band radar data acquired by the DLR’s airborne F-SAR.