Training Data Selection Strategy for CFAR Ship Detection in Range-Compressed Radar Data

Abstract

In the paper a novel ship detection algorithm using range-compressed airborne radar data is proposed. Ships are detected in range-Doppler domain using CFAR (constant false alarm rate) based sea clutter models. The advantage of this domain is that even ships with low radar cross sections (RCS) can be detected when they move with a certain line-of-sight (LOS) velocity so that they are shifted to the exo-clutter region. For estimating accurately the parameters of the sea clutter models and for computing a true CFAR threshold, the training data has to be selected carefully and has to be free from any targets for avoiding estimation biases. Therefore, an automatic ocean training data extraction procedure is proposed. A novel pre-detection algorithm cancels unwanted targets from the training data. The applicability and performance of the proposed algorithm are demonstrated with experimental radar data acquired with DLR’s airborne radar sensor F-SAR. Since the algorithm requires comparatively low processing effort, it is principally suitable for real time applications.