The enhanced FRACTA algorithm with knowledge-aided covariance estimation

The enhanced FRACTA (FRACTA.E) algorithm has been shown to be an effective space-time adaptive processing (STAP) methodology for the airborne radar configuration in which there exists non-homogeneous clutter, jamming, and dense target clusters. In this paper, the FRACTA.E algorithm is supplemented with knowledge-aided covariance estimation (KACE) in order to reduce the required sample support, which may be necessary in severely non-homogeneous environments. The resulting algorithm is applied to the KASSPER I challenge data cube where it is shown via simulation that KACE enables FRACTA.E to achieve essentially the same level of detection performance with considerably less training data.