Knowledge-Aided STAP Approach for Bistatic Ground Moving Target Imaging

Clutter heterogeneity caused by cultivation variation of the terrain properties degrades STAP detection performance. In recent years, a priori knowledge sources has been used directly and indirectly for STAP performance improvement. Monostatic radar systems are typically considered, but in the bistatic case, strong clutter non-stationarity introduced by the geometry makes convetional STAP not possible. In this case it is also very difficult to exploit a priori knowledge either directly or indirectly. In this paper an original processing chain that combines a priori knowledge with STAP filtering for detection performance improvement is proposed for bistatic geometries. Finally, ISAR processing is jointly combined with the knowledge-aided bistatic STAP to obtain focused images of non-cooperative moving targets.