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.

Knowledge-Aided STAP approach for bistatic ground moving target imaging

Gelli, S.
;
Bacci, A.
;
Martorella, M.
;
Berizzi, F.
2017-01-01

Abstract

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.
2017
9783736993433
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/901707
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