A priori knowledge allows for clutter suppression and moving target detection to be improved. Specifically, in the Intelligent Filter and Training Selection (ITFS) approach terrain/clutter databases allow for the segmentation of terrain in Regions of Interest to be performed. This information is then used to optimize two adaptive filtering steps: the filter training strategy and the filter selection. In this paper a comparison between Knowledge-Based STAP and conventional STAP processing will be carried out. A real dataset is used to test and validate the proposed algorithm and to demonstrate the improvement with respect to conventional STAP.
Effectiveness of knowledge-based STAP in ground targets detection with real dataset
Gelli, S.;Bacci, A.;Giusti, E.;Martorella, M.;Berizzi, F.
2017-01-01
Abstract
A priori knowledge allows for clutter suppression and moving target detection to be improved. Specifically, in the Intelligent Filter and Training Selection (ITFS) approach terrain/clutter databases allow for the segmentation of terrain in Regions of Interest to be performed. This information is then used to optimize two adaptive filtering steps: the filter training strategy and the filter selection. In this paper a comparison between Knowledge-Based STAP and conventional STAP processing will be carried out. A real dataset is used to test and validate the proposed algorithm and to demonstrate the improvement with respect to conventional STAP.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
