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.
|Titolo:||Effectiveness of knowledge-based STAP in ground targets detection with real dataset|
|Anno del prodotto:||2017|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|