Performance of two-dimensional parametric STAP: KASSPER airborne radar data analysis
- Abramovich, Y.
- Rangaswamy, M.
- Johnson, B.
- Corbell, P.
- Spencer, N.
Publication date
January 2008
DOI Publisher
Institute of Electrical and Electronics Engineers (IEEE)Abstract
We investigate a new class of 2D parametric models for space-time adaptive processing (STAP) of ground clutter in airborne radar using KASSPER Dataset 1. The signal-to-interference-plus-noise (SINR) degradation with respect to the optimal receiver is analyzed for various parametric models, regularizations and training sample volumes. We show that a very small training sample volume (5-15 training range bins), with a suitable parametric model and estimation technique, can give acceptable STAP performance for the KASSPER scenario.Yuri Abramovich, Muralidhar Rangaswamy, Ben Johnson, Phillip Corbell and Nicholas Spence
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