Performance of two-dimensional parametric STAP: KASSPER airborne radar data analysis

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, regulariz...

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Bibliographic Details
Main Authors: Abramovich, Y., Rangaswamy, M., Johnson, B., Corbell, P., Spencer, N.
Format: Conference Proceeding
Language:English
Subjects:
Clutter
Computational modeling
Covariance matrix
Interference
Parametric statistics
Signal to noise ratio
Training
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Summary: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.
ISSN:2155-5745
2155-5753
DOI:10.1109/IRS.2008.4585703