A Knowledge based approach in GMTI for the estimation of the clutter covariance matrix in space time adaptive processing

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2012
Anadol, Erman
Ground Moving Target Indication (GMTI) operation relies on clutter suppression techniques for the detection of slow moving ground targets in the presence of strong radar returns from the ground. Space Time Adaptive Processing (STAP) techniques provide a means to achieve this goal by adaptively forming the clutter suppression filter, whose parameters are obtained using an estimated covariance matrix of the clutter data. Therefore, the performance of the GMTI operation is directly a ected by the performance of the estimation process mentioned above. Knowledge based techniques are applicable in applications such as the parametric estimation of the clutter covariance matrix and the estimation of the clutter covariance matrix in a nonhomogeneous clutter environment. In this study, a knowledge based approach which makes use of both a priori and instantaneous data is proposed for the mentioned estimation process. The proposed approach makes use of Shuttle Radar Topography Mission (SRTM) data as well as instantaneous platform ownship data in order to determine distributed homogeneous regions present in the region of interest; and afterwards employs Doppler Beam Sharpening (DBS) maps along with the colored loading technique for the blending process of the a priori data and the instantaneous data corresponding to the obtained homogeneous regions. A nonhomogeneity detector (NHD) is also implemented for the elimination of discrete clutter and target-like signals which may contaminate the STAP training data. Simulation results are presented for both the knowledge aided and the traditional cases. Finally, the performance of the STAP algorithm will be evaluated and compared for both cases. Results indicate that by using the developed processing approach, detection of previously undetectable targets become possible, and the overall number of false alarms is reduced.