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An efficient and training-sample-reducing space-time adaptive processing (STAP) algorithm based on sparse representation for ground clutter suppression in airborne radar is proposed in this paper. First of all, the principle and problems of sample matrix inversion-based STAP and sparse representation (SR)-based STAP algorithms are reviewed. Then, the conception of the local space-time spectrum (LSTS) of clutter is considered by exploiting the intrinsic sparsity nature of clutter in local beams and the Doppler domain. To estimate the LSTS using the sparse representation technique in a cost-effective way, a variable space-time mask matrix is designed. Finally, the reduced-dimension clutter plus noise covariance clutter matrix and the corresponding adaptive weight vector are calculated based on the estimated LSTS. Numerical results with both simulated data and Mountain-Top data demonstrate that the new algorithm provides an excellent performance of clutter suppression and moving target detection with only one training range cell and significant computational savings compared with existing SR-based STAP algorithms.

Sparse Representation Based Algorithm for Airborne Radar in Beam-Space Post-Doppler Reduced-Dimension Space-Time Adaptive Processing