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Space-time adaptive processing (STAP) is one of the most effective approachesto suppressing ground clutters in airborne radar systems. It basically takestwo forms, i.e., full-dimension STAP (FD-STAP) and reduced-dimension STAP(RD-STAP). When the numbers of clutter training samples are less than two timestheir respective system degrees-of-freedom (DOF), the performances of bothFD-STAP and RD-STAP degrade severely due to inaccurate clutter estimation. Toenhance STAP performance under the limited training samples, this paperdevelops a STAP theory with random matrix theory (RMT). By minimizing theoutput clutter-plus-noise power, the estimate of the inversion of clutter plusnoise covariance matrix (CNCM) can be obtained through optimally manipulatingits eigenvalues, and thus producing the optimal STAP weight vector. Two STAPalgorithms, FD-STAP using RMT (RMT-FD-STAP) and RD-STAP using RMT(RMT-RD-STAP), are proposed. It is found that both RMT-FD-STAP and RMT-RD-STAPgreatly outperform other-related STAP algorithms when the numbers of trainingsamples are larger than their respective clutter DOFs, which are much less thanthe corresponding system DOFs. Theoretical analyses and simulation demonstratethe effectiveness and the performance advantages of the proposed STAPalgorithms.

Space-Time Adaptive Processing Using Random Matrix Theory Under Limited Training Samples