  Space-time adaptive processing (STAP) is one of the most effective approaches
to suppressing ground clutters in airborne radar systems. It basically takes
two 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 times
their respective system degrees-of-freedom (DOF), the performances of both
FD-STAP and RD-STAP degrade severely due to inaccurate clutter estimation. To
enhance STAP performance under the limited training samples, this paper
develops a STAP theory with random matrix theory (RMT). By minimizing the
output clutter-plus-noise power, the estimate of the inversion of clutter plus
noise covariance matrix (CNCM) can be obtained through optimally manipulating
its eigenvalues, and thus producing the optimal STAP weight vector. Two STAP
algorithms, 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-STAP
greatly outperform other-related STAP algorithms when the numbers of training
samples are larger than their respective clutter DOFs, which are much less than
the corresponding system DOFs. Theoretical analyses and simulation demonstrate
the effectiveness and the performance advantages of the proposed STAP
algorithms.
