  Space-time adaptive processing (STAP) is an effective tool for detecting a
moving target in the airborne radar system. Due to the fast-changing clutter
scenario and/or non side-looking configuration, the stationarity of the
training data is destroyed such that the statistical-based methods suffer
performance degradation. Direct data domain (D3) methods avoid non-stationary
training data and can effectively suppress the clutter within the test cell.
However, this benefit comes at the cost of a reduced system degree of freedom
(DOF), which results in performance loss. In this paper, by exploiting the
intrinsic sparsity of the spectral distribution, a new direct data domain
approach using sparse representation (D3SR) is proposed, which seeks to
estimate the high-resolution space-time spectrum with only the test cell. The
simulation of both side-looking and non side-looking cases has illustrated the
effectiveness of the D3SR spectrum estimation using focal underdetermined
system solution (FOCUSS) and norm minimization. Then the clutter covariance
matrix (CCM) and the corresponding adaptive filter can be effectively obtained.
Since D3SR maintains the full system DOF, it can achieve better performance of
output signal-clutter-ratio (SCR) and minimum detectable velocity (MDV) than
current D3 methods, e.g., direct data domain least squares (D3LS). Thus D3SR is
more effective against the range-dependent clutter and interference in the
non-stationary clutter scenario.
