  Space-time adaptive processing (STAP) is an effective tool for detecting a
moving target in spaceborne or airborne radar systems. Statistical-based STAP
methods generally need sufficient statistically independent and identically
distributed (IID) training data to estimate the clutter characteristics.
However, most actual clutter scenarios appear only locally stationary and lack
sufficient IID training data. In this paper, by exploiting the intrinsic
sparsity of the clutter distribution in the angle-Doppler domain, a new STAP
algorithm called SR-STAP is proposed, which uses the technique of sparse
recovery to estimate the clutter space-time spectrum. Joint sparse recovery
with several training samples is also used to improve the estimation
performance. Finally, an effective clutter covariance matrix (CCM) estimate and
the corresponding STAP filter are designed based on the estimated clutter
spectrum. Both the Mountaintop data and simulated experiments have illustrated
the fast convergence rate of this approach. Moreover, SR-STAP is less dependent
on prior knowledge, so it is more robust to the mismatch in the prior knowledge
than knowledge-based STAP methods. Due to these advantages, SR-STAP has great
potential for application in actual clutter scenarios.
