Space-time adaptive processing (STAP) is an effective tool for detecting moving target in airborne radar system.However

in actual clutter environment

the performance of conventional STAP algorithms will degrade a lot for lacking sufficient independent identically distributed training samples.By exploiting the intrinsic sparsity of the clutter distribution in the angle-Doppler domain

an algorithm called SR-STAP is proposed to obtain super-resolution space-time spectrum as well as the clutter covariance matrix with much less training samples.The results of both Mountaintop real data and simulations have proved that SR-STAP can obtain fast convergence rate and achieve better clutter suppression performance than conventional method in actual clutter scenario.