Robust space–time adaptive processing (STAP) in non-Gaussian clutter environments

The problem of space–time adaptive processing (STAP) in non-Gaussian clutter is addressed. First, it is shown that actual ground clutter returns are heavy-tailed, and their statistics can be accurately characterized by means of alpha-stable distributions. Then, a new class of adaptive beamforming techniques is developed, based on fractional lower-order moment theory. The proposed STAP methods adjust the radar array response to a desired signal while discriminating against non-Gaussian heavy-tailed clutter modelled as a stable process. Experimental results with both simulated and actual clutter data show that the new class of STAP algorithms performs better than current gradient descent state-of-the-art methods, in localising a target both in space and Doppler, and thus offers the potential for improved airborne radar performance in STAP applications.