Robust approaches for joint angle/Doppler estimation for space-time adaptive processing (STAP) airborne radar in non-Gaussian clutter

This paper deals with the problem of joint angle and Doppler estimation for STAP airborne radar in non-Gaussian clutter modeled as a complex symmetric alpha-stable (SfiS) process. We propose two recursive covariance estimation approaches based on a pre-processing stage, which are robust to the heavy-tailed clutter effect: we use the truncated data in the first one and the compressed data in the second one. The estimate of the signal subspace then used to robustify the MUSIC subspace estimation method. Remarkably, simulation results show that the devised compression-based (a modified robust MUSIC) method provides higher estimation performance than the existing ones, notably in higher impulsive noise environments.