An efficient reduced-rank STAP based on PASTd algorithm

In this paper, the projection approximation subspace tracking deflation (PASTd) algorithm is investigated in the context of the reduced-rank STAP (RR-STAP), where a recursive approach is employed to estimate the clutter subspace. Instead of a direct use of the classic PASTd algorithm in the RRSTAP, a modified PASTd adapted to the STAP is applied. With respect to the conventional eigenvalue decomposition (ED) approach, the presented algorithm is computationally more efficient and meanwhile able to achieve comparable convergence effectiveness. The presented methodology is validated by the Monte Carlo simulation.