Reduced-rank sub-CPI STAP with fast convergence measure of effectiveness in nonhomogenous clutter

The fast convergence measure of effectiveness (MOE) is extremely important for space-time adaptive processing (STAP) in nonhomogeneous clutter. Therefore, the reduce-drank sub-Coherent Processing Interval (sub-CPI) STAP method is proposed to enhance the performance of convergence MOE by using the principal component analysis (PCA) in this paper. In order to maintain the fast performance of convergence MOE for subspace leakage phenomenon in nonhomogeneous clutter environment, the proposed method is modified based on the covariance matrix taper (CMT). The simulation results show that the proposed method can give faster convergence MOE and more robust performance than the conventional sub-CPI STAP method. The proposed method is more suitable for actual nonhomogeneous clutter environments.