Robust two-stage reduced-dimension STAP algorithm and its performance analysis

• 作 者: Yuanzhang Fan Yongxu Liu jianping An Xiangyuan Bu
• 机 构: School of Information and Electronic,Beijing Institute of Technology,Zhongyuan Institute of Technolo
• 出 处: Journal of Systems Engineering and Electronics
• 发表日期: 2016年05期
• 关键词: STAP clutter covariance airborne leakage poral faster normalized subspace dimens

A new two-stage reduced-dimension space-time adaptive processing(STAP) approach, which combines the subcoherent processing interval(sub-CPI) STAP and the principal component analysis(PCA), is proposed to achieve a more enhanced convergence measure of effectiveness(MOE). Furthermore, in the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper(CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.

A new two-stage reduced-dimension space-time adaptive processing (STAP) approach, which combines the subcoherent processing interval (sub-CPI) STAP and the principal component analysis (PCA), is proposed to achieve a more enhanced convergence measure of effectiveness (MOE). In the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper (CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.