Abstract
A parametric adaptive matched filter based on space-time scan and an improved conjugate gradient method (SCG-PAMF) is proposed. When the environment is sparse, the data from the secondary range cells is used to uniformly scan and estimate the powers in the normalized space-time 2-D frequency plane. Then, the multichannel autoregressive (AR) covariance matrix and cross-correlation vectors are reconstructed. Next, an improved conjugate gradient method is used to resolve the AR matrix coefficients with conjugate gradient method’s rapid convergence. At last, the validity and the advantages of SCG-PAMF algorithm are verified by numerical results. Compared with other conventional space-time adaptive processing (STAP) methods, SCG-PAMF algorithm has better detection performance and robustness of detection, especially, when training size is small.
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