Abstract
In this paper, a hybrid space time adaptive processing (STAP) algorithm of direct data domain (DDD) approach and cost function reconstruction is presented to provide a solution to sample support problem at a low cost of space-time aperture loss. The correlation matrix estimated in DDD approach is partitioned into sub-matrices and two equivalent cost functions are reconstructed. By iteratively solving cost functions, sample support requirements and computational burden can be mitigated. The experiments results on the real data show that the proposed algorithm outperforms conventional DDD method and DDD-JDL with low aperture loss.
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