Space-time adaptive processing in bistatic passive radar exploiting complex Bayesian learning

In this paper, we develop a new space-time adaptive processing (STAP) technique for bistatic passive radar by exploiting clutter sparsity so as to enable effective clutter suppression with a small set of data samples. The Bayesian compressive sensing (BCS) technique is utilized for sparse clutter reconstruction, and the persymmetry property of the STAP processor is used to cast the complex sparse signal recovery problem into a group sparsity formulation. This approach provides improved recovery of the clutter and, thereby, yields better STAP performance.