AE-STAP ALGORITHM FOR SPACE-TIME ANTI-JAMMING

Space-time adaptive processing (STAP) algorithms can provide effective interference suppression potential in global navigation satellite system (GNSS). However, the performance of these algorithms is limited by the training samples support in practical applications. This paper presents an effective STAP based on atoms extension (named as AE-STAP) algorithm to provide better antijamming performance even if within a very small number of snapshots. In the proposed algorithm, a spatial-temporal plane is constructed firstly by the sparsity of received signals in the spatial domain. In the plane, each grid point corresponds to a space-time steering vector, named as an atom. Then, the optimal atoms are selected by searching atoms that best match with the received signals in the spatial-temporal plane. These space-time steering vectors corresponding to the optimal atoms are used to construct the interference subspace iteratively. Finally, in order to improve the estimation accuracy of interference subspace, an atoms extension (AE) method is given by extending the optimal atoms in a diagonal manner. The STAP weight vector is obtained by projecting the snapshots on the subspace orthogonal to the interference subspace. Simulation results demonstrate that the proposed method can provide better interference suppression performance and higher output signal-to-interference-plus-noise ratios (SINRs) than the previous works.