Off-grid STAP Algorithm Based on Reduced-Dimension Local Search Orthogonal Matching Pursuit

The influence of off-grid in sparse recovery based space-time adaptive processing (SR-STAP) can significantly degrade the performance of SR-STAP. To eliminate the effect of off-grid in SR-STAP, a off-grid STAP algorithm based on reduced-dimension (RD) local search (LS) orthogonal matching pursuit (OMP) is proposed. In the proposed algorithm, a RD STAP dictionary is designed using the clutter spectrum estimated by training samples. Then, global atoms which are matched with the clutter signal are selected from the RD STAP dictionary. In addition, in the local search steps, the local STAP dictionary is designed and the local atoms that are more matched with the clutter signal are selected from local STAP dictionary. Finally, several experiments are implemented to demonstrate the performance of the proposed algorithm, and the results show that the proposed algorithms can obtain a better performance against the off-grid problem with low computational load.