L1 regularized stap algorithm with a generalized sidelobe canceler architecture for airborne radar

In this paper, we propose a new l₁ regularized space-time adaptive processing (STAP) technique with a generalized sidelobe canceler (GSC) architecture for airborne phased-array radar applications. The core idea of the proposed method is imposing a sparse regularization (l₁-norm) to the minimum mean-square error (MMSE) criterion. By solving this optimization problem, the filter weight vector based on l₁-norm regularization is computed. In order to make this method practical, a l₁-based online coordinate descent (OCD) adaptive algorithm which is similar to an RLS adaptive algorithm is developed. Computational complexity analysis shows that the proposed l₁-based OCD algorithm has nearly the same cost of the full-rank RLS STAP. The simulation results show that the proposed STAP method converges at a fast speed and provides a SINR improvement over the full-rank RLS STAP.