Motivated by the sparsity of filter coefficients in full-dimension space-Time adaptive processing (STAP) algorithms, this paper proposes a fast ℓ1-regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the convergence and reduce the calculations. The proposed algorithm uses a splitting variable to obtain an equivalent optimization formulation, which is addressed with an augmented Lagrangian method. Using the alternating recursive algorithm, the method can rapidly result in a low minimum mean-square error without a large number of calculations. Through theoretical analysis and experimental verification, we demonstrate that the proposed algorithm provides a better output signal-To-clutter-noise ratio performance than other algorithms.
- alternating direction method of multipliers
- generalized side-lobe canceler
- recursive least-squares
- space-Time adaptive processing
- sparse representation