Fast ℓ₁-regularized space-time adaptive processing using alternating direction method of multipliers

Abstract

, "Fast l 1 -regularized space-time adaptive processing using alternating direction method of multipliers," J. Appl. Remote Sens. 11(2), 026004 (2017), doi: 10.1117/1.JRS.11.026004. Abstract. Motivated by the sparsity of filter coefficients in full-dimension space-time adaptive processing (STAP) algorithms, this paper proposes a fast l 1 -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-toclutter-noise ratio performance than other algorithms. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.