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

Lilong Qin*, Manqing Wu, Xuan Wang, Zhen Dong

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

163 Downloads (Pure)


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.

Original languageEnglish
Article number026004
Number of pages14
Issue number2
Publication statusPublished - 1 Apr 2017
MoE publication typeA1 Journal article-refereed


  • alternating direction method of multipliers
  • generalized side-lobe canceler
  • recursive least-squares
  • space-Time adaptive processing
  • sparse representation

Fingerprint Dive into the research topics of 'Fast ℓ<sub>1</sub>-regularized space-Time adaptive processing using alternating direction method of multipliers'. Together they form a unique fingerprint.

  • Cite this