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

Lilong Qin, Manqing Wu, Xuan Wang, Zhen Dong

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Abstract

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
Pages (from-to)1-13
Number of pages13
JournalJournal of Applied Remote Sensing
Volume11
Issue number2
DOIs
Publication statusPublished - 2017

Keywords

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

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