• 44920118

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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
Issue number2
Publication statusPublished - 2017

    Research areas

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

ID: 44920118