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

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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
    Number of pages14
    JournalJournal of Applied Remote Sensing
    Volume11
    Issue number2
    DOIs
    Publication statusPublished - 1 Apr 2017
    MoE publication typeA1 Journal article-refereed

    Keywords

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

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