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

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Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2017-04-01
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Mcode
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Language
en
Pages
14
Series
JOURNAL OF APPLIED REMOTE SENSING, Volume 11, issue 2
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.
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Keywords
alternating direction method of multipliers, generalized side-lobe canceler, recursive least-squares, space-Time adaptive processing, sparse representation
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Citation
Qin , L , Wu , M , Wang , X & Dong , Z 2017 , ' Fast ℓ 1 -regularized space-Time adaptive processing using alternating direction method of multipliers ' , JOURNAL OF APPLIED REMOTE SENSING , vol. 11 , no. 2 , 026004 . https://doi.org/10.1117/1.JRS.11.026004