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

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2017-04-01

Department

Department of Signal Processing and Acoustics

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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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DOI

10.1117/1.JRS.11.026004

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

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https://urn.fi/URN:NBN:fi:aalto-201705315143

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[article-cris] Sähkötekniikan korkeakoulu / ELEC