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Fast ℓ1-regularized space-Time adaptive processing using alternating direction method of multipliers. / Qin, Lilong; Wu, Manqing; Wang, Xuan; Dong, Zhen.

In: Journal of Applied Remote Sensing, Vol. 11, No. 2, 026004, 2017, p. 1-13.

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Harvard

Qin, L, Wu, M, Wang, X & Dong, Z 2017, 'Fast ℓ1-regularized space-Time adaptive processing using alternating direction method of multipliers' Journal of Applied Remote Sensing, vol. 11, no. 2, 026004, pp. 1-13. https://doi.org/10.1117/1.JRS.11.026004

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Vancouver

Author

Qin, Lilong ; Wu, Manqing ; Wang, Xuan ; Dong, Zhen. / Fast ℓ1-regularized space-Time adaptive processing using alternating direction method of multipliers. In: Journal of Applied Remote Sensing. 2017 ; Vol. 11, No. 2. pp. 1-13.

BibTeX

@article{0c3909f3e05743de987ba6cda8bd96a2,
title = "Fast ℓ1-regularized space-Time adaptive processing using alternating direction method of multipliers",
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.",
keywords = "alternating direction method of multipliers, generalized side-lobe canceler, recursive least-squares, space-Time adaptive processing, sparse representation",
author = "Lilong Qin and Manqing Wu and Xuan Wang and Zhen Dong",
year = "2017",
doi = "10.1117/1.JRS.11.026004",
language = "English",
volume = "11",
pages = "1--13",
journal = "Journal of Applied Remote Sensing",
issn = "1931-3195",
publisher = "SPIE",
number = "2",

}

RIS

TY - JOUR

T1 - Fast ℓ1-regularized space-Time adaptive processing using alternating direction method of multipliers

AU - Qin, Lilong

AU - Wu, Manqing

AU - Wang, Xuan

AU - Dong, Zhen

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - alternating direction method of multipliers

KW - generalized side-lobe canceler

KW - recursive least-squares

KW - space-Time adaptive processing

KW - sparse representation

UR - http://www.scopus.com/inward/record.url?scp=85018776026&partnerID=8YFLogxK

UR - http://resolver.tudelft.nl/uuid:0c3909f3-e057-43de-987b-a6cda8bd96a2

U2 - 10.1117/1.JRS.11.026004

DO - 10.1117/1.JRS.11.026004

M3 - Article

VL - 11

SP - 1

EP - 13

JO - Journal of Applied Remote Sensing

T2 - Journal of Applied Remote Sensing

JF - Journal of Applied Remote Sensing

SN - 1931-3195

IS - 2

M1 - 026004

ER -

ID: 44920118