12 April 2017 Fast 1-regularized space-time adaptive processing using alternating direction method of multipliers
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J. of Applied Remote Sensing, 11(2), 026004 (2017). doi:10.1117/1.JRS.11.026004
Abstract
Motivated by the sparsity of filter coefficients in full-dimension space-time adaptive processing (STAP) algorithms, this paper proposes a fast 1-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.
Lilong Qin, Manqing Wu, Xuan Wang, Zhen Dong, "Fast 1-regularized space-time adaptive processing using alternating direction method of multipliers," Journal of Applied Remote Sensing 11(2), 026004 (12 April 2017). http://dx.doi.org/10.1117/1.JRS.11.026004
Submission: Received 21 December 2016; Accepted 24 March 2017
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KEYWORDS
Detection and tracking algorithms

Algorithms

Doppler effect

Radar

Monte Carlo methods

Computer simulations

Optimization (mathematics)

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