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

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

Qin, Lilong (National University of Defense Technology)
Wu, Manqing (China Electronics Technology Group Corporation)
Wang, X. (TU Delft Microwave Sensing, Signals & Systems)
Dong, Zhen (National University of Defense Technology)

2017

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.

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

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

https://doi.org/10.1117/1.JRS.11.026004

Journal of Applied Remote Sensing, 11 (2), 1-13

Institutional Repository

journal article

© 2017 Lilong Qin, Manqing Wu, X. Wang, Zhen Dong