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.
Highlights
Space-time adaptive processing (STAP) can effectively suppress strong ground/sea clutter and improve the moving target indication performance for airborne/spaceborne radar systems.[1]
In this study, according to the optimal criterion of minimizing the mean-square error, we propose an algorithm based on the alternating direction method of multipliers (ADMM) technique to solve the l1-regularized space-time adaptive processing (STAP) problem
The simulation parameters for the ground moving target indication application are listed in Table 2: a radar system equipped with a side-looking uniform linear array (ULA) is employed, and the elements are spaced half a wavelength apart, i.e., d 1⁄4 λ∕2
Summary
Space-time adaptive processing (STAP) can effectively suppress strong ground/sea clutter and improve the moving target indication performance for airborne/spaceborne radar systems.[1]. To reduce the computational expense and the number of training snapshots simultaneously, some typical reduced-dimension STAP algorithms have been proposed, such as the joint domain localized approach, auxiliary channel processing, etc.[6,7,8] the nonadaptive selection of the reduced-dimension projection matrix, which relies on intuitive experience, results in a performance degradation to a certain extent.[2]. The alternating direction method of multipliers (ADMM) is a technique used to combine the decomposability of dual ascent with the rapid convergence speed of the method of multipliers.[17,18] This technique is well suited for solving the optimization problems of the l1 constraint, large-scale problems.[19] The ADMM technique can converge within a few tens of iterations, which is acceptable in practical use.[20] In this study, according to the optimal criterion of minimizing the mean-square error, we propose an algorithm based on the ADMM technique to solve the l1-regularized STAP problem. EðxÞ denotes the expected value of x, jxj indicates the absolute value of x, and ðxÞþ ≜ maxð[0]; xÞ. signð·Þ is the component-wise sign function.[13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
