ADMM-Based Low-Complexity Off-Grid Space-Time Adaptive Processing Methods

• Published in: IEEE access Vol. 8; pp. 206646 - 206658
• Main Authors: Li, Zhongyue, Wang, Tong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Airborne radar; Algorithms; Clutter; clutter suppression; Computational efficiency; Computing time; Covariance; Covariance matrices; Estimation; Iterative methods; Manganese; Matching pursuit algorithms; Mountains; off-grid effect; Parameters; Regularization; Space-time adaptive processing; Spacetime; sparse recovery; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3037652

In this paper, we consider the problems of off-grid effects elimination and fast implementations for sparse recovery based space-time adaptive processing (SR-STAP) methods. To improve the computational efficiency of recently proposed atomic norm minimization based space-time adaptive processing (ANM-STAP) method, we derive a fast iterative scheme by exploiting the framework of the alternating direction method of multipliers (ADMM), where the unknown parameters are iteratively updated with closed-form expressions. Furthermore, to bypass the selection of regularization parameter in ANM-STAP, we also develop two novel gridless STAP methods by utilizing the covariance fitting criterion (CFC) and the properties of the clutter plus noise matrix (CNCM). Likewise, the corresponding ADMM-based fast implementations are also derived for both CFC-based methods to reduce their computational complexities. Simulation results with both simulated and Mountain-Top data demonstrate that high computational efficiency and good performance of proposed algorithms are achieved.