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ADMM-Based Low-Complexity Off-Grid Space-Time Adaptive Processing Methods
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DOI：10.1109/ACCESS.2020.3037652.png)
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摘要
En 中文
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.
Keyword:
Clutter
Training
Covariance matrices
Manganese
Matching pursuit algorithms
Iterative methods
Estimation
Airborne radar
clutter suppression
off-grid effect
space-time adaptive processing
sparse recovery
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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Algorithms for simultaneous sparse approximation. Part II: Convex relaxation同时稀疏逼近的算法。第二部分: 凸松弛.png)
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