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

Zhongyue Li

doi:10.1109/ACCESS.2020.3037652

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

2020-01-01

Zhongyue Li

Tong Wang *

DOI：10.1109/ACCESS.2020.3037652

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Abstract

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.

Keywords:

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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