Abstract
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.
Highlights
Space-time adaptive processing (STAP) [1]–[6] is an effective way for clutter suppression and moving targets detection for airborne phased-array radar systems
The atomic norm minimization (ANM)-STAP method eliminates the off-grid effect, it still suffers from two shortcomings: (a) the computational complexity of CVX solver-based ANM-STAP is very high, especially for large-scale problems, which will hinder its real-time implementation, and (b) the performance of ANM-STAP is dependent on the choice of the regularization parameter, which is not easy to be set in practical applications
We provide the alternating direction method of multipliers (ADMM)-based fast implementations for the proposed covariance fitting criterion (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
Summary
Space-time adaptive processing (STAP) [1]–[6] is an effective way for clutter suppression and moving targets detection for airborne phased-array radar systems. By exploiting the intrinsic sparsity of the clutter spectrum and norm minimization-based schemes, the algorithms in [32]–[40] can improve the estimation accuracy of CNCM with a few or even one training samples This kind of on-grid approaches generally suffer from the so-called off-grid effect induced by the discretization of angle-Doppler plane. The ANM-STAP method eliminates the off-grid effect, it still suffers from two shortcomings: (a) the computational complexity of CVX solver-based ANM-STAP is very high, especially for large-scale problems, which will hinder its real-time implementation, and (b) the performance of ANM-STAP is dependent on the choice of the regularization parameter, which is not easy to be set in practical applications.
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