基于无网格稀疏贝叶斯学习的机载双基地雷达杂波抑制方法

doi:10.12305/j.issn.1001-506X.2025.12.07

Abstract

Abstract:

Constrained by the grid mismatch problem and the requirement for convex relaxation of the l₀ norm in the solution process, all existing sparse recovery (SR) space-time adaptive processing (STAP) methods suffer from degradation in clutter suppression performance. To address the aforementioned issues, a gridless SR-STAP method based on extended Bayesian learning is proposed for the airborne bistatic radar scenario. The clutter space-time spectrum reconstruction problem is transformed into an optimization model based on gridless sparse Bayesian inference. Furthermore, a two-layer cyclic iterative strategy is designed for solution by integrating the maximization-minimization algorithm and the alternating direction method of multipliers. Simulation results demonstrate that the proposed method exhibits superior clutter suppression performance for airborne bistatic radar compared with existing methods, providing a more optimal scheme for airborne bistatic radar clutter suppression with limited training samples.

Key words: airborne bistatic radar, sparse recovery (SR), space-time adaptive processing （STAP）, off-grid, Bayesian learning

CLC Number:

TN 95

Junxian CHEN, Longfei SHI, Jialei LIU, Jiazhi MA. Clutter suppression method for airborne bistatic radar based on gridless sparse Bayesian learning[J]. Systems Engineering and Electronics, 2025, 47(12): 4057-4067.

Figures/Tables 13

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名称	数值
基线长度/km	100
发射平台飞行高度/km	3
发射平台飞行速度/(m/s)	70
接收平台飞行高度/km	3
接收平台飞行速度/(m/s)	70
发射峰值功率/kW	200
工作频率/GHz	1
脉冲重复频率/Hz	700
相参脉冲数/个	8
阵元方向图	余弦方向图
阵元方向图后向衰减系数/dB	−3
阵元增益/dB	4
阵元数/个	8
发射增益/dB	30
接收增益/dB	30
CUT双基距离和/km	80

几何配置方式	$ {\delta _T} $	$ {\delta _R} $
航向一致	0	0
航向垂直	90	0
航向并行	90	90
航向交叉	30	60

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Clutter suppression method for airborne bistatic radar based on gridless sparse Bayesian learning

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