系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (12): 4057-4067.doi: 10.12305/j.issn.1001-506X.2025.12.07

• 传感器与信号处理 • 上一篇    

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

陈俊先, 施龙飞, 刘甲磊, 马佳智   

  1. 国防科技大学电子科学学院电子信息系统复杂电磁环境效应国家重点实验室,湖南 长沙 410073
  • 收稿日期:2024-07-23 修回日期:2024-11-23 出版日期:2025-02-27 发布日期:2025-02-27
  • 通讯作者: 施龙飞
  • 作者简介:陈俊先(2000—),男,硕士研究生,主要研究方向为新体制雷达、雷达信号处理
    刘甲磊(1994—),男,讲师,博士,主要研究方向为阵列信号处理、盲源分离
    马佳智(1987—),男,副研究员,博士,主要研究方向为雷达对抗、极化雷达信号处理

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

Junxian CHEN, Longfei SHI, Jialei LIU, Jiazhi MA   

  1. State Key laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System,College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2024-07-23 Revised:2024-11-23 Online:2025-02-27 Published:2025-02-27
  • Contact: Longfei SHI

摘要:

受制于网格失配问题与求解中对l0范数凸松弛的需求,现有各类稀疏恢复(sparse recovery, SR)空时自适应处理(space-time adaptive processing, STAP)方法杂波抑制性能均存在一定损失。针对上述问题,面向机载双基地雷达场景提出一种基于扩展贝叶斯学习的无网格SR-STAP方法,将杂波空时谱重构问题转化为基于无网格稀疏贝叶斯推断的优化模型,并基于最大化-最小化算法与交替方向乘子法设计了一种双层循环迭代策略进行求解。仿真结果表明,所提方法对机载双基地雷达杂波的抑制性能优于现有方法,可为有限训练样本下的机载双基地雷达杂波抑制提供更优方案。

关键词: 机载双基地雷达, 稀疏恢复, 空时自适应处理, 网格失配, 贝叶斯学习

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

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