基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

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基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

  • 基金项目:

Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)

  • Fund Project: The New Century Excellent Talents Supporting Plan of Ministry Education (No.NCET-11-0866)

  • 摘要: 高速采样和传输是目前雷达系统面临的一个重要挑战。针对这一问题,该文提出一种利用信号块结构特性的雷达目标压缩感知方法。该方法采用一个简单的测量矩阵对信号进行采样,然后运用块稀疏贝叶斯学习算法恢复信号。经典的块稀疏贝叶斯学习算法适用于实信号,该文将其扩为可直接处理雷达信号的复数域稀疏贝叶斯算法。相对于现有压缩感知方法,该方法不仅具有更好的信号重构精度和鲁棒性,更重要的是其压缩测量矩阵形式简单、易于硬件实现。数值仿真实验结果验证了该方法的有效性。
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  • 收稿日期:  2015-05-11
  • 录用日期:  2016-02-01
  • 刊出日期:  2016-02-28

基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

  • 1. (国防科技大学自动目标识别重点实验室 长沙 410073)
基金项目: 

摘要: 高速采样和传输是目前雷达系统面临的一个重要挑战。针对这一问题,该文提出一种利用信号块结构特性的雷达目标压缩感知方法。该方法采用一个简单的测量矩阵对信号进行采样,然后运用块稀疏贝叶斯学习算法恢复信号。经典的块稀疏贝叶斯学习算法适用于实信号,该文将其扩为可直接处理雷达信号的复数域稀疏贝叶斯算法。相对于现有压缩感知方法,该方法不仅具有更好的信号重构精度和鲁棒性,更重要的是其压缩测量矩阵形式简单、易于硬件实现。数值仿真实验结果验证了该方法的有效性。

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