基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

钟金荣文贡坚

钟金荣文贡坚, . 基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)[J]. 雷达学报, 2016, 5(1): 99-108. doi: 10.12000/JR15056
引用本文: 钟金荣文贡坚, . 基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)[J]. 雷达学报, 2016, 5(1): 99-108. doi: 10.12000/JR15056
Zhong Jinrong, Wen Gongjian. Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)[J]. Journal of Radars, 2016, 5(1): 99-108. doi: 10.12000/JR15056
Citation: Zhong Jinrong, Wen Gongjian. Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)[J]. Journal of Radars, 2016, 5(1): 99-108. doi: 10.12000/JR15056

基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

doi: 10.12000/JR15056

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

Funds: 

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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出版历程
  • 收稿日期:  2015-05-11
  • 修回日期:  2016-02-01

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