系统工程与电子技术

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基于约束等距的块稀疏压缩采样匹配追踪算法

陈鹏, 王成, 孟晨   

  1. 军械工程学院导弹工程系, 河北 石家庄 050003
  • 出版日期:2015-01-28 发布日期:2010-01-03

Block compressive sampling matching pursuit based on restricted isometry property

CHEN Peng, WANG Cheng, MENG Chen   

  1. Department of Missile Engineering, Ordnance Engineering College, Shijiazhuang 050003, China
  • Online:2015-01-28 Published:2010-01-03

摘要:

为提高块稀疏信号重构算法性能,利用测量矩阵块相干特性对块稀疏约束等距常数进行估计和讨论。在此基础上,将联合子空间的分块思想引入压缩采样匹配追踪(compressive sampling matching pursuit, CoSaMP)算法,提出了基于约束等距的块稀疏压缩采样匹配追踪(block CoSaMP, BCoSaMP)算法,以子矩阵为单位更新重构支撑集,放宽了约束等距条件。在高斯随机测量矩阵条件下,证明分块尺寸越大、最优相干块更新数量在适当范围内越少,重构误差收敛性越好且信号临界稀疏比越大。最后,利用某型预警雷达多批次回波信号进行重构仿真,验证了本文算法比目前其他块稀疏重构算法具有更高的重构成功率、更优的误差稳定性和更好的应用价值。

Abstract:

In order to improve the block sparse signal recovery performance, the block restricted isometry constant(RIC)is evaluated by estimating the matrices block-coherence. On this basis, the idea of blocking in union of subspace is introduced into compressive sampling matching pursuit(CoSaMP)in this work, and block CoSaMP(BCoSaMP)is proposed. The support sets were restructured by submatrices, making the restricted isometry property(RIP)relax. It is proved that the larger the block sizes and the fewer the updating block are, the faster the convergence and the bigger the critical sparse ratio are on the conditions of Gaussian measurement matrices. Finally, the recovery simulation of warning radar multi-batches echo is operated, and the results show that a faster speed and higher error stability can be obtained when comparing with other algorithms. Moreover, it has better application value in future.