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基于CS的正则化稀疏度变步长自适应匹配追踪算法

刘浩强 赵洪博 冯文全

刘浩强, 赵洪博, 冯文全等 . 基于CS的正则化稀疏度变步长自适应匹配追踪算法[J]. 北京航空航天大学学报, 2017, 43(10): 2109-2117. doi: 10.13700/j.bh.1001-5965.2016.0830
引用本文: 刘浩强, 赵洪博, 冯文全等 . 基于CS的正则化稀疏度变步长自适应匹配追踪算法[J]. 北京航空航天大学学报, 2017, 43(10): 2109-2117. doi: 10.13700/j.bh.1001-5965.2016.0830
LIU Haoqiang, ZHAO Hongbo, FENG Wenquanet al. Regularized sparsity variable step-size adaptive matching pursuit algorithm for compressed sensing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 2109-2117. doi: 10.13700/j.bh.1001-5965.2016.0830(in Chinese)
Citation: LIU Haoqiang, ZHAO Hongbo, FENG Wenquanet al. Regularized sparsity variable step-size adaptive matching pursuit algorithm for compressed sensing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 2109-2117. doi: 10.13700/j.bh.1001-5965.2016.0830(in Chinese)

基于CS的正则化稀疏度变步长自适应匹配追踪算法

doi: 10.13700/j.bh.1001-5965.2016.0830
基金项目: 

国家自然科学基金 91438116

中国航天科技创新基金 2016-1-107

详细信息
    作者简介:

    刘浩强  男, 硕士研究生。主要研究方向:信号处理、压缩感知、室内定位和信息融合技术

    赵洪博  男, 博士, 讲师, 硕士生导师。主要研究方向:卫星导航、飞行器通信与测控等相关理论和关键技术

    冯文全  男, 博士, 教授, 博士生导师。主要研究方向:卫星通信与测控、卫星综合测试与仿真、卫星导航

    通讯作者:

    赵洪博, E-mail: bhzhb@126.com

  • 中图分类号: TN919.1

Regularized sparsity variable step-size adaptive matching pursuit algorithm for compressed sensing

Funds: 

National Natural Science Foundation of China 91438116

China Aerospace Science and Technology Innovation Fund 2016-1-107

More Information
  • 摘要:

    压缩感知(CS)能够突破Nyquist采样定理的瓶颈,使得高分辨率信号采集成为可能。重构算法是压缩感知中最为关键的部分,迭代贪婪算法是其中比较重要的研究方向。对压缩感知理论进行了详细分析,并在现有重构算法的基础上提出了一种新的迭代贪婪算法——正则化稀疏度变步长自适应匹配追踪(RSVssAMP)算法,可在信号稀疏度未知的情况下,结合正则化和步长自适应变化思想,快速精确地进行重构。相比于传统迭代贪婪算法,本文算法不依赖于信号稀疏度,并且应用正则化以确保选取支撑集的正确性。此外,应用自适应变化步长代替固定步长,能够提高重构速率,而且达到更高的精度。为了验证本文算法的正确性,选取高斯稀疏信号和离散稀疏信号分别进行仿真,并与现有算法进行比较。仿真结果表明,本文算法相比于现有算法可以实现更加精确快速的重构。

     

  • 图 1  正则化稀疏度变步长自适应匹配追踪算法流程图

    Figure 1.  Flowchart of regularized sparsity variable step-size adaptive matching pursuit algorithm

    图 2  不同稀疏度下重构成功率比较

    Figure 2.  Comparison of reconstruction success rate under different sparsity

    图 3  不同稀疏度下重构时间比较

    Figure 3.  Comparison of reconstruction time under different sparsity

    图 4  不同观测值下重构成功率比较

    Figure 4.  Comparison of reconstruction success rate under different observed values

    图 5  不同观测值下重构时间比较

    Figure 5.  Comparison of reconstruction time under different observed values

    图 6  不同步长下重构成功率比较

    Figure 6.  Comparison of reconstruction success rate under different step-sizes

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出版历程
  • 收稿日期:  2016-10-27
  • 录用日期:  2016-12-23
  • 刊出日期:  2017-10-20

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