基于两次分段弱选择的压缩感知子空间追踪算法

王博伟; 谭劲

doi:10.11959/j.issn.1000-0801.2020149

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基于两次分段弱选择的压缩感知子空间追踪算法

研究与开发 | 更新时间：2024-06-05

- 基于两次分段弱选择的压缩感知子空间追踪算法
- Compressed sensing subspace pursuit algorithm based on two stagewise weak selection
- 电信科学 2020年36卷第5期页码：83-92
- 作者机构：
- 作者简介：
  
  [ "王博伟（1994- ），男，中国计量大学信息工程学院硕士生，主要研究方向为物联网、无线传感网与压缩感知等" ]
  [ "谭劲（1962- ），男，博士，中国计量大学信息工程学院副教授，主要研究方向为无线网络与通信、多媒体技术" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province of China(LY16F020013)
- DOI：10.11959/j.issn.1000-0801.2020149
  中图分类号： TP391
- 网络出版日期：2020-05，
  
  纸质出版日期：2020-05-20
- 稿件说明：
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王博伟, 谭劲. 基于两次分段弱选择的压缩感知子空间追踪算法[J]. 电信科学, 2020,36(5):83-92.

Bowei WANG, Jin TAN. Compressed sensing subspace pursuit algorithm based on two stagewise weak selection[J]. Telecommunications science, 2020, 36(5): 83-92.
王博伟, 谭劲. 基于两次分段弱选择的压缩感知子空间追踪算法[J]. 电信科学, 2020,36(5):83-92. DOI： 10.11959/j.issn.1000-0801.2020149.

Bowei WANG, Jin TAN. Compressed sensing subspace pursuit algorithm based on two stagewise weak selection[J]. Telecommunications science, 2020, 36(5): 83-92. DOI： 10.11959/j.issn.1000-0801.2020149.

摘要

压缩感知是一种新的信号采样和数据压缩方式，子空间追踪算法在压缩感知重构算法中兼具较高的效率和精度，但是它需要将信号的稀疏度作为先验信息，如果稀疏度估计不够准确会降低算法重构效果。针对这个问题，提出一种基于两次分段弱选择的子空间追踪算法，它不需要预先知道信号的稀疏度，第一次弱选择自适应地选择初始原子候选集，第二次弱选择自适应地从当前原子支撑集中剔除之前可能选择的错误原子，最后通过回溯法从当前原子候选集中选择多个相关原子加入原子支撑集。仿真分析表明，该算法可以在稀疏度未知的情况下实现一维随机信号和二维图像信号的精确重构，且具有较高的稳定性；与OMP算法、SWOMP算法、BAOMP算法、SAMP算法和SP算法相比，均方误差降低了60.5%～99.1%，峰值信噪比提高了2.1%～34.3%。

Abstract

Compressed sensing is a new way of signal sampling and data compression.The subspace pursuit algorithm has higher efficiency and precision in the compressed sensing reconstruction algorithms

but it needs the sparsity of the signal as a priori information.And if the sparsity estimation is not accurate enough

it will reduce the algorithm reconstruction effect.Aiming at this problem

a two stagewise weak selection-based subspace pursuit (TSWSP) algorithm was proposed

which didn’t need to know the sparsity of the signal in advance.The first weak selection adaptively selected the initial atom candidate set

and the second weak selection adaptively culled the wrong atoms that may had been previously selected from the current atom support set

and finally it selected a plurality of related atoms from the current atom candidate set to join the atom support set by the backtracking method.Simulation analysis shows that the proposed algorithm can reconstruct one-dimensional random signals and two-dimensional image signals accurately with unknown sparsity

and it has high stability

compared with OMP

SWOMP

BAOMP

SAMP and SP algorithm

the mean-square erroris reduced by 60.5% to 99.1%

the peak signal-to-noise ratio is improved by 2.1% to 34.3%.

关键词

Keywords

references

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