Robust Sparse Recovery Based on Approximate l <sub>0</sub> Norm

WANG Jun-hua; HUANG Zhi-tao; ZHOU Yi-yu; WANG Feng-hua

doi:10.3969/j.issn.0372-2112.2012.06.020

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Robust Sparse Recovery Based on Approximate l ₀ Norm

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- Robust Sparse Recovery Based on Approximate l ₀ Norm
- Acta Electronica Sinica Vol. 40, Issue 6, Pages: 1185-1189(2012)
- 作者机构：
  
  1. 国防科学技术大学电子科学与工程学院,湖南,长沙,410073
  2. 二炮士官学校,山东青州,262500
  3. 国防科学技术大学电子科学与工程学院湖南长沙,410073
  4. 二炮士官学校山东青州,262500
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61072120);Project supported by Program for New Century Excellent Talents in University (No.SCET)
- DOI：10.3969/j.issn.0372-2112.2012.06.020
  CLC： TN911.72
- Published：2012
- 稿件说明：
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WANG Jun-hua, HUANG Zhi-tao, ZHOU Yi-yu, et al. Robust Sparse Recovery Based on Approximate l ₀ Norm[J]. Acta Electronica Sinica, 2012, 40(6): 1185-1189.
DOI：

WANG Jun-hua, HUANG Zhi-tao, ZHOU Yi-yu, et al. Robust Sparse Recovery Based on Approximate l ₀ Norm[J]. Acta Electronica Sinica, 2012, 40(6): 1185-1189. DOI： 10.3969/j.issn.0372-2112.2012.06.020.

摘要

针对测量值受噪声污染的稀疏重构问题

本文提出了稳健近似

范数最小化算法.该算法首先利用反正切函数近似l

范数

然后建立基于近似l

范数的含噪稀疏重构模型

最后通过拟牛顿法求解该模型

并分析了算法的收敛性.数值仿真表明

本文提出的算法重构稀疏向量时需要较少的测量值

且具有较高的计算精度.

Abstract

For the problem of recovering sparse vector with noisy measurements

robust approximate

norm minimization algorithm is proposed.Firstly

norm is approximately expressed by arctan function.Secondly

the model of sparse recovery in the present of noise is constructed based on approximate

norm.Finally

the model is solved by quasi-Newton method to estimate sparse vector.Simulation results show that our algorithm needs fewer measurements and provides the better accuracy than the existing methods.

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Robust Sparse Recovery Based on Approximate l 0 Norm

DOI：10.3969/j.issn.0372-2112.2012.06.020

摘要

Abstract

关键词

Keywords

references

Robust Sparse Recovery Based on Approximate l ₀ Norm