北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (6): 1203-1209.doi: 10.13700/j.bh.1001-5965.2015.0375

• 论文 • 上一篇    下一篇

基于优化字典学习算法的压缩数据收集

易可夫, 王东豪, 万江文   

  1. 北京航空航天大学 仪器科学与光电工程学院, 北京 100083
  • 收稿日期:2015-06-08 出版日期:2016-06-20 发布日期:2016-07-06
  • 通讯作者: 万江文,Tel.:010-82339889 E-mail:jwwan@buaa.edu.cn E-mail:jwwan@buaa.edu.cn
  • 作者简介:易可夫 男,博士研究生。主要研究方向:无线传感器网络。E-mail:corfyi@163.com;万江文 男,博士,教授,博士生导师。主要研究方向:无线传感器网络。Tel.:010-82339889 E-mail:jwwan@buaa.edu.cn
  • 基金资助:
    国家自然科学基金(61371135)

Optimized dictionary learning algorithm for compressive data gathering

YI Kefu, WANG Donghao, WAN Jiangwen   

  1. School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2015-06-08 Online:2016-06-20 Published:2016-07-06

摘要: 为了提高压缩数据收集对多样化传感数据的适应能力,同时抑制环境噪声对数据收集精度的影响,提出了一种优化字典学习算法来构造压缩数据收集中的稀疏字典。理论分析表明在压缩数据收集中由环境噪声导致的数据收集误差和稀疏字典的自相干程度正相关。为此在字典学习的过程中引入了自相干惩罚项来抑制环境噪声对数据收集精度的影响。该惩罚项还能减少字典学习过程中对训练数据的过拟合,从而进一步提高了该算法的稀疏表示能力。实验表明,该算法的稀疏表示能力高于同类字典学习算法,而且能有效地抑制环境噪声对压缩数据收集精度的影响。

关键词: 无线传感器网络(WSNs), 压缩感知, 稀疏表示, 数据收集, 字典学习

Abstract: To improve the adaptability of compressive data gathering for various classes of sensory data, and to reduce the recovery error caused by environmental noise, an optimized dictionary learning algorithm was proposed to adaptively construct the sparse dictionary in compressive data gathering. Theoretical analysis shows that in compressive data gathering the recovery error caused by environmental noise is positively correlated to the self-coherence of the sparse dictionary. Therefore, in order to alleviate the recovery error caused by environmental noise, the proposed algorithm introduces a penalty term into the dictionary learning procedure to reduce the self-coherence of the learned dictionary. The introduced penalty term can also alleviate the over-fitting on the training data during the dictionary learning procedure, which further improves the sparse representation performance of the learned dictionary. The experimental results verify that the proposed method achieves better sparse representation performance than other dictionary learning methods, and can alleviate the recovery error caused by environmental noise.

Key words: wireless sensor networks (WSNs), compressive sensing, sparse representation, data gathering, dictionary learning

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发