Compressive Sensing for Autoregressive Hidden Markov Model Signal

被引：0
|
作者：
Wu, Ji [1 ]
Liang, Qilian [1 ]
Zhou, Zheng [2 ]
机构：
[1] Univ Texas Arlington, Dept Elect Engn, Arlington, TX 76019 USA
[2] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China
来源：
WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS | 2010年 / 6221卷
基金：
美国国家科学基金会;
关键词：
compressive sensing; coefficient estimation; hidden markov model; RECOVERY;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Cornpressive sensing(CS) is an emerging filed based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. One challenging problem in compressive sensing is that it is difficult to represent signal in sparse basis, which makes this algorithm sometimes impractical. In this paper, we can setup a new standard compressive sensing problem for autoregressive hidden markov signal by utilizing the original observation vector and the autoregressive coefficients.
引用
收藏
页码：360 / +
页数：2
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