Sparse Bayesian Learning Approach for Streaming Signal Recovery

被引：0

作者：

Wijewardhana, U. L. ^{[1
]}

Codreanu, M. ^{[1
]}

机构：

[1] Univ Oulu, Ctr Wireless Commun, POB 4500, FIN-90014 Oulu, Finland

来源：

2014 IEEE INFORMATION THEORY WORKSHOP (ITW) | 2014年

基金：

芬兰科学院;

关键词：

Compressive sensing; sparse Bayesian learning; recursive methods; streaming signals;

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：

We discuss the reconstruction of streaming signals from compressive measurements. We propose to use an algorithm based on sparse Bayesian learning to reconstruct the streaming signal over small shifting intervals. The proposed algorithm utilizes the previous estimates to improve the accuracy of the signal estimate and the speed of the recovery algorithm. Simulation results show that the proposed algorithm can achieve better signal-to-error ratios compared with the existing l1-homotopy based recovery algorithm.

引用

页码：302 / 306

页数：5

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