@inproceedings{eb438e06cc6f4d4a9549d2d727daf037,
title = "Fast pursuit method for greedy algorithms in Distributed Compressive Sensing",
abstract = "This paper proposes a fast pursuit method for greedy algorithms when reconstructing multi-signals under Distributed Compressive Sensing (DCS) framework. DCS takes advantage of both intra- and inter-signal correlation structures to reduce the measurements required for signals recovery. Greedy algorithms, much faster than l0 and l1 minimization algorithms, are widely used in DCS. General approaches transform DCS model to Compressive Sensing (CS) model and then directly use greedy algorithms to reconstruct signals, but the recovery speed becomes very slow as the signal number n increasing. In this paper, we propose a fast pursuit method which exploits the structural features of joint measurement matrix to reduce the computational complexity form O(n2) to O(n) when calculating inner-product in greedy algorithms, which improves the recovery speed significantly without reducing recovery accuracy.",
keywords = "Distributed compressive sensing, Fast pursuit method, Greedy algorithms, Joint sparse model",
author = "Hongwei Xu and Ning Fu and Liyan Qiao and Xiyuan Peng",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2015 ; Conference date: 11-05-2015 Through 14-05-2015",
year = "2015",
month = jul,
day = "6",
doi = "10.1109/I2MTC.2015.7151428",
language = "英语",
series = "Conference Record - IEEE Instrumentation and Measurement Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1118--1122",
booktitle = "2015 IEEE International Instrumentation and Measurement Technology Conference - The {"}Measurable{"} of Tomorrow",
address = "美国",
}