An adaptive transpose measurement matrix algorithm for signal reconstruction in compressed sensing
by Qi Kang; Lei Shi; Tian Li; Jing An
International Journal of Innovative Computing and Applications (IJICA), Vol. 6, No. 3/4, 2015

Abstract: Compressed sensing is a new signal sampling theory put forward in recent years. It can obtain a signal's discrete sample in the condition that the sampling rate of signal is far smaller than the Nyquist sampling rate, and then make the original signal perfectly reconstructed with nonlinear reconstruction algorithm. In this paper, three typical greedy reconstruction algorithms, i.e., orthogonal matching pursuit, regularised orthogonal matching pursuit, and subspace tracking algorithm are compared in terms of reconstruction accuracy, error rate and time of reconstruction. Furthermore, a new transpose measurement matrix reconstruction algorithm is proposed and tested through simulation. The experiments on both simulation and calculation showed that this new algorithm can effectively improve the optimisation of the signal transmission.

Online publication date: Wed, 11-Nov-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Innovative Computing and Applications (IJICA):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com