Title: An adaptive transpose measurement matrix algorithm for signal reconstruction in compressed sensing

Authors: Qi Kang; Lei Shi; Tian Li; Jing An

Addresses: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China ' Department of Control Science and Engineering, Tongji University, Shanghai 201804, China ' Department of Control Science and Engineering, Tongji University, Shanghai 201804, China ' School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 201418, China

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.

Keywords: compressed sensing; sparse representation; greedy reconstruction; random measurement matrix; coding measures; adaptive transpose measurement matrix; signal processing; signal sampling theory; orthogonal matching pursuit; subspace tracking; simulation.

DOI: 10.1504/IJICA.2015.072990

International Journal of Innovative Computing and Applications, 2015 Vol.6 No.3/4, pp.216 - 222

Received: 09 Feb 2015
Accepted: 14 May 2015
Published online: 11 Nov 2015 *

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