Sparse Signal Reconstruction Based on Multiparameter Approximation Function with Smoothed Norm

Xiao-Feng Fang, Jiang-She Zhang and Ying-Qi Li

Mathematical Problems in Engineering, 2014, vol. 2014, 1-9

Abstract:

The smoothed norm algorithm is a reconstruction algorithm in compressive sensing based on approximate smoothed norm. It introduces a sequence of smoothed functions to approximate the norm and approaches the solution using the specific iteration process with the steepest method. In order to choose an appropriate sequence of smoothed function and solve the optimization problem effectively, we employ approximate hyperbolic tangent multiparameter function as the approximation to the big “steep nature†in norm. Simultaneously, we propose an algorithm based on minimizing a reweighted approximate norm in the null space of the measurement matrix. The unconstrained optimization involved is performed by using a modified quasi-Newton algorithm. The numerical simulation results show that the proposed algorithms yield improved signal reconstruction quality and performance.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2014/416542.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2014/416542.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:416542

DOI: 10.1155/2014/416542

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().