Reconstruction of Non-stationary Signals With Missing Samples Using S-method and a Gradient Based Reconstruction Algorithm

Authors

  • Miloš Brajović University of Montenegro
  • Ljubiša Stanković University of Montenegro
  • Miloš Daković University of Montenegro

Keywords:

Compressive sensing, Digital signal processing, Gradient algorithm, Sparse Signal processing, S-method

Abstract

This paper addresses the reconstruction problem of non-stationary signals with missing samples. The reconstruction is achieved by using concentration measures of time-frequency representations in combination with a gradient-based iterative algorithm. As an example of time-frequency representation, the S-method is used in the proposed approach. The sparsity of the transform domain, needed for a successful reconstruction, is interpreted through the concept of concentration measures, and limits for successful reconstruction are discussed. Several examples with nonstationary signals which exhibit different concentrations in the time-frequency
domain illustrate the presented theoretical concepts.

Cover

Downloads

Published

22.03.2023

Issue

Vol. 21 No. 1 (2015)

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.

Provided they are the owners of the copyright to their work, authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository, in a journal or publish it in a book), with an acknowledgment of its initial publication in this journal.