Prior knowledge aided super-resolution line spectral estimation: An iterative reweighted algorithm

Feiyu Wang, Jun Fang, Hongbin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper concerns detecting the frequency components from a spectral sparse, undersampled signal. This problem is also called super-resolution line spectral estimation because the frequencies can take arbitrary continuous values. The prior knowledge of the frequency distribution is often available in many applications. To exploit the prior knowledge, a weighting function w designed according to the frequency distribution p is introduced. The prior information can be harnessed through minimizing the corresponding weighted log-sum penalty function. We solve the optimization problem through iteratively decreasing a surrogate function majorizing the original penalty function. Simulation results show that the proposed algorithm outperforms other methods both in noiseless and noisy case, and it also presents superior performance in resolving closely-spaced frequency components.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Pages3296-3300
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - 16 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • Compressed sensing
  • iterative reweighted method
  • probabilistic prior
  • super-resolution

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