Comparison of four super-resolution techniques for complex spectra or points estimation

Guanze Peng, I. Tai Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we evaluate performances of four super-resolution techniques for estimating complex spectra or points under various scenarios. Suitable for resolving non-coherent signals, the two adaptive techniques (Root-MUSIC and ESPRIT) use multiple snapshots to acquire data covariance matrix, which can then be divided into signal subspace and noise subspace for estimating the desired complex parameters. While only utilizing one snapshot to estimate parameters, the two non-adaptive techniques (Matrix Pencil and MODE) are suitable to deal with coherent signals.

Original languageEnglish (US)
Title of host publication2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538638873
DOIs
StatePublished - Aug 3 2017
Event2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017 - Farmingdale, United States
Duration: May 5 2017 → …

Publication series

Name2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017

Other

Other2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017
CountryUnited States
CityFarmingdale
Period5/5/17 → …

Keywords

  • Complex Direction Of Arrival (DOA)
  • Complex Sourse Points
  • Complex Time Of Arrival (TOA)
  • Complex resonance
  • ESPRIT
  • Matrix Pencil
  • Mode
  • Root-MUSIC

ASJC Scopus subject areas

  • Artificial Intelligence
  • Instrumentation
  • Software
  • Computer Science Applications
  • Renewable Energy, Sustainability and the Environment

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  • Cite this

    Peng, G., & Lu, I. T. (2017). Comparison of four super-resolution techniques for complex spectra or points estimation. In 2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017 [8001978] (2017 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LISAT.2017.8001978