Enhancing polynomial MUSIC algorithm for coherent broadband sources through spatial smoothing

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Abstract

Direction of arrival algorithms which exploit the eigenstructure of the spatial covariance matrix (such as MUSIC) encounter difficulties in the presence of strongly correlated sources. Since the broadband polynomial MUSIC is an extension of the narrowband version, it is unsurprising that the same issues arise. In this paper, we extend the spatial smoothing technique to broadband scenarios via spatially averaging polynomial spacetime covariance matrices. This is shown to restore the rank of the polynomial source covariance matrix. In the application of the polynomial MUSIC algorithm, the spatially smoothed spacetime covariance matrix greatly enhances the direction of arrival estimate in the presence of strongly correlated sources. Simulation results are described shows the performance improvement gained using the new approach compared to the conventional non-smoothed method.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages2448-2452
Number of pages5
Volume2017-January
ISBN (Electronic)9780992862671
DOIs
Publication statusPublished - 23 Oct 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sep 2017

Conference

Conference25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period28/08/172/09/17

Keywords

  • spatial smoothing
  • direction of arrival
  • DoA
  • polynomial eigenvalue decomposition
  • PEVD

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  • Cite this

    Coventry, W., Clemente, C., & Soraghan, J. (2017). Enhancing polynomial MUSIC algorithm for coherent broadband sources through spatial smoothing. In 25th European Signal Processing Conference, EUSIPCO 2017 (Vol. 2017-January, pp. 2448-2452). [8081650] IEEE. https://doi.org/10.23919/EUSIPCO.2017.8081650