Subspace perturbation bounds with an application to angle of arrival estimation using the MUSIC algorithm

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Abstract

This paper explores how angle of arrival (AoA) estimation using the multiple signal classification (MUSIC) algorithm, is affected by estimation errors in space-time covariance matrix. In particular, we explore how this estimation error perturbs the signal-plus-noise and noise-only subspaces of this matrix, and how this subsequently affects the performance of MUSIC for AoA estimation. This subspace perturbation is shown to depend on the space-time covariance matrix itself, the sample size over which it is estimated, as well as the distance of the smallest signal-related eigenvalue to the noise floor. We link a bound of this perturbation to a bound on MUSIC performance, and demonstrate its utility for AoA estimation in simulations.
Original languageEnglish
Number of pages5
Publication statusAccepted/In press - 26 Jun 2020
EventInternational Conference on Sensor Signal Processing for Defence - Edinburgh, United Kingdom
Duration: 15 Sep 202016 Sep 2020
https://sspd.eng.ed.ac.uk/

Conference

ConferenceInternational Conference on Sensor Signal Processing for Defence
Abbreviated titleSSPD
CountryUnited Kingdom
CityEdinburgh
Period15/09/2016/09/20
Internet address

Keywords

  • space-time covariance matrix
  • parahermitian matrix
  • cross-correlation sequence
  • angle of arrival
  • multiple signal classification

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    Delaosa, C., Pestana, J., Weiss, S., & Proudler, I. (Accepted/In press). Subspace perturbation bounds with an application to angle of arrival estimation using the MUSIC algorithm. Paper presented at International Conference on Sensor Signal Processing for Defence, Edinburgh, United Kingdom.