Narrowband angle of arrival estimation exploiting graph topology and graph signals

Proudler, Ian K. and Stankovic, Vladimir and Weiss, Stephan; (2020) Narrowband angle of arrival estimation exploiting graph topology and graph signals. In: 2020 Sensor Signal Processing for Defence Conference (SSPD). IEEE, GBR. ISBN 9781728138114

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    Abstract

    Based on recent results of applying graph signal processing (GSP) to narrowband angle of arrival estimation for uniform linear arrays, we generalise the analysis to the case of arrays with elements placed arbitrarily in three dimensional space. We comment on the selection of the adjacency matrix, analyse how this new approach compares to the multiple signal classification (MUSIC) algorithm, and provide an efficient implementation. We demonstrate that the GSP approach can perform as well as the MUSIC algorithm in terms of accuracy and computational cost. Simulations indicate that the proposed GSP approach avoids the severe performance degradation with which MUSIC is associated at low signal to noise ratios.

    ORCID iDs

    Proudler, Ian K., Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420 and Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206;
    • Item type:Book Section
      ID code:73295
      Dates:
      Date
      Event
      30 November 2020
      Published
      20 July 2020
      Accepted
      Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
      Keywords:noise ratios, graph topology, arrival estimation techniques, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
      Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
      Department:Faculty of Engineering > Electronic and Electrical Engineering
      Technology and Innovation Centre > Sensors and Asset Management
      Depositing user: Pure Administrator
      Date deposited:22 Jul 2020 13:55
      Last modified:12 Jul 2021 09:14
      URI:https://strathprints.strath.ac.uk/id/eprint/73295
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