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 (https://doi.org/10.1109/SSPD47486.2020.9272170)
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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: https://orcid.org/0000-0002-1075-2420 and Weiss, Stephan ORCID: https://orcid.org/0000-0002-3486-7206;-
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Item type: Book Section ID code: 73295 Dates: DateEvent30 November 2020Published20 July 2020AcceptedNotes: © 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. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 22 Jul 2020 13:55 Last modified: 20 Nov 2024 14:48 URI: https://strathprints.strath.ac.uk/id/eprint/73295