Jiang, Z., Chen, H., Liu, W. orcid.org/0000-0003-2968-2888 et al. (1 more author) (2022) 3-D temporal-spatial-based near-field source localization considering amplitude attenuation. Signal Processing, 201. 108735. ISSN 0165-1684
Abstract
A three-dimensional (3-D) localization algorithm for multiple near-field (NF) sources considering amplitude attenuation is proposed in this paper. Firstly, we use the symmetry of the array and the delay autocorrelation of NF signals to construct the virtual received data, whose phase factor is linear with the sensor position. By using the symmetry of amplitude attenuation of the virtual received data, a one-dimensional (1-D) peak search estimator is constructed to obtain the first angle parameter. Then, the estimated result is substituted into another spectral peak search function based on the original data to estimate the range parameter. Finally, a single-snapshot virtual received data set is generated, and the remaining angle parameter is solved by a phase retrieval operation. The proposed algorithm can automatically match the 3-D parameters, and has a stable estimation performance considering amplitude attenuation, as demonstrated by simulation results.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Published by Elsevier B.V. This is an author produced version of a paper subsequently published in Signal Processing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Source localization; near-field sources; amplitude attenuation; temporal-spatial |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Aug 2022 15:27 |
Last Modified: | 17 Aug 2023 00:13 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.sigpro.2022.108735 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190101 |