Wang, W., Chen, H. orcid.org/0000-0002-2918-8735, Liu, W. orcid.org/0000-0003-2968-2888 et al. (2 more authors) (2023) Trilinear decomposition based near-field source localization with MIMO velocity vector sensor arrays. Signal Processing, 210. 109061. ISSN 0165-1684
Abstract
This paper studies the near-field (NF) parameter estimation problem for a multiple-input multiple-output (MIMO) array system, which employs multiple pairs of orthogonal velocity sensors at both the transmitter and the receiver. A trilinear decomposition method is proposed to estimate the four-dimensional (4-D) parameters, including the direction of departure (DOD), the range from transmitter to target (RFTT), the direction of arrival (DOA), and the range from target to receiver (RFTR). Firstly, the output of the matched filter at the receiver is formulated in a third-order parallel factor (PARAFAC) model; secondly, the initial coarse estimates of DOD, RFTT, DOA and RFTR embedded in the velocity vector sensors are obtained through trilinear decomposition, and then more accurate estimates of DOD, RFTT, DOA and RFTR are achieved from the steering vector. The proposed method is search-free and has a close form, with automatically paired results. Its performance is demonstrated via numerical examples.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Elsevier. 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: | MIMO; Velocity vector sensor; Near-field; Parallel factor |
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: | 24 Apr 2023 09:39 |
Last Modified: | 02 Oct 2024 15:51 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.sigpro.2023.109061 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198459 |