Chen, H., Zhu, W.-P., Liu, W. orcid.org/0000-0003-2968-2888 et al. (4 more authors) (2019) RARE-based localization for mixed near-field and far-field rectilinear sources. Digital Signal Processing, 85. pp. 54-61. ISSN 1051-2004
Abstract
In this paper, a novel localization method for mixed near-field (NF) and far-field (FF) rectilinear or strictly noncircular sources is proposed using the noncircular information for a symmetric uniform linear array (ULA). For FF case, we adopt the NC-MUSIC method to achieve the DOA parameter, for NF case, by exploiting the center symmetrical characteristic of the ULA, we decouple the array steering vectors into two new vectors: one related only to the DOA parameter, and the other dependent on both DOA and range parameters. Based on the principle of rank reduction (RARE), three MUSIC-like estimators are formed to estimate the direction of arrival (DOA) and the range of mixed NF and FF rectilinear sources successively. Meanwhile, distinguishing the types of sources is also solved. The deterministic Cramer–Rao bound (CRB) of the mixed rectilinear signals is derived by the Slepian–Bangs formulation. Simulation results are provided, showing that the proposed method yields a performance better than existing ones.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier. This is an author produced version of a paper subsequently published in Digital 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: | DOA estimation; Far-field; Near-field; Rectilinear signals; Cramer-Rao bound (CRB) |
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: | 28 Feb 2019 13:27 |
Last Modified: | 28 Nov 2019 01:39 |
Published Version: | https://doi.org/10.1016/j.dsp.2018.11.006 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.dsp.2018.11.006 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143092 |
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