<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>952</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>174931</pageFirst>
    <pageLast>174942</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>8</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-09-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios</title>
    <abstract language="eng">Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9528</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ACCESS.2020.3024597</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mateus</first_name>
      <last_name>Da Rosa Zanatta</last_name>
    </author>
    <author>
      <first_name>Joao Paulo</first_name>
      <last_name>Carvalho Lustosa da Costa</last_name>
    </author>
    <author>
      <first_name>Felix</first_name>
      <last_name>Antreich</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Haardt</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <author>
      <first_name>Fábio Lúcio</first_name>
      <last_name>Lopes de Mendonça</last_name>
    </author>
    <author>
      <first_name>Rafael Timóteo</first_name>
      <last_name>de Sousa Junior</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Global navigation satellite systems (GNSS)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>global positioning system (GPS)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GPS3</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>time-delay estimation (TDE)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multipath components</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model order selection (MOS)</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/952/Tensor-Based_Framework.pdf</file>
  </doc>
</export-example>
