Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios
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 ca...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9200326/ |
| _version_ | 1818853407002198016 |
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| author | Mateus Da Rosa Zanatta Joao Paulo Carvalho Lustosa Da Costa Felix Antreich Martin Haardt Gordon Elger Fabio Lucio Lopes De Mendonca Rafael Timoteo De Sousa |
| author_facet | Mateus Da Rosa Zanatta Joao Paulo Carvalho Lustosa Da Costa Felix Antreich Martin Haardt Gordon Elger Fabio Lucio Lopes De Mendonca Rafael Timoteo De Sousa |
| author_sort | Mateus Da Rosa Zanatta |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-12-19T07:36:19Z |
| format | Article |
| id | doaj.art-594f0df3caea4359ab90a1d71f7b7962 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:36:19Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-594f0df3caea4359ab90a1d71f7b79622022-12-21T20:30:34ZengIEEEIEEE Access2169-35362020-01-01817493117494210.1109/ACCESS.2020.30245979200326Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath ScenariosMateus Da Rosa Zanatta0https://orcid.org/0000-0001-6370-947XJoao Paulo Carvalho Lustosa Da Costa1https://orcid.org/0000-0002-8616-4924Felix Antreich2https://orcid.org/0000-0001-6596-0123Martin Haardt3https://orcid.org/0000-0001-7810-975XGordon Elger4https://orcid.org/0000-0002-7643-7327Fabio Lucio Lopes De Mendonca5Rafael Timoteo De Sousa6https://orcid.org/0000-0003-1101-3029Department of Electrical Engineering, University of Brasilia, Brasília, BrazilDepartment of Electrical Engineering, University of Brasilia, Brasília, BrazilDepartment of Telecommunications, Aeronautics Institute of Technology (ITA), São José dos Campos, BrazilCommunications Research Laboratory, Technische Universität Ilmenau, Ilmenau, GermanyFaculty of Electrical Engineering and Information Technology, Technische Hochschule Ingolstadt, Ingolstadt, GermanyDepartment of Electrical Engineering, University of Brasilia, Brasília, BrazilDepartment of Electrical Engineering, University of Brasilia, Brasília, BrazilGlobal 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).https://ieeexplore.ieee.org/document/9200326/Global navigation satellite systems (GNSS)global positioning system (GPS)GPS3time-delay estimation (TDE)multipath componentsmodel order selection (MOS) |
| spellingShingle | Mateus Da Rosa Zanatta Joao Paulo Carvalho Lustosa Da Costa Felix Antreich Martin Haardt Gordon Elger Fabio Lucio Lopes De Mendonca Rafael Timoteo De Sousa Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios IEEE Access Global navigation satellite systems (GNSS) global positioning system (GPS) GPS3 time-delay estimation (TDE) multipath components model order selection (MOS) |
| title | Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios |
| title_full | Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios |
| title_fullStr | Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios |
| title_full_unstemmed | Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios |
| title_short | Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios |
| title_sort | tensor based framework with model order selection and high accuracy factor decomposition for time delay estimation in dynamic multipath scenarios |
| topic | Global navigation satellite systems (GNSS) global positioning system (GPS) GPS3 time-delay estimation (TDE) multipath components model order selection (MOS) |
| url | https://ieeexplore.ieee.org/document/9200326/ |
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