Publication
Deep learning soft-decision GNSS multipath detection and mitigation
| authorProfile.email | biblioteca@isel.pt | |
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| dc.contributor.author | Nunes, Fernando | |
| dc.contributor.author | Sousa, Fernando | |
| dc.date.accessioned | 2025-09-11T11:02:35Z | |
| dc.date.available | 2025-09-11T11:02:35Z | |
| dc.date.issued | 2024-07-18 | |
| dc.description.abstract | A technique is proposed to detect the presence of the multipath effect in Global Navigation Satellite Signal (GNSS) signals using a convolutional neural network (CNN) as the building block. The network is trained and validated, for a wide range of 𝐶/𝑁0 values, with a realistic dataset constituted by the synthetic noisy outputs of a 2D grid of correlators associated with different Doppler frequencies and code delays (time-domain dataset). Multipath-disturbed signals are generated in agreement with the various scenarios encompassed by the adopted multipath model. It was found that pre-processing the outputs of the correlators grid with the two-dimensional Discrete Fourier Transform (frequency-domain dataset) enables the CNN to improve the accuracy relative to the time-domain dataset. Depending on the kind of CNN outputs, two strategies can then be devised to solve the equation of navigation: either remove the disturbed signal from the equation (hard decision) or process the pseudoranges with a weighted least-squares algorithm, where the entries of the weighting matrix are computed using the analog outputs of the neural network (soft decision). | eng |
| dc.identifier.citation | Nunes, F., & Sousa, F. (2024). Deep learning soft-decision GNSS multipath detection and mitigation. Sensors, 24(14), 1-20. https://doi.org/10.3390/s24144663 | |
| dc.identifier.doi | https://doi.org/10.3390/s24144663 | |
| dc.identifier.eissn | 1424-8220 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/22131 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation | Instituto de Telecomunicações | |
| dc.relation.hasversion | https://www.mdpi.com/1424-8220/24/14/4663 | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Multipath detection | |
| dc.subject | Multipath mitigation | |
| dc.subject | Deep learning | |
| dc.subject | Convolutional neural network | |
| dc.subject | Multilayer perceptron | |
| dc.title | Deep learning soft-decision GNSS multipath detection and mitigation | eng |
| dc.type | research article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Instituto de Telecomunicações | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT | |
| oaire.citation.endPage | 20 | |
| oaire.citation.issue | 14 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Sensors | |
| oaire.citation.volume | 24 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |
| person.familyName | Nunes | |
| person.familyName | Sousa | |
| person.givenName | Fernando | |
| person.givenName | Fernando | |
| person.identifier.orcid | 0000-0001-6573-4492 | |
| person.identifier.orcid | 0000-0003-2520-7553 | |
| person.identifier.rid | R-2624-2016 | |
| person.identifier.scopus-author-id | 7005110259 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
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