Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM Article Swipe
Yahang Qin
,
Zhenni Li
,
Shengli Xie
,
Bo Li
,
Ming Liu
,
Victor Kuzin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26599/tst.2024.9010004
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26599/tst.2024.9010004
Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System (BDS). However, most existing approaches to this issue involve supervised machine learning (ML) methods, and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal labeling. Inspired by an autoencoder with powerful unsupervised feature extraction, we propose a new deep learning (DL) model for BDS signal recognition that places a long short-term memory (LSTM) module in series with a convolutional sparse autoencoder to create a new autoencoder structure. First, we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time series. Second, we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data, which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series signals. Finally, we add an l1/2 regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition accuracy. We tested our proposed approach on a real urban canyon dataset, and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods (e.g., 11% better than a support vector machine) and two existing DL-based methods (e.g., 7.26% better than convolutional neural networks).
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26599/tst.2024.9010004
- https://ieeexplore.ieee.org/ielx7/5971803/10367774/10480322.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393207127
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4393207127Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26599/tst.2024.9010004Digital Object Identifier
- Title
-
Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTMWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-26Full publication date if available
- Authors
-
Yahang Qin, Zhenni Li, Shengli Xie, Bo Li, Ming Liu, Victor KuzinList of authors in order
- Landing page
-
https://doi.org/10.26599/tst.2024.9010004Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/5971803/10367774/10480322.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/5971803/10367774/10480322.pdfDirect OA link when available
- Concepts
-
Autoencoder, Computer science, Artificial intelligence, Line (geometry), Pattern recognition (psychology), Multipath propagation, Line-of-sight, Mathematics, Deep learning, Telecommunications, Engineering, Geometry, Aerospace engineering, Channel (broadcasting)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4393207127 |
|---|---|
| doi | https://doi.org/10.26599/tst.2024.9010004 |
| ids.doi | https://doi.org/10.26599/tst.2024.9010004 |
| ids.openalex | https://openalex.org/W4393207127 |
| fwci | 0.73833695 |
| type | article |
| title | Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM |
| awards[0].id | https://openalex.org/G4380340894 |
| awards[0].funder_id | https://openalex.org/F4320321543 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2022M720840 |
| awards[0].funder_display_name | China Postdoctoral Science Foundation |
| awards[1].id | https://openalex.org/G170923117 |
| awards[1].funder_id | https://openalex.org/F4320337111 |
| awards[1].display_name | |
| awards[1].funder_award_id | 2023A1515011480,2023A1515011159 |
| awards[1].funder_display_name | Basic and Applied Basic Research Foundation of Guangdong Province |
| biblio.issue | 1 |
| biblio.volume | 30 |
| biblio.last_page | 86 |
| biblio.first_page | 68 |
| topics[0].id | https://openalex.org/T10326 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9876000285148621 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Indoor and Outdoor Localization Technologies |
| topics[1].id | https://openalex.org/T10205 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9843999743461609 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Advanced Fiber Optic Sensors |
| topics[2].id | https://openalex.org/T13717 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9819999933242798 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2207 |
| topics[2].subfield.display_name | Control and Systems Engineering |
| topics[2].display_name | Advanced Algorithms and Applications |
| funders[0].id | https://openalex.org/F4320321543 |
| funders[0].ror | https://ror.org/0426zh255 |
| funders[0].display_name | China Postdoctoral Science Foundation |
| funders[1].id | https://openalex.org/F4320337111 |
| funders[1].ror | |
| funders[1].display_name | Basic and Applied Basic Research Foundation of Guangdong Province |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C101738243 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8528579473495483 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q786435 |
| concepts[0].display_name | Autoencoder |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5900291800498962 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.507770836353302 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C198352243 |
| concepts[3].level | 2 |
| concepts[3].score | 0.505396842956543 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q37105 |
| concepts[3].display_name | Line (geometry) |
| concepts[4].id | https://openalex.org/C153180895 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5032245516777039 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[4].display_name | Pattern recognition (psychology) |
| concepts[5].id | https://openalex.org/C161218011 |
| concepts[5].level | 3 |
| concepts[5].score | 0.49524322152137756 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11827794 |
| concepts[5].display_name | Multipath propagation |
| concepts[6].id | https://openalex.org/C2780928442 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4296457767486572 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6553027 |
| concepts[6].display_name | Line-of-sight |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.2113417685031891 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C108583219 |
| concepts[8].level | 2 |
| concepts[8].score | 0.18307426571846008 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[8].display_name | Deep learning |
| concepts[9].id | https://openalex.org/C76155785 |
| concepts[9].level | 1 |
| concepts[9].score | 0.1365855634212494 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[9].display_name | Telecommunications |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.08724626898765564 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C2524010 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[11].display_name | Geometry |
| concepts[12].id | https://openalex.org/C146978453 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[12].display_name | Aerospace engineering |
| concepts[13].id | https://openalex.org/C127162648 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q16858953 |
| concepts[13].display_name | Channel (broadcasting) |
| keywords[0].id | https://openalex.org/keywords/autoencoder |
| keywords[0].score | 0.8528579473495483 |
| keywords[0].display_name | Autoencoder |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5900291800498962 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.507770836353302 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/line |
| keywords[3].score | 0.505396842956543 |
| keywords[3].display_name | Line (geometry) |
| keywords[4].id | https://openalex.org/keywords/pattern-recognition |
| keywords[4].score | 0.5032245516777039 |
| keywords[4].display_name | Pattern recognition (psychology) |
| keywords[5].id | https://openalex.org/keywords/multipath-propagation |
| keywords[5].score | 0.49524322152137756 |
| keywords[5].display_name | Multipath propagation |
| keywords[6].id | https://openalex.org/keywords/line-of-sight |
| keywords[6].score | 0.4296457767486572 |
| keywords[6].display_name | Line-of-sight |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.2113417685031891 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/deep-learning |
| keywords[8].score | 0.18307426571846008 |
| keywords[8].display_name | Deep learning |
| keywords[9].id | https://openalex.org/keywords/telecommunications |
| keywords[9].score | 0.1365855634212494 |
| keywords[9].display_name | Telecommunications |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.08724626898765564 |
| keywords[10].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.26599/tst.2024.9010004 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S24978797 |
| locations[0].source.issn | 1007-0214, 1878-7606 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1007-0214 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Tsinghua Science & Technology |
| locations[0].source.host_organization | https://openalex.org/P4310311901 |
| locations[0].source.host_organization_name | Tsinghua University Press |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311901 |
| locations[0].source.host_organization_lineage_names | Tsinghua University Press |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://ieeexplore.ieee.org/ielx7/5971803/10367774/10480322.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Tsinghua Science and Technology |
| locations[0].landing_page_url | https://doi.org/10.26599/tst.2024.9010004 |
| locations[1].id | pmh:oai:repository.hkust.edu.hk:1783.1-144745 |
| locations[1].is_oa | False |
| locations[1].source | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://repository.hkust.edu.hk/ir/Record/1783.1-144745 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5102783652 |
| authorships[0].author.orcid | https://orcid.org/0009-0002-8632-0269 |
| authorships[0].author.display_name | Yahang Qin |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I139024713 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Automation, Guangdong University of Technology, and also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou 510006, China |
| authorships[0].institutions[0].id | https://openalex.org/I139024713 |
| authorships[0].institutions[0].ror | https://ror.org/04azbjn80 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I139024713 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Guangdong University of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yahang Qin |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Automation, Guangdong University of Technology, and also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou 510006, China |
| authorships[1].author.id | https://openalex.org/A5062429830 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8098-0341 |
| authorships[1].author.display_name | Zhenni Li |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I139024713 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Automation, Guangdong University of Technology, and also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou 510006, China |
| authorships[1].institutions[0].id | https://openalex.org/I139024713 |
| authorships[1].institutions[0].ror | https://ror.org/04azbjn80 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I139024713 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Guangdong University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhenni Li |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Automation, Guangdong University of Technology, and also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou 510006, China |
| authorships[2].author.id | https://openalex.org/A5043262479 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2041-5214 |
| authorships[2].author.display_name | Shengli Xie |
| authorships[2].affiliations[0].raw_affiliation_string | 111 Center for Intelligent Batch Manufacturing Based on IoT Technology, and also with Key Laboratory of Intelligent Information Processing and System Integration of IoT, Ministry of Education, Guangzhou 510006, China |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Shengli Xie |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | 111 Center for Intelligent Batch Manufacturing Based on IoT Technology, and also with Key Laboratory of Intelligent Information Processing and System Integration of IoT, Ministry of Education, Guangzhou 510006, China |
| authorships[3].author.id | https://openalex.org/A5100374424 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3793-9156 |
| authorships[3].author.display_name | Bo Li |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I139024713 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Automation, Guangdong University of Technology, and also with Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China |
| authorships[3].institutions[0].id | https://openalex.org/I139024713 |
| authorships[3].institutions[0].ror | https://ror.org/04azbjn80 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I139024713 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Guangdong University of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Bo Li |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Automation, Guangdong University of Technology, and also with Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China |
| authorships[4].author.id | https://openalex.org/A5100347785 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-4500-238X |
| authorships[4].author.display_name | Ming Liu |
| authorships[4].countries | HK |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I200769079, https://openalex.org/I889458895 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Electronic and Computer Engineering, Hong Kong University of Science and Technolog, Hong Kong, China |
| authorships[4].institutions[0].id | https://openalex.org/I200769079 |
| authorships[4].institutions[0].ror | https://ror.org/00q4vv597 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I200769079 |
| authorships[4].institutions[0].country_code | HK |
| authorships[4].institutions[0].display_name | Hong Kong University of Science and Technology |
| authorships[4].institutions[1].id | https://openalex.org/I889458895 |
| authorships[4].institutions[1].ror | https://ror.org/02zhqgq86 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I889458895 |
| authorships[4].institutions[1].country_code | HK |
| authorships[4].institutions[1].display_name | University of Hong Kong |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Ming Liu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Electronic and Computer Engineering, Hong Kong University of Science and Technolog, Hong Kong, China |
| authorships[5].author.id | https://openalex.org/A5047969373 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-2805-9325 |
| authorships[5].author.display_name | Victor Kuzin |
| authorships[5].countries | RU |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210106329 |
| authorships[5].affiliations[0].raw_affiliation_string | Academician of Russian Engineering Academy, Moscow, Russia |
| authorships[5].institutions[0].id | https://openalex.org/I4210106329 |
| authorships[5].institutions[0].ror | https://ror.org/01fvvak72 |
| authorships[5].institutions[0].type | other |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210106329 |
| authorships[5].institutions[0].country_code | RU |
| authorships[5].institutions[0].display_name | Russian Engineering Academy |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Victor Kuzin |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Academician of Russian Engineering Academy, Moscow, Russia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ieeexplore.ieee.org/ielx7/5971803/10367774/10480322.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10326 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9876000285148621 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Indoor and Outdoor Localization Technologies |
| related_works | https://openalex.org/W3013693939, https://openalex.org/W2159052453, https://openalex.org/W2566616303, https://openalex.org/W3131327266, https://openalex.org/W2734887215, https://openalex.org/W4297051394, https://openalex.org/W2752972570, https://openalex.org/W2145836866, https://openalex.org/W2803255133, https://openalex.org/W2343874524 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | doi:10.26599/tst.2024.9010004 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S24978797 |
| best_oa_location.source.issn | 1007-0214, 1878-7606 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1007-0214 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Tsinghua Science & Technology |
| best_oa_location.source.host_organization | https://openalex.org/P4310311901 |
| best_oa_location.source.host_organization_name | Tsinghua University Press |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311901 |
| best_oa_location.source.host_organization_lineage_names | Tsinghua University Press |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://ieeexplore.ieee.org/ielx7/5971803/10367774/10480322.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Tsinghua Science and Technology |
| best_oa_location.landing_page_url | https://doi.org/10.26599/tst.2024.9010004 |
| primary_location.id | doi:10.26599/tst.2024.9010004 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S24978797 |
| primary_location.source.issn | 1007-0214, 1878-7606 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1007-0214 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Tsinghua Science & Technology |
| primary_location.source.host_organization | https://openalex.org/P4310311901 |
| primary_location.source.host_organization_name | Tsinghua University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311901 |
| primary_location.source.host_organization_lineage_names | Tsinghua University Press |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/5971803/10367774/10480322.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Tsinghua Science and Technology |
| primary_location.landing_page_url | https://doi.org/10.26599/tst.2024.9010004 |
| publication_date | 2024-03-26 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4210806292, https://openalex.org/W4283392533, https://openalex.org/W3199427656, https://openalex.org/W4320486660, https://openalex.org/W4285087989, https://openalex.org/W2023409854, https://openalex.org/W6604312705, https://openalex.org/W2793716893, https://openalex.org/W4320486865, https://openalex.org/W3035647407, https://openalex.org/W3113118202, https://openalex.org/W3006550473, https://openalex.org/W4366378568, https://openalex.org/W2901279795, https://openalex.org/W3174598035, https://openalex.org/W3010881854, https://openalex.org/W2989902081, https://openalex.org/W4313016472, https://openalex.org/W3139920757, https://openalex.org/W4306950675, https://openalex.org/W4285731527, https://openalex.org/W3011039333, https://openalex.org/W3164058559, https://openalex.org/W2805604006, https://openalex.org/W2180507614, https://openalex.org/W3189475240, https://openalex.org/W4281261192, https://openalex.org/W2904787380, https://openalex.org/W4223438888, https://openalex.org/W4313147016, https://openalex.org/W3118534614, https://openalex.org/W1689711448, https://openalex.org/W2056201402, https://openalex.org/W2789587413, https://openalex.org/W4220733308, https://openalex.org/W3152534801, https://openalex.org/W3207592251, https://openalex.org/W6677759377, https://openalex.org/W4225428162, https://openalex.org/W2118718620 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 62, 74, 83, 89, 125, 132, 185, 210 |
| abstract_inverted_index.DL | 165 |
| abstract_inverted_index.We | 179 |
| abstract_inverted_index.an | 53, 156 |
| abstract_inverted_index.by | 13, 52, 107 |
| abstract_inverted_index.in | 48, 80, 101, 118 |
| abstract_inverted_index.is | 3, 35 |
| abstract_inverted_index.it | 34 |
| abstract_inverted_index.of | 45, 135, 163 |
| abstract_inverted_index.on | 184 |
| abstract_inverted_index.to | 5, 8, 24, 37, 39, 87, 96, 112, 159, 167 |
| abstract_inverted_index.we | 60, 94, 123, 154 |
| abstract_inverted_index.11% | 207 |
| abstract_inverted_index.BDS | 69 |
| abstract_inverted_index.add | 155 |
| abstract_inverted_index.and | 33, 143, 190, 214 |
| abstract_inverted_index.for | 68 |
| abstract_inverted_index.new | 63, 90 |
| abstract_inverted_index.our | 164, 181, 195 |
| abstract_inverted_index.the | 6, 14, 46, 98, 109, 114, 119, 136, 160, 172, 191 |
| abstract_inverted_index.two | 203, 215 |
| abstract_inverted_index.(DL) | 66 |
| abstract_inverted_index.(ML) | 31 |
| abstract_inverted_index.LSTM | 110 |
| abstract_inverted_index.deep | 64 |
| abstract_inverted_index.from | 147, 171 |
| abstract_inverted_index.l1/2 | 157 |
| abstract_inverted_index.long | 75 |
| abstract_inverted_index.mine | 113 |
| abstract_inverted_index.most | 21 |
| abstract_inverted_index.move | 38 |
| abstract_inverted_index.real | 186 |
| abstract_inverted_index.than | 202, 209, 222 |
| abstract_inverted_index.that | 72, 130, 194 |
| abstract_inverted_index.then | 140 |
| abstract_inverted_index.this | 25 |
| abstract_inverted_index.time | 120 |
| abstract_inverted_index.with | 55, 82 |
| abstract_inverted_index.7.26% | 220 |
| abstract_inverted_index.could | 197 |
| abstract_inverted_index.data, | 138 |
| abstract_inverted_index.input | 137 |
| abstract_inverted_index.issue | 26 |
| abstract_inverted_index.model | 67, 166 |
| abstract_inverted_index.urban | 187 |
| abstract_inverted_index.using | 108 |
| abstract_inverted_index.which | 139 |
| abstract_inverted_index.while | 175 |
| abstract_inverted_index.(BDS). | 19 |
| abstract_inverted_index.(LSTM) | 78 |
| abstract_inverted_index.(e.g., | 206, 219 |
| abstract_inverted_index.BeiDou | 15, 103, 149 |
| abstract_inverted_index.First, | 93 |
| abstract_inverted_index.System | 18 |
| abstract_inverted_index.better | 199, 208, 221 |
| abstract_inverted_index.canyon | 188 |
| abstract_inverted_index.change | 116 |
| abstract_inverted_index.create | 88 |
| abstract_inverted_index.learns | 131 |
| abstract_inverted_index.memory | 77 |
| abstract_inverted_index.method | 129 |
| abstract_inverted_index.module | 79, 111 |
| abstract_inverted_index.neural | 173, 224 |
| abstract_inverted_index.places | 73 |
| abstract_inverted_index.remove | 168 |
| abstract_inverted_index.series | 81, 151 |
| abstract_inverted_index.signal | 1, 42, 49, 70 |
| abstract_inverted_index.sparse | 85, 127 |
| abstract_inverted_index.tested | 180 |
| abstract_inverted_index.vector | 212 |
| abstract_inverted_index.Second, | 122 |
| abstract_inverted_index.ability | 7 |
| abstract_inverted_index.achieve | 198 |
| abstract_inverted_index.because | 44 |
| abstract_inverted_index.capture | 97 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.develop | 124 |
| abstract_inverted_index.enables | 141 |
| abstract_inverted_index.feature | 58, 145 |
| abstract_inverted_index.involve | 27 |
| abstract_inverted_index.machine | 29 |
| abstract_inverted_index.methods | 205, 218 |
| abstract_inverted_index.network | 174 |
| abstract_inverted_index.neurons | 170 |
| abstract_inverted_index.propose | 61, 95 |
| abstract_inverted_index.provide | 9 |
| abstract_inverted_index.results | 192 |
| abstract_inverted_index.series. | 121 |
| abstract_inverted_index.signals | 106 |
| abstract_inverted_index.support | 211 |
| abstract_inverted_index.DL-based | 217 |
| abstract_inverted_index.Finally, | 153 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.Inspired | 51 |
| abstract_inverted_index.ML-based | 204 |
| abstract_inverted_index.approach | 183 |
| abstract_inverted_index.dataset, | 189 |
| abstract_inverted_index.ensuring | 176 |
| abstract_inverted_index.existing | 22, 216 |
| abstract_inverted_index.function | 162 |
| abstract_inverted_index.learning | 30, 65 |
| abstract_inverted_index.machine) | 213 |
| abstract_inverted_index.methods, | 32 |
| abstract_inverted_index.patterns | 117 |
| abstract_inverted_index.powerful | 56 |
| abstract_inverted_index.proposed | 182 |
| abstract_inverted_index.services | 12 |
| abstract_inverted_index.signals. | 152 |
| abstract_inverted_index.temporal | 99, 115 |
| abstract_inverted_index.Multipath | 0 |
| abstract_inverted_index.Satellite | 17 |
| abstract_inverted_index.accuracy. | 178 |
| abstract_inverted_index.algorithm | 196 |
| abstract_inverted_index.difficult | 36 |
| abstract_inverted_index.labeling. | 50 |
| abstract_inverted_index.multipath | 41 |
| abstract_inverted_index.objective | 161 |
| abstract_inverted_index.redundant | 169 |
| abstract_inverted_index.satellite | 104, 150 |
| abstract_inverted_index.Navigation | 16 |
| abstract_inverted_index.approaches | 23 |
| abstract_inverted_index.compressed | 133 |
| abstract_inverted_index.downscaled | 142 |
| abstract_inverted_index.extraction | 146 |
| abstract_inverted_index.networks). | 225 |
| abstract_inverted_index.short-term | 76 |
| abstract_inverted_index.structure. | 92 |
| abstract_inverted_index.supervised | 28 |
| abstract_inverted_index.autoencoder | 54, 86, 91, 128 |
| abstract_inverted_index.extraction, | 59 |
| abstract_inverted_index.limitations | 47 |
| abstract_inverted_index.performance | 201 |
| abstract_inverted_index.recognition | 2, 43, 71, 177 |
| abstract_inverted_index.regularizer | 158 |
| abstract_inverted_index.time-series | 105 |
| abstract_inverted_index.correlations | 100 |
| abstract_inverted_index.demonstrated | 193 |
| abstract_inverted_index.unsupervised | 40, 57, 144 |
| abstract_inverted_index.convolutional | 84, 126, 223 |
| abstract_inverted_index.long-duration | 102, 148 |
| abstract_inverted_index.classification | 200 |
| abstract_inverted_index.high-precision | 10 |
| abstract_inverted_index.representation | 134 |
| abstract_inverted_index.absolute-position | 11 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.65229034 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |