Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/116994
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorHe, Z-
dc.creatorMao, Y-
dc.creatorYang, Y-
dc.creatorChen, W-
dc.date.accessioned2026-01-21T03:54:40Z-
dc.date.available2026-01-21T03:54:40Z-
dc.identifier.issn2662-9291-
dc.identifier.urihttp://hdl.handle.net/10397/116994-
dc.language.isoenen_US
dc.publisherSpringerOpenen_US
dc.rights© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rightsThe following publication He, Z., Mao, Y., Yang, Y. et al. An improved track-before-detect method for moving target detection using GNSS reflected signals. Satell Navig 6, 24 (2025) is available at https://doi.org/10.1186/s43020-025-00176-7.en_US
dc.subjectDynamic programmingen_US
dc.subjectGlobal navigation satellite systemen_US
dc.subjectLong-time hybrid integrationen_US
dc.subjectMoving target detectionen_US
dc.subjectTrack-before-detecten_US
dc.titleAn improved track-before-detect method for moving target detection using GNSS reflected signalsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume6-
dc.identifier.issue1-
dc.identifier.doi10.1186/s43020-025-00176-7-
dcterms.abstractGlobal Navigation Satellite System (GNSS) reflected signals have been used to detect moving targets through long-time integration processing. However, mismatches arise between the target’s actual motion and the assumed motion model over a long integration time, degrading detection performance. To address this problem, this paper proposes an improved Track-before-Detect (TbD) method characterized by a two-stage architecture. In the first stage, current Long-Time Hybrid Integration (LTHI) techniques are employed to correct the range and Doppler migrations caused by the target’s motion, thereby concentrating the target energy within an individual scan duration. In the second stage, plot lists extracted from multiple scans are recursively processed to further accumulate target energy by using the target’s kinematic constraints across different scans. Finally, target energy is enhanced sufficiently for reliable detection, while also enabling the acquisition of the target’s motion parameters over all scans. Compared to the existing Dynamic Programming (DP)-TbD method, the proposed method can exploit the characteristics of the stack of integrated range and Doppler maps produced by the LTHI techniques to improve algorithm execution efficiency without sacrificing detection performance and parameter estimation accuracy. The results from the simulations and field trials confirm the effectiveness of the proposed method in detecting maneuvering and non-maneuvering targets. Meanwhile, the proposed method achieves detection performance and motion parameter estimation errors comparable to the existing DP-TbD method, while significantly lowering the computational time.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSatellite navigation, Dec. 2025, v. 6, no. 1, 24-
dcterms.isPartOfSatellite navigation-
dcterms.issued2025-12-
dc.identifier.scopus2-s2.0-105014891696-
dc.identifier.eissn2662-1363-
dc.identifier.artn24-
dc.description.validate202601 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by the National Natural Science Foundation of China (Grant No. 524031511, 42274051).en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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