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Track-to-track association methodology for operational surveillance scenarios with radar observations

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Ingeniería Aeroespaciales
dc.affiliation.institutoUC3M. Instituto Universitario sobre Modelización y Simulación en Fluidodinámica, Nanociencia y Matemática Industrial Gregorio Millán Barbanyes
dc.contributor.authorPastor Rodríguez, Alejandro
dc.contributor.authorSanjurjo Rivo, Manuel
dc.contributor.authorEscobar, Diego
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2023-06-12T12:18:44Z
dc.date.available2023-06-12T12:18:44Z
dc.date.issued2023-07
dc.description.abstractThis paper proposes a novel track-to-track association methodology able to detect and catalogue resident space objects (RSOs) from associations of uncorrelated tracks (UCTs) obtained by radar survey sensors. It is a multi-target multi-sensor algorithm approach able to associate data from surveillance sensors to detect and catalogue objects. The association methodology contains a series of steps, each of which reduces the complexity of the combinational problem. The main focus are real operational environments, in which brute-force approaches are computationally unaffordable. The hypotheses are scored in the measurement space by evaluating a figure of merit based on the residuals of the observations. This allows us to filter out most of the false hypotheses that would be present in brute-force approaches, as well as to distinguish between true and false hypotheses. The suitability of the proposed track-to-track association has been assessed with a simulated scenario representative of a real operational environment, corresponding to 2 weeks of radar survey data obtained by a single survey radar. The distribution and evolution of the hypotheses along the association process is analysed and typical association performance metrics are included. Most of the RSOs are detected and catalogued and only one false positive is obtained. Besides, the rate of false positives is kept low, most of them corresponding to particular cases or objects with high eccentricity or limited observability.en
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This project has received funding from the “Comunidad de Madrid” under “Ayudas destinadas a la realizacion doctorados industriales” program (project IND2017/TIC7700)en
dc.format.extent17es
dc.identifier.bibliographicCitationPastor, A. P., Sanjurjo-Rivo, M., & Escobar, D. A. (2023). Track-to-track association methodology for operational surveillance scenarios with radar observations. CEAS Space J, 15, 535–551en
dc.identifier.doihttp://dx.doi.org/10.1007/s12567-022-00441-4
dc.identifier.issn1868-2502
dc.identifier.publicationfirstpage535es
dc.identifier.publicationissue4es
dc.identifier.publicationlastpage551es
dc.identifier.publicationtitleCEAS Space Journal (CEAS Space Journal)en
dc.identifier.publicationvolume15es
dc.identifier.urihttps://hdl.handle.net/10016/37469
dc.identifier.uxxiAR/0000032924
dc.language.isoengen
dc.publisherSpringer Natureen
dc.relation.projectIDComunidad de Madrid. IND2017/TIC-7700es
dc.rights© The Author(s) 2022en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaAeronáuticaes
dc.subject.ecienciaAstronomíaes
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.ecienciaFísicaes
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaIngeniería Industriales
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherTrack associationen
dc.subject.otherTrack correlationen
dc.subject.otherCatalogue build-upen
dc.subject.otherObject detectionen
dc.subject.otherSpace surveillanceen
dc.titleTrack-to-track association methodology for operational surveillance scenarios with radar observationsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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