Multi-sensor multi-target tracking using domain knowledge and clustering

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dc.contributor.authorHe, Shaoming
dc.contributor.authorShin, Hyosang
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2018-09-03T15:10:47Z
dc.date.available2018-09-03T15:10:47Z
dc.date.issued2018-08-03
dc.description.abstractThis paper proposes a novel joint multi-target tracking and track maintenance algorithm over a sensor network. Each sensor runs a local joint probabilistic data association (JPDA) filter using only its own measurements. Unlike the original JPDA approach, the proposed local filter utilises the detection amplitude as domain knowledge to improve the estimation accuracy. In the fusion stage, the DBSCAN clustering in conjunction with statistical test is proposed to group all local tracks into several clusters. Each generated cluster represents the local tracks that are from the same target source and the global estimation of each cluster is obtained by the generalized covariance intersection (GCI) algorithm. Extensive simulation results clearly confirms the effectiveness of the proposed multisensor multi-target tracking algorithm.en_UK
dc.identifier.citationShaoming He, Hyo-Sang Shin and Antonios Tsourdos. Multi-sensor multi-target tracking using domain knowledge and clustering. IEEE Sensors Journal, Available online 3 August 2018en_UK
dc.identifier.issn1530-437X
dc.identifier.urihttps://doi.org/10.1109/JSEN.2018.2863105
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13454
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMulti-sensor multi-target trackingen_UK
dc.subjectJoint probabilistic data associationen_UK
dc.subjectDetection amplitudeen_UK
dc.subjectDBSCAN clusteringen_UK
dc.titleMulti-sensor multi-target tracking using domain knowledge and clusteringen_UK
dc.typeArticleen_UK

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