Sangam: A Confluence of Knowledge Streams

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

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dc.creator He, Shaoming
dc.creator Shin, Hyo-Sang
dc.creator Tsourdos, Antonios
dc.date 2018-09-03T15:10:47Z
dc.date 2018-09-03T15:10:47Z
dc.date 2018-08-03
dc.date.accessioned 2022-05-25T16:38:14Z
dc.date.available 2022-05-25T16:38:14Z
dc.identifier Shaoming He, Hyo-Sang Shin and Antonios Tsourdos. Multi-sensor multi-target tracking using domain knowledge and clustering. IEEE Sensors Journal, Available online 3 August 2018
dc.identifier 1530-437X
dc.identifier https://doi.org/10.1109/JSEN.2018.2863105
dc.identifier http://dspace.lib.cranfield.ac.uk/handle/1826/13454
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/182311
dc.description This 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.
dc.language en
dc.publisher IEEE
dc.rights Attribution-NonCommercial 4.0 International
dc.rights http://creativecommons.org/licenses/by-nc/4.0/
dc.subject Multi-sensor multi-target tracking
dc.subject Joint probabilistic data association
dc.subject Detection amplitude
dc.subject DBSCAN clustering
dc.title Multi-sensor multi-target tracking using domain knowledge and clustering
dc.type Article


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