Cluster based sensor scheduling in a target tracking application with particle filtering
dc.authorid | 0000-0001-6556-4104 | |
dc.authorid | 0000-0002-3591-6567 | |
dc.contributor.author | Özfidan, Özgür | en_US |
dc.contributor.author | Bayazıt, Uluğ | en_US |
dc.contributor.author | Çırpan, Hakan Ali | en_US |
dc.date.accessioned | 2019-08-31T12:10:23Z | |
dc.date.accessioned | 2019-08-05T16:03:06Z | |
dc.date.available | 2019-08-31T12:10:23Z | |
dc.date.available | 2019-08-05T16:03:06Z | |
dc.date.issued | 2007 | |
dc.department | Işık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.department | Işık University, Faculty of Engineering, Department of Electrical-Electronics Engineering | en_US |
dc.description | This research has been funded by the The Scientific & Technological Research Council of Turkey (TUB ITAK), Project No: 104E130 and also supported in part by the Research Fund of the University of Istanbul. Project number: 513/05052006. | en_US |
dc.description.abstract | In multi-sensor applications management of sensors is necessary for the classification of data they produce and for the efficient use of sensors as well. One of the important aspects in sensor management is the sensor scheduling. By scheduling the sensors, serious reductions can be achieved in the cost of bandwidth, power, and computation. In this work a simple solution for the problem of sensor scheduling in a multi-sensor target tracking application is presented. Due to non-linearity of the problem itself, proposed solution is presented in the framework of non-linear Bayesian estimation. | en_US |
dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) | en_US |
dc.description.sponsorship | Istanbul University | en_US |
dc.description.version | Publisher's Version | en_US |
dc.identifier.citation | Özfidan, O., Bayazıt, U., & Çırpan, H. A. (2007). Cluster based sensor scheduling in a target tracking application with particle filtering. Paper presented at the 1741-1746. doi:10.1109/ISIE.2007.4374868 | en_US |
dc.identifier.doi | 10.1109/ISIE.2007.4374868 | |
dc.identifier.endpage | 1746 | |
dc.identifier.isbn | 9781424407545 | |
dc.identifier.isbn | 9781424407552 | |
dc.identifier.isbn | 1424407559 | |
dc.identifier.issn | 2163-5137 | |
dc.identifier.issn | 2163-5145 | |
dc.identifier.scopus | 2-s2.0-50049097498 | |
dc.identifier.scopusquality | N/A | |
dc.identifier.startpage | 1741 | |
dc.identifier.uri | https://hdl.handle.net/11729/1808 | |
dc.identifier.uri | https://dx.doi.org/10.1109/ISIE.2007.4374868 | |
dc.identifier.wos | WOS:000252265104001 | |
dc.identifier.wosquality | N/A | |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.indekslendigikaynak | Conference Proceedings Citation Index – Science (CPCI-S) | en_US |
dc.institutionauthor | Bayazıt, Uluğ | en_US |
dc.institutionauthorid | 0000-0001-6556-4104 | |
dc.language.iso | en | en_US |
dc.peerreviewed | Yes | en_US |
dc.publicationstatus | Published | en_US |
dc.publisher | IEEE | en_US |
dc.relation.journal | 2007 IEEE International Symposium on Industrial Electronics, Proceedings, Vols 1-8 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Bandwidth | en_US |
dc.subject | Bayesian estimations | en_US |
dc.subject | Bayesian methods | en_US |
dc.subject | Bayesian networks | en_US |
dc.subject | Chlorine compounds | en_US |
dc.subject | Cluster based sensor scheduling | en_US |
dc.subject | Cluster based | en_US |
dc.subject | Clustering algorithms | en_US |
dc.subject | Clutter (information theory) | en_US |
dc.subject | Costs | en_US |
dc.subject | Data engineering | en_US |
dc.subject | Density filter | en_US |
dc.subject | Electronics industry | en_US |
dc.subject | Filtering theory | en_US |
dc.subject | Industrial electronics | en_US |
dc.subject | Intelligent sensors | en_US |
dc.subject | International symposium | en_US |
dc.subject | Master-slave | en_US |
dc.subject | Multi sensors | en_US |
dc.subject | Multi-sensor applications | en_US |
dc.subject | Non linearities | en_US |
dc.subject | Non-linear | en_US |
dc.subject | Nonlinear Bayesian estimation | en_US |
dc.subject | OF sensors | en_US |
dc.subject | Parameter estimation | en_US |
dc.subject | Particle filtering | en_US |
dc.subject | Pattern clustering | en_US |
dc.subject | Processor scheduling | en_US |
dc.subject | Production control | en_US |
dc.subject | Scheduling | en_US |
dc.subject | Sensor fusion | en_US |
dc.subject | Sensor phenomena and characterization | en_US |
dc.subject | Sensor management | en_US |
dc.subject | Sensor scheduling | en_US |
dc.subject | Sensors | en_US |
dc.subject | Signal filtering and prediction | en_US |
dc.subject | Target tracking | en_US |
dc.subject | Target tracking application | en_US |
dc.subject | Technical presentations | en_US |
dc.title | Cluster based sensor scheduling in a target tracking application with particle filtering | en_US |
dc.type | Conference Object | en_US |
dspace.entity.type | Publication |
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