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Track estimation with binary derivative observations

dc.contributor.authorIckowicz, Adrien
dc.date.accessioned2011-07-25T09:12:15Z
dc.date.available2011-07-25T09:12:15Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6774
dc.language.isoenen
dc.subjecttracking algorithmen
dc.subjectsingle index modelen
dc.subjectmaximum likelihooden
dc.subjectbinary sensorsen
dc.subjectparameter estimationen
dc.subject.ddc621.3en
dc.titleTrack estimation with binary derivative observationsen
dc.typeDocument de travail / Working paper
dc.description.abstractenWe focus in this paper in the estimation of a target trajectory defined by whether a time constant parameter in a simple stochastic process or a random walk with binary observations. The binary observation comes from binary derivative sensors, that is, the target is getting closer or moving away. Such a binary obervation has a time property that will be used to ensure the quality of a max-likelihood estimation, through single index model or classification for the constant velocity movement. In the second part of this paper we present a new algorithm for target tracking within a binary sensor network when the target trajectory is assumed to be modeled by a random walk. For a given target, this algorithm provides an estimation of its velocity and its position. The greatest improvements are made through a position correction and velocity analysis.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages14en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00610181/fr/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelTraitement du signalen


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