Track estimation with binary derivative observations
Ickowicz, Adrien (2011), Track estimation with binary derivative observations. https://basepub.dauphine.fr/handle/123456789/6774
TypeDocument de travail / Working paper
External document linkhttp://hal.archives-ouvertes.fr/hal-00610181/fr/
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Abstract (EN)We 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.
Subjects / Keywordstracking algorithm; single index model; maximum likelihood; binary sensors; parameter estimation
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