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Gnot, S.; Rafajłowicz, Ewaryst; Urbańska-Motyka, Agnieszka

doi:10.1023/a:1012426923971

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Applications To Random Linear Models1

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Cited by 4 publications

(1 citation statement)

References 7 publications

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“…In the next section we illustrate this approach to a special model which was used by Gnot et al [4] for measurement descriptions provided by several sensors.…”

Section: Applications To Random Linear Modelsmentioning

confidence: 99%

On admissibility of linear estimators in models with finitely generated parameter space

Synówka-Bejenka¹,

Zontek²

2016

Kybernetika

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“…In the next section we illustrate this approach to a special model which was used by Gnot et al [4] for measurement descriptions provided by several sensors.…”

Section: Applications To Random Linear Modelsmentioning

confidence: 99%

On admissibility of linear estimators in models with finitely generated parameter space

Synówka-Bejenka¹,

Zontek²

2016

Kybernetika

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On geometry of the set of admissible invariant quadratic estimators in balanced two variance components model

Neumann

Zontek

2004

Statistical Papers

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Sensor network design for the estimation of spatially distributed processes

Uciński¹,

Patan²

2010

International Journal of Applied Mathematics and Computer Science

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In a typical moving contaminating source identification problem, after some type of biological or chemical contamination has occurred, there is a developing cloud of dangerous or toxic material. In order to detect and localize the contamination source, a sensor network can be used. Up to now, however, approaches aiming at guaranteeing a dense region coverage or satisfactory network connectivity have dominated this line of research and abstracted away from the mathematical description of the physical processes underlying the observed phenomena. The present work aims at bridging this gap and meeting the needs created in the context of the source identification problem. We assume that the paths of the moving sources are unknown, but they are sufficiently smooth to be approximated by combinations of given basis functions. This parametrization makes it possible to reduce the source detection and estimation problem to that of parameter identification. In order to estimate the source and medium parameters, the maximum-likelihood estimator is used. Based on a scalar measure of performance defined on the Fisher information matrix related to the unknown parameters, which is commonly used in optimum experimental design theory, the problem is formulated as an optimal control one. From a practical point of view, it is desirable to have the computations dynamic data driven, i.e., the current measurements from the mobile sensors must serve as a basis for the update of parameter estimates and these, in turn, can be used to correct the sensor movements. In the proposed research, an attempt will also be made at applying a nonlinear model-predictive-control-like approach to attack this issue.

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Cited by 4 publications

References 7 publications

On admissibility of linear estimators in models with finitely generated parameter space

On admissibility of linear estimators in models with finitely generated parameter space

On geometry of the set of admissible invariant quadratic estimators in balanced two variance components model

Sensor network design for the estimation of spatially distributed processes

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