Boudjemaa, R; Forbes, A B (2004) Parameter estimation methods in data fusion. NPL Report. CMSC 38/04

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Abstract

Many metrology systems involve more than one sensor and the analysis of the data produced by these systems has to take into account the characteristics of the data arising from the different sensors. For well-characterized systems in which the behaviour of the sensors is known a priori, appropriate methods for estimating the parameters of the system from measurement data can be derived according to maximum likelihood principles. For systems subject to unknown or unpredictable variations, estimation methods that can adapt to these variations are required. In this report, we show how a class of such methods based on Bayesian approaches can be defined and illustrate their behaviour on a number of examples relevant to metrology.

Item Type:	Report/Guide (NPL Report)
NPL Report No.:	CMSC 38/04
Subjects:	Mathematics and Scientific Computing Mathematics and Scientific Computing > Modelling
Last Modified:	02 Feb 2018 13:16
URI:	http://eprintspublications.npl.co.uk/id/eprint/2891

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