Titlebook: Data Fusion: Concepts and Ideas; H B Mitchell Textbook 2012Latest edition Springer-Verlag Berlin Heidelberg 2012 Bayesion Probabilistic Fr

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H B MitchellComprehensive introduction to the concepts and idea of multisensor data fusion.Extensively revised second edition of the book: "Multi-Sensor Data Fusion: An Introduction".Illustrated with many real-li

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Joyce C. Niland PhD,Julie Hom BS time axis ... Temporal alignment is one of the basic processes required for creating a common representational format. It often plays a critical role in applications involving in many multi-sensor data fusion applications. This is especially true for applications operating in real-time (see Sect. 2.4.2).

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Richard Ashcroft MA, PhD, FHEA, FRSBf . multi-dimensional sensor observations ..,. ∈ {1,2,…,.}, where .. We suppose the .. were generated by a parametric distribution .(.|.,.), where . = (..,..,…,..). are the parameters and . is any background information we may have. Then parameter estimation involves estimating . from y.

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round for easy identification and advancd material is marked with an asterisk. .The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familarity 978-3-642-43730-4978-3-642-27222-6

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Architecture,her node via an exchange of messages. An algorithmic description of the fusion block is provided by the software which is embedded in the nodes and which determines the behaviour of the block and coordinates its activities.

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Radiometric Normalization,t and radiometric normalization. The reader should be careful not to confuse the two terms..In many multi-sensor data fusion applications radiometric normalization is the primary fusion algorithm. In Table 8.1 we list some of these applications together with the classification of the type of fusion algorithm involved.