Data Fusion: Concepts and Ideas (2nd Edition) by H. B. Mitchell

By H. B. Mitchell

-Comprehensive advent to the recommendations and proposal of multisensor facts fusion
-Extensively revised moment version of the e-book: "Multi-Sensor info Fusion: An Introduction"
-Illustrated with many real-life examples taken from a various diversity of functions and comprises an in depth checklist of contemporary references
-Self-contained booklet, no earlier wisdom of multi-sensor facts fusion is assumed

“Data Fusion: thoughts and Ideas” offers a finished advent to the strategies and concept of multisensor facts fusion. This textbook is an greatly revised moment variation of the author's winning ebook: "Multi-Sensor info Fusion: An Introduction". The publication is self-contained and no prior wisdom of multi-sensor information fusion is thought. The reader is made accustomed to instruments taken from quite a lot of varied matters together with: neural networks, sign processing, statistical estimation, monitoring algorithms, computing device imaginative and prescient and keep an eye on thought that are mixed by utilizing a typical statistical framework. as a result, the underlying trend of relationships that exists among different methodologies is made obtrusive. The booklet is illustrated with many real-life examples taken from a various variety of functions and includes an in depth record of contemporary references. the hot thoroughly revised and up-to-date version contains approximately 70 pages of latest fabric together with a whole new bankruptcy in addition to nearly 30 new sections, 50 new examples and a hundred new references in addition to extra Matlab code the place acceptable.

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Extra resources for Data Fusion: Concepts and Ideas (2nd Edition)

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This includes the name of the physical property which was measured by the sensor and the units in which it is measured. Often the units are defined implicitly in the way the system processes the measurement value. Spatial Location x. This is the position in space to which the measured physical property refers. In many cases the spatial location is not given explicitly and is instead defined implicitly as the position of the sensor element [2] . Time Instant t. This is the time when the physical property was measured.

2 σ m=1 m ∑ Since the sources Sm all supply the same piece of information, then y1 = y = y2 = . . = yM , σ1 = σ = σ2 = . . = σM , and thus 2 σML = σ 2 /M . yML = y , The 1/M reduction in uncertainty is clearly incorrect: it occurs because the node F assumes that each (ym , σm2 ), m ∈ {1, 2, . . , M}, represents an independent piece of information. In the method of covariance intersection we solve this problem by fusing the measurements ym , m ∈ {1, 1, . . , M}, using a weighted maximum likelihood estimate.

Managing data incest in a distributed sensor network. In: Proc. IEEE Int. Conf. , vol. 5, pp. 269–272 (2003) 15. : Robust sensor fusion: analysis and applications to audio-visual speech recognition. Mach. Learn. 32, 85–100 (1998) 16. : Integration of multiple cues in biometric systems. MSc thesis, Michagan State University (2005) 17. : Fault-tolerant clock synchronization for embedded distributed multi-cluster systems. PhD thesis, Institut fur Technische Informatik, Technischen Universitat Wien (2002) 18.

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