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Cognitive Fusion for Target Tracking / by Ioannis Kyriakides

By: Kyriakides, Ioannis, autor
Material type: materialTypeLabelE-bookSeries: (Synthesis Lectures on Algorithms and Software in Engineering, 1938-1735).Publisher: Cham : Springer International Publishing, 2019Edition: 1st edition 2019.Description: 1 recurso en línea (VIII, 57 páginas).ISBN: 9783031015281.Subject: Radar | Redes de sensores inalámbricasOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- Cognitive Fusion -- Cognitive Fusion for Target Tracking with Foveal and Radar Nodes -- Conclusions -- Bibliography -- Author's Biography.
Summary: The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources. In addition, the use of heterogeneous sensing nodes provides a diversity of information that also enhances estimation performance. This work reviews cognitive systems and presents a cognitive fusion framework for sequential state estimation using adaptive configuration of heterogeneous sensing nodes and heterogeneous data fusion. This work also provides an application of cognitive fusion to the sequential estimation problem of target tracking using foveal and radar sensors.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería TK6580 2019 EB (Browse shelf(Opens below)) Acceso electrónico eBook.01112242
Total holds: 0

Introduction -- Cognitive Fusion -- Cognitive Fusion for Target Tracking with Foveal and Radar Nodes -- Conclusions -- Bibliography -- Author's Biography.

The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources. In addition, the use of heterogeneous sensing nodes provides a diversity of information that also enhances estimation performance. This work reviews cognitive systems and presents a cognitive fusion framework for sequential state estimation using adaptive configuration of heterogeneous sensing nodes and heterogeneous data fusion. This work also provides an application of cognitive fusion to the sequential estimation problem of target tracking using foveal and radar sensors.

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