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Cognitive Fusion for Target Tracking [electronic resource] / by Ioannis Kyriakides.

By: Kyriakides, Ioannis [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Algorithms and Software in Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: VIII, 57 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031015281.Subject(s): Signal processing | Signal, Speech and Image ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621,382 Online resources: Click here to access online
Contents:
Introduction -- Cognitive Fusion -- Cognitive Fusion for Target Tracking with Foveal and Radar Nodes -- Conclusions -- Bibliography -- Author's Biography.
In: Springer Nature eBookSummary: 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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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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