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Springer Kalman Filtering and Information Fusion - Springer Technical Reference

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Description

Kalman Filtering and Information Fusion is a comprehensive technical reference that explores one of the 20th century's most significant discoveries in digital information processing. This Springer publication guides readers through the complexities of managing various uncertainties in single and multi-system environments, offering adaptive estimation solutions tailored to real-world applications. The book provides in-depth coverage of Kalman filtering technology as applied to multi-sensor and multi-agent systems, making it essential for engineers and researchers working with intelligent robots, autonomous vehicles, smart home systems, smart building infrastructure, and other advanced technological implementations. Whether you're designing sensor fusion algorithms or optimising information processing in complex systems, this technical reference delivers the theoretical foundations and practical insights needed to master Kalman filtering methodologies and their modern applications.

Key Features

Kalman filtering fundamentals and theory presented as a key technology for digital information processing systems.

Adaptive estimation solutions address multiple uncertainties within single plant environments and complex multi-sensor architectures.

Multi-sensor and multi-agent system applications covered in detail for intelligent robots, autonomous cars, and smart infrastructure.

Springer's authoritative technical reference provides comprehensive guidance on implementing Kalman filtering in modern digital systems.

Smart home and smart building integration strategies demonstrate real-world applications of information fusion technology.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
06 December 2020
Listed Since
10 November 2020

Barcode

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