Sensor and Data Fusion

This book PDF is perfect for those who love Multisensor data fusion genre, written by Lawrence A. Klein and published by SPIE Press which was released on 24 April 2024 with total hardcover pages 346. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Sensor and Data Fusion books below.

Sensor and Data Fusion

Author	: Lawrence A. Klein
File Size	: 42,8 Mb
Publisher	: SPIE Press
Language	: English
Release Date	: 24 April 2024
ISBN	: 0819454354
Pages	: 346 pages

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Sensor and Data Fusion by Lawrence A. Klein Book PDF Summary

This book illustrates the benefits of sensor fusion by considering the characteristics of infrared, microwave, and millimeter-wave sensors, including the influence of the atmosphere on their performance. Applications that benefit from this technology include: vehicular traffic management, remote sensing, target classification and tracking- weather forecasting- military and homeland defense. Covering data fusion algorithms in detail, Klein includes a summary of the information required to implement each of the algorithms discussed, and outlines system application scenarios that may limit sensor size but that require high resolution data.

Sensor and Data Fusion by Lawrence A. Klein

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Quantitative Information Fusion for Hydrological Sciences by Xing Cai,T.-C. Jim Yeh

In this rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? Here, two highly qualified scientists edit a volume that takes the angle of computational hydrology and envision one of the science’s future directions – namely, the quantitative integration of high-quality hydrologic field

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Data Fusion Mathematics by Jitendra R. Raol

Fills the Existing Gap of Mathematics for Data FusionData fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component

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Multisensor Data Fusion by Hassen Fourati

Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art

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Data Fusion and Perception by Giacomo Della Riccia,G. Della Riccia,Hanz-Joachim Lenz,Rudolf Kruse

This work is a collection of front-end research papers on data fusion and perceptions. Authors are leading European experts of Artificial Intelligence, Mathematical Statistics and/or Machine Learning. Area overlaps with "Intelligent Data Analysis”, which aims to unscramble latent structures in collected data: Statistical Learning, Model Selection, Information Fusion, Soccer

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Sensor and Data Fusion Concepts and Applications by Lawrence A. Klein

First published in 1993, this Tutorial Text has been revised and updated to provide explanations and examples of data fusion algorithms in areas not covered in the first edition. These include Bayesian inference, artificial neural networks and fuzzy logic. All of the chapters in the first edition have been revised and

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Fuzzy Logic and Information Fusion by Tomasa Calvo Sánchez,Joan Torrens Sastre

This book offers a timely report on key theories and applications of soft-computing. Written in honour of Professor Gaspar Mayor on his 70th birthday, it primarily focuses on areas related to his research, including fuzzy binary operators, aggregation functions, multi-distances, and fuzzy consensus/decision models. It also discusses a number

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Multi Sensor Data Fusion with MATLAB by Jitendra R. Raol

Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly

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