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Kalman Filtering and Information Fusion

  • Hongbin Ma
  • Liping Yan
  • Yuanqing Xia
  • Mengyin Fu
Book

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Kalman Filtering: Preliminaries

    1. Front Matter
      Pages 1-1
    2. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 3-9
    3. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 11-18
  3. Kalman Filtering for Uncertain Systems

    1. Front Matter
      Pages 19-19
    2. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 21-49
    3. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 71-94
    4. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 95-118
    5. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 119-146
    6. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 147-161
  4. Kalman Filtering for Multi-sensor Systems

    1. Front Matter
      Pages 163-163
    2. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 199-222
  5. Kalman Filtering for Multi-agent Systems

    1. Front Matter
      Pages 239-239
    2. Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
      Pages 241-271

About this book

Introduction

This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques.
Overall, the book’s goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields.
To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus.

Keywords

Kalman filter information fusion uncertainty multi-agent systems multi-sensor systems

Authors and affiliations

  • Hongbin Ma
    • 1
  • Liping Yan
    • 2
  • Yuanqing Xia
    • 3
  • Mengyin Fu
    • 4
  1. 1.School of AutomationBeijing Institute of TechnologyBeijingChina
  2. 2.School of AutomationBeijing Institute of TechnologyBeijingChina
  3. 3.School of AutomationBeijing Institute of TechnologyBeijingChina
  4. 4.School of AutomationBeijing Institute of TechnologyBeijingChina

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