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Kalman Filtering

with Real-Time Applications

  • Textbook
  • © 1991

Overview

Authors:
  1. Charles K. Chui

    1. Department of Mathematics and Department of Electrical Engineering, Texas A&M University, College Station, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Guanrong Chen

    1. Department of Electrical Engineering, University of Houston, Houston, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Springer Series in Information Sciences (SSINF, volume 17)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Preliminaries

    • Charles K. Chui, Guanrong Chen
    Pages 1-19

  3. Kalman Filter: An Elementary Approach

    • Charles K. Chui, Guanrong Chen
    Pages 20-32

  4. Orthogonal Projection and Kalman Filter

    • Charles K. Chui, Guanrong Chen
    Pages 33-48

  5. Correlated System and Measurement Noise Processes

    • Charles K. Chui, Guanrong Chen
    Pages 49-66

  6. Colored Noise

    • Charles K. Chui, Guanrong Chen
    Pages 67-76

  7. Limiting Kalman Filter

    • Charles K. Chui, Guanrong Chen
    Pages 77-96

  8. Sequential and Square-Root Algorithms

    • Charles K. Chui, Guanrong Chen
    Pages 97-107

  9. Extended Kalman Filter and System Identification

    • Charles K. Chui, Guanrong Chen
    Pages 108-130

  10. Decoupling of Filtering Equations

    • Charles K. Chui, Guanrong Chen
    Pages 131-142

  11. Notes

    • Charles K. Chui, Guanrong Chen
    Pages 143-157

  12. Back Matter

    Pages 158-195
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Keywords

About this book

In addition to making a number of minor corrections and updat­ ing the references, we have expanded the section on "real-time system identification" in Chapter 10 of the first edition into two sections and combined it with Chapter 8. In its place, a very brief introduction to wavelet analysis is included in Chapter 10. Although the pyramid algorithms for wavelet decompositions and reconstructions are quite different from the Kalman filtering al­ gorithms, they can also be applied to time-domain filtering, and it is hoped that splines and wavelets can be incorporated with Kalman filtering in the near future. College Station and Houston Charles K. Chui September 1990 Guanrong Chen Preface to the First Edition Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and min­ imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fire control. With the recent development of high-speed computers, the Kalman filter has become more use­ ful even for very complicated real-time applications.

Authors and Affiliations

  • Department of Mathematics and Department of Electrical Engineering, Texas A&M University, College Station, USA

    Charles K. Chui

  • Department of Electrical Engineering, University of Houston, Houston, USA

    Guanrong Chen

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