Chapter 3. Random Processes and Stochastic Systems

  1. Mohinder S. Grewal PhD, PE1 and
  2. Angus P. Andrews PhD2

Published Online: 28 MAY 2002

DOI: 10.1002/0471266388.ch3

Kalman Filtering: Theory and Practice Using MATLAB ®, Second Edition

Kalman Filtering: Theory and Practice Using MATLAB ®, Second Edition

How to Cite

Grewal, M. S. and Andrews, A. P. (2002) Random Processes and Stochastic Systems, in Kalman Filtering: Theory and Practice Using MATLAB ®, Second Edition, John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471266388.ch3

Author Information

  1. 1

    California State University at Fullerton

  2. 2

    Rockwell Science Center, Thousand Oaks, California

Publication History

  1. Published Online: 28 MAY 2002
  2. Published Print: 2 JAN 2002

ISBN Information

Print ISBN: 9780471392545

Online ISBN: 9780471266389

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Keywords:

  • random processes;
  • stochastic systems;
  • probability;
  • random variables;
  • statistical properties;
  • linear system models;
  • shaping filters;
  • state augmentation;
  • covariance propagation equations;
  • orthogonality

Summary

In this chapter, some of the basic notions and mathematical models of statistical and deterministic mechanics are combined into a stochastic system model, which represents the state of knowledge about a dynamic system. These models represent what we know about a dynamic system, including a quantitative model for our uncertainty about what we know.