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An introduction to Kalman filtering with MATLAB examples / Narayan Kovvali, Mahesh Banavar, and Andreas Spanias.

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Bibliographic Details
Online Access: Full Text (via Morgan & Claypool)
Full Text (via Morgan & Claypool)
Main Authors: Kovvali, Narayan V. S. K. (Author), Banavar, Mahesh K. (Author), Spanias, Andreas (Author)
Format: eBook
Language:English
Published: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Series:Synthesis lectures on signal processing (Online) ; # 12.
Subjects:
Kalman filtering.
MATLAB.
Table of Contents:
  • 1. Introduction
  • 2. The estimation problem
  • 2.1 Background
  • 2.1.1 Example: maximum-likelihood estimation in Gaussian noise
  • 2.2 Linear estimation
  • 2.3 The Bayesian approach to parameter estimation
  • 2.3.1 Example: estimating the bias of a coin
  • 2.4 Sequential Bayesian estimation
  • 2.4.1 Example: the 1-D Kalman filter
  • 3. The Kalman filter
  • 3.1 Theory
  • 3.2 Implementation
  • 3.2.1 Sample MATLAB code
  • 3.2.2 Computational issues
  • 3.3 Examples
  • 3.3.1 Target tracking with radar
  • 3.3.2 Channel estimation in communications systems
  • 3.3.3 Recursive least squares (RLS) adaptive filtering
  • 4. Extended and decentralized Kalman filtering
  • 4.1 Extended Kalman filter
  • 4.1.1 Example: predator-prey system
  • 4.2 Decentralized Kalman filtering
  • 4.2.1 Example: distributed object tracking
  • 5. Conclusion
  • Notation
  • Bibliography
  • Authors' biographies.

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