An introduction to Kalman filtering with MATLAB examples /

• 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 Publishers, [2014]
• Series: Synthesis lectures on signal processing ; #12.
• Subjects: MATLAB.; Kalman filtering.; TECHNOLOGY & ENGINEERING / Engineering (General); dynamical system; parameter estimation; tracking; state space model; sequential; Bayesian estimation; linearity; Gaussian noise; Kalman filter; Electronic books.
• Online Access: View fulltext via EzAccess

The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applicati...

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.