Kalman Filtering: Theory and Practice with MATLAB (IEEE Press)
The definitive textbook and professional reference on Kalman Filtering fully updated, revised, and expanded
This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control.
Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
Why Read This Book
You will get a rigorous, practical treatment of Kalman filtering that bridges theory and real-world implementation, with extensive MATLAB examples you can run and adapt. The authors expose common pitfalls, numerical issues, and application recipes (GNSS/INS, radar, tracking, communications) so you can design, tune, and validate estimators for practical systems.
Who Will Benefit
Advanced undergraduates, graduate students, and practicing engineers in signal processing, navigation, radar, and communications who need a deep, application-oriented reference on Kalman filtering and state estimation.
Level: Advanced — Prerequisites: Linear algebra (matrix algebra, eigenvalues), probability and stochastic processes (Gaussian random variables, covariance), basic state-space systems and control concepts, and familiarity with MATLAB.
Key Takeaways
- Implement and simulate the discrete and continuous Kalman filter and smoother using MATLAB
- Diagnose and fix numerical and modeling issues (covariance inconsistencies, divergence, square-root filters)
- Adapt Kalman filters to nonlinear problems (Extended and Unscented Kalman Filters) and evaluate their performance
- Integrate and apply filters to real-world systems such as GNSS/INS, radar tracking, and communications receivers
- Design and tune adaptive estimation strategies and understand the impact of process/measurement noise modeling
Topics Covered
- Introduction and Notation; overview of estimation problems
- Mathematical and Statistical Preliminaries (random vectors, covariance, linear algebra)
- Linear Dynamic Models and State-Space Formulations
- The Continuous and Discrete Kalman Filter
- Filter Implementation: Numerical Stability, Joseph Form, and Square‑Root Methods
- Kalman Smoothers and Batch Estimation
- Nonlinear Filtering: Extended and Unscented Kalman Filters (and practical variants)
- Adaptive Estimation and Covariance Tuning
- Applications: GNSS, Inertial Navigation Systems, and Sensor Error Modeling
- Applications: Radar/Target Tracking and Communication System Examples
- Practical Issues: Initialization, Observability, Fault Detection, and Robust Filtering
- MATLAB Examples, Code Listings, and Simulation Case Studies
Languages, Platforms & Tools
How It Compares
More application- and MATLAB-focused than Gelb's classic Applied Optimal Estimation and more comprehensive in practical numerical issues and GNSS/INS examples than Dan Simon's Optimal State Estimation.
