Introduction to Random Signals and Applied Kalman Filtering: With MATLAB Exercises
The Fourth Edition to the Introduction of Random Signals and Applied Kalman Filtering is updated to cover innovations in the Kalman filter algorithm and the proliferation of Kalman filtering applications from the past decade. The text updates both the research advances in variations on the Kalman filter algorithm and adds a wide range of new application examples. Several chapters include a significant amount of new material on applications such as simultaneous localization and mapping for autonomous vehicles, inertial navigation systems and global satellite navigation systems.
Why Read This Book
You will get a practical, example-driven introduction to stochastic signals and state estimation that shows not just theory but how to implement Kalman filters in MATLAB. The Fourth Edition brings modern applications — SLAM, INS/GNSS, and other navigation and tracking problems — so you can see how estimation algorithms are used in real engineering systems.
Who Will Benefit
Engineers and graduate students with some signals/probability background who need to design, implement, or evaluate Kalman filters and related estimators for navigation, radar, audio/communications, or control applications.
Level: Intermediate — Prerequisites: Undergraduate calculus and linear algebra, basic probability and random processes, intro to signals and systems, and familiarity with MATLAB (or willingness to learn it).
Key Takeaways
- Model random signals and compute autocorrelation functions and power spectral densities for engineering problems
- Implement discrete- and continuous-time Kalman filters from state-space models and assess filter performance
- Apply nonlinear filters (EKF, UKF) and smoothing algorithms to practical problems such as SLAM and INS/GNSS
- Design and tune process and measurement noise covariances and perform covariance analysis and consistency checks
- Perform parameter estimation and adaptive filtering and understand numerical/implementation issues when moving MATLAB prototypes to deployment
- Translate theoretical estimation results into MATLAB exercises and worked examples you can adapt to radar, communications, audio, and speech problems
Topics Covered
- 1. Introduction to Random Signals and Estimation
- 2. Probability Review and Stochastic Processes
- 3. Correlation Functions and Power Spectral Density
- 4. Linear Systems Driven by Random Inputs
- 5. State-Space Modeling and Observability
- 6. Derivation and Implementation of the Discrete Kalman Filter
- 7. Continuous-Time Filters, Steady-State Filters, and the Riccati Equation
- 8. Nonlinear Filtering: Extended and Unscented Kalman Filters
- 9. Smoothing, Fixed-Interval and Fixed-Lag Algorithms
- 10. Parameter Estimation, Adaptive Filtering and Model Identification
- 11. Numerical Issues, Robustness, and Practical Implementation
- 12. Applications: INS/GNSS, SLAM, Radar, Communications, and Audio/Speech Examples
- 13. MATLAB Exercises and Example Code
Languages, Platforms & Tools
How It Compares
Covers similar ground to Grewal & Andrews and Dan Simon's Optimal State Estimation but places stronger emphasis on random-signal background and hands-on MATLAB exercises with expanded application examples (SLAM, INS/GNSS).
