Learn the Unscented Kalman Filter quickly
This book will guide you through examples step-by-step to do just that!
- Most people never learn the Unscented Kalman Filter because they get tired of reading complex mathematical proofs. Why? Because it doesn’t work!
- Unscented Kalman Filter Made Easy presents the Unscented Kalman Filter framework in small digestible chunks with easy to follow examples!
- Readers learn the best by seeing real life examples with all equations written out accompanied by MATLAB source code. This is the only way readers will understand how the Unscented Kalman Filter works!
- Start this journey right now! By clicking the Amazon or Gumroad link below!
See what my readers are saying about my first book, Kalman Filter Made Easy!
Step-by-Step Examples
Fed up with reading about the simple examples that don’t provide the insight you need for your application? This book includes multiple examples: simple ones and complex ones. By going through multiple types of examples, you will see how the fundamentals of the Kalman Filter remain the same.
Simulation and Analysis
How do you know if your Kalman Filter will work? Most blogposts, technical papers, and posts don’t include this type of information. Understanding the basics of setting up a simulation and analyzing the results will give you the ability to iterate over your filter design until you’re confident.
What's in the book?
Table of Contents
Chapter 1: What is a filter?
• On Uncertainty in Measurements
• Average Filter
• Moving Average Filter
• Exponential Moving Average Filter
• Kalman Filter
Chapter 2: The Kalman Filter Explained Simply
• Kalman Filter Algorithm Overview
Chapter 3: Image Object Tracking Tutorial with a Kalman Filter
• Symbolic Kalman Filter Tutorial
• Step 1: Initialize System State
• Step 2: Reinitialize System State
• Step 3: Predict System State Estimate
• Step 4: Compute the Kalman Gain
• Step 5: Estimate System State and System State Error Covariance Matrix
Chapter 4: Nonlinear Filters
• Why Non-Linear Filters are Needed
• Challenges of Designing and Implementing Nonlinear Filters
• Extended Kalman Filter vs Unscented Kalman Filter
Chapter 5: Extended Kalman Filter – Projectile Example
• What is the Extended Kalman Filter?
• Projectile Example Overview
Chapter 6: Unscented Kalman Filter
• Unscented Kalman Filter Algorithm Overview
• Step 1: Initialization
• Step 2: Reinitialization
• Step 3: Prediction
• Computing Sigma Points
• Unscented Transformation
• Step 4: Computing the Kalman Gain
• Step 5: Estimation 54
• Unscented Kalman Filter – Simple MATLAB Example
Chapter 7: Unscented Kalman Filter – Symbolic Projectile Tutorial
• Step 1: Initialization
o Initialize System State Equations
• Step 2: Reinitialization
o Reinitialize System State Equations
• Step 3: Prediction
o Prediction Equations
• Step 4: Compute the Kalman Gain
o Computing the Kalman Gain Equations
• Step 5: Estimation
o Estimation Equations
Chapter 8: Unscented Kalman Filter – MATLAB Example
• MATLAB Implementation
• Simulation
• Computing Sigma Points
• Unscented Transformation
• Sigma Points – Prediction
• Sigma Points – State-to-Measurement Conversion
• Unscented Kalman Filter
• Testing
• Analyzing Results
Chapter 9: Getting Started with Data and Simulation
• Integrate Filter into System
• Use Data from Previous System Operation
• Find Comparable Data from Different System
• Simulation
• Simulation Requirements
Chapter 10: Getting Started with Performance Analysis
• Logging Data
• Defining Requirements
• Validating Requirements
* All MATLAB example source code files will be provided with the book
About the Author
William completed his Bachelor and Master of Science in Mechanical Engineering with concentrations in Fluid and Mass Transfer and Manufacturing Technology. William has a broad range of experience from working as a mechanical engineer in the chemical processing, construction, tech, and defense industries. William currently works in the IoT industry designing Kalman Filters for low power sensors.