Unscented Kalman Filter Made Easy (with MATLAB source code files)

Are you having problems learning about the Unscented Kalman Filter?

Stop the pain, and start reading the Unscented Kalman Filter Made Easy eBook!

What you get?

• Unscented Kalman Filter Made Easy eBook
• MATLAB Source Code for Book Examples

If you want a sample of my writing check out one of my blog posts on the Kalman Filter!

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