Kalman Filter Made Easy eBook (With Python Source Code!)

Are you having trouble learning about the Kalman Filter?

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

What you get?

• Kalman Filter Made Easy eBook
• Python Source Code for Book Examples
• Microsoft Excel Worksheet for Book Example

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?

• Average Filter
• Moving Average Filter
• Exponential Moving Average Filter
• Kalman Filter

Chapter 2: The Kalman Filter Explained Simply

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: Kalman Filter Basic Example

• Designing the System Model
• Executing the Filter
• Changing the System Model

Chapter 5: Kalman Filter - Python Example - Estimating Velocity from Position

• Computing Measurements
• Filtering Measurements
• Testing Kalman Filter
• Plot Kalman Filter Results
• Analyze Kalman Filter Results

Chapter 6: Extended Kalman Filter - Python Example

• Extending Kalman Filter Algorithm
• Extended Kalman Filter - Python Implementation
• Computing Measurements
• Testing Extended Kalman Filter

Chapter 7: Getting Started with Data and Simulation

• Integrating Filter into System
• Use Data from Previous System Operation
• Simulation

Chapter 8: Getting Started with Performance Analysis

• Logging Data
• Defining Requirements
• Validating Requirements

Chapter 9: Sensor Fusion

• What is Sensor Fusion
• Implementation Differences

Appendix A: Kalman Filter Derivation

• Assumptions
• Prediction
• Estimation
• Kalman Gain

* All Python example source code files will be provided with the book