Kalman Filter Made Easy A Beginners Guide to the Kalman Filter and Extended Kalman Filter with Real Life Examples Supported by Python Source Code |
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| Author:
| Franklin, William |
| ISBN: | 979-8-8461-4081-3 |
| Publication Date: | Aug 2022 |
| Publisher: | Independently Published
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| Book Format: | Paperback |
| List Price: | USD $39.99 |
Book Description:
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The Kalman Filter Made Easy book is written for beginners who want to understand how a Kalman Filter works without getting lost in the dense theory of the Kalman Filter for an affordable price. The author takes a simple approach of starting with the moving average filter and exponential moving average filter and steps up to the Kalman Filter. This methodical approach allows the reader to understand why they are filtering which then allows them to understand how to design,...
More Description The Kalman Filter Made Easy book is written for beginners who want to understand how a Kalman Filter works without getting lost in the dense theory of the Kalman Filter for an affordable price. The author takes a simple approach of starting with the moving average filter and exponential moving average filter and steps up to the Kalman Filter. This methodical approach allows the reader to understand why they are filtering which then allows them to understand how to design, implement, and evaluate a Kalman Filter. Additionally, it covers the Extended Kalman Filter and how it differs in application and implementation from the Kalman Filter.
Why should you learn about the Kalman Filter?
The Kalman Filter is a fundamental algorithm that has made its way into rockets, financial modeling, space ships, autonomous vehicles, radar, lidar, cameras, smartphones, IOT devices and many more physical systems. If you are a software engineer or systems designer, you will most likely run into a Kalman Filter sometime in your career.
Who is this book for?
If you are looking for a theoretical and mathematical dense book about the Kalman Filter, this is not the right book for you. If you are looking to understand how the Kalman Filter works with real life examples supported by the Python source code used for the real-time application, you are in the right place. If you are an undergraduate student, graduate student, or working professionally with control theory or robotics, this book will provide the necessary primer on the Kalman Filter in a simple to understand way.