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Kalman and Bayesian Filters in Python
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  • Title: Kalman and Bayesian Filters in Python
  • Author(s) Roger R Labbe
  • Publisher: GitHub (2020); eBook (Creative Commons Licensed)
  • License(s): CC BY 4.0 and The MIT License (Source Code)
  • Paperback: N/A
  • eBook: Jupyter Notebook and PDF (506 pages)
  • Language: English
  • ISBN-10/ASIN: N/A
  • ISBN-13: N/A
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Book Description

This book is an introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn?

The book focuses on building intuition and experience, not formal proofs. Includes Kalman filters, extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

The book teaches you how to solve these sorts of filtering problems. It uses many different algorithms, but they are all based on Bayesian Probability. In simple terms Bayesian probability determines what is likely to be true based on past information.

The motivation for this book came out of my desire for a gentle introduction to Kalman filtering. I'm a software engineer that spent almost two decades in the avionics field, and so I have always been 'bumping elbows' with the Kalman filter, but never implemented one myself.

This book is for the hobbyist, the curious, and the working engineer that needs to filter or smooth data.

About the Authors
  • Roger R Labbe develops software for tracking, navigation, mapping, 2D and 3D graphics, avionics, computer vision, statistical analysis, and embedded system. Author of popular book on Kalman filtering. Strong focus on delivering on time and in-budget, and effectively lead at the technical to program level.
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