Kalman Filter Made Easy: A Beginners Guide to the Kalman Filter and Extended Kalman Filter with Real Life Examples Supported by Python Source Code

I bought this book because I am not a math guy, and I was recommended to use a Kalman filter to reduce the amount of noise returned from a machine learning model I am running in my Android app. Most of the online resources did not give enough background information to provide me with the context necessary to understand what was being discussed, nor did they take the time to break down the algorithm and provide examples of state mutation through multiple iterations. This last piece is important since some previous values affect the state of the values of the current iteration.This book provides a concise, thorough, and easily readable description of Kalman Filters. It details every step and includes various modifications made to the algorithm for specific use cases. Of course, it includes sample code in Python, and if you email the author, they will provide additional source code samples.It is very admirable that the author(s) went out of their way to write this book. It shows their true passion for Kalman Filters, and those who need to implement them are fortunate to have this resource because these filters can be quite the rabbit hole.

Ce livre est parfait pour comprendre en détail le filtrage de Kalman si l'on a déjà les bases du controle par variables d'état et du filtrage numérique. L'approche du filtrage par coefficients exponentiels est originale, j'aurais aimé avoir cela durant mes études. Ce n'est pas le seul livre que je possède sur le sujet mais c'est le premier qui a provoqué quelques déclics de compréhension.

Kalman filter: Although I studied physics, it was really confusing and more or less a "black box" for me to use Kalman filters anywhere. After reading William's book: I had several "aha" and "wow" effects and could easily build my own Kalman filter application to get much better readings from a BNO055 accelerometer. This book is highly recommended! Thank you William!

his book is greate as an introduction to the Kalman filter. The progression from the average, moving average, exponent filters to the Kalman filter helps a lot in understanding and demystifying what is going on. The python and matlab code is extremely helpful.

This is a good book for starting designing kalmanfilter applications. With this book you become fast familiar with the kalmanfilter.