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.

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 book is a very clear and concise description of Kalman filter basics. The author does a great job of getting across to the reader why Kalman filters are so useful as well as the fundamentals for using them. I appreciate the fact that he goes right into doing practical examples and leaves the theoretical proofs to the many other resources already existing, speaking of which there are so many bad descriptions of Kalman filters out there where the author is not so much trying to inform the reader but show how much equations they can write and even with all the equations still not get anything useful across. This book on the other hand is one of the very few useful ones on the subject. I am looking forward to reading through his next book.

Kalman filters are more difficult to understand than more conventional digital filters. The author does a good job of explaining their theory and design but the text would have been benefited from an editor.

This is book is printed using excellent quality white paper and is of 8X11 inches size book printed using multiple color graphs, python code color syntax and some figures also in color. Also many formulas and equations are printed in multiple colors. Color printing definitely helps in understanding the information relatively easily.Author gave, his email address and this book's website URL address in the book and author has emailed to me the book's example Python programs. I ran the programs quickly and easily using Spyder GUI software.The Spider GUI displayed multiple color graphs as output. Python programs are very easier to read and understand using Spyder GUI syntax highlighting.In Chapter 4, author Franklin has analyzed bitcoin data using Microsoft Excel and calculated Kalman gain etc. After going through the Excel implementation of Kalman filter equations, understanding Kalman filter Python programs in later chapters have been relatively easier.Overall, this book is an excellent introduction to the Kalman filter using Python.I definitely recommend this book to others.

William Franklin has done an excellent job of explaining this complex tool in a way that brings it within the grasp of engineers who are not all that comfortable with linear algebra and matrix math. His approach is to point out that the Kalman Filter behaves like other filters, such as averaging or moving average type, but with a gain value that gets calculated on the fly for each measured value. The method of the calculation is explained in a way that makes sense but avoids getting bogged down in excruciating detail. Examples start with a one-dimensional problem that can be solved on a spreadsheet, then more complicated ones using Python and the numpy library to crunch through the matrix math.

I have been trying to learn Kalman Filtering for several years. I even purchased a $100 text book on it. I was unable to understand the preface let alone the first introductory chapter. This book on the other hand was enough to get me going. I must agree that with others that the screen shots are hard to read, but in fairness, I got the source code emailed to me as soon as I registered as indicated on page 37. My only other comment to date was the omission on how to compute the alpha term for the exponential filter. I realize the whole discussion on the exponential filter was as an intro to Kalman, but a foot note giving the equation (alpha = 1 - e^(dt/tau)) would have been nice.

I like the book. I don’t like paying for a new book and getting a used book.

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!

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