Kalman and Bayesian Filters in Python (2018)

Kalman and Bayesian filters blend our noisy and limited knowledge of how a system behaves with the noisy and limited sensor readings to produce the best possible estimate of the state of the system. A few simple probability rules, some intuition about how we integrate disparate knowledge to explain events in our everyday life and the core concepts of the Kalman filter are accessible. If you are serious about Kalman filters this book will not be the last book you need.

Source: github.com