The author presents an online tutorial and book covering the Kalman Filter algorithm, which is essential for estimating and predicting system states amidst uncertainty. The material is designed to be accessible and understandable, moving from basic to advanced topics like non-linear Kalman Filters and sensor fusion. The focus is on intuitive explanations over rigorous math, with illustrative examples aiding comprehension. The tutorial and e-book provide practical guidelines for implementation, helping readers design, simulate, and evaluate Kalman Filters. The need for such algorithms is illustrated using the example of radar tracking, emphasizing the importance of accurate estimation in the face of measurement and process uncertainties.
https://www.kalmanfilter.net/default.aspx