Kalman Filter Tutorial: Understanding State Estimation Algorithms

The Kalman Filter is an algorithm for estimating and predicting the state of a system in the presence of uncertainty, such as measurement noise or influences of unknown external factors.

The Kalman Filter is an essential tool in areas like object tracking, navigation, robotics, and control.

For instance, it can be applied to estimate the trajectory of a moving object when only noisy sensor measurements are available.

The algorithm works by combining predictions from a mathematical model with actual measurements to produce optimal estimates.

This tutorial aims to make the Kalman Filter approachable through practical examples and intuitive explanations.