Kalman filters are powerful recursive estimation algorithms widely used in control systems, signal processing, and navigation. They provide an efficient means to estimate the internal state of a dynamic system in the presence of noise and uncertainty, making them indispensable in applications such as target tracking, sensor fusion, robotics, and communications.

This presentation introduces the foundations and practical implementation of Kalman filters in an accessible manner. We begin with the motivation for state estimation, reviewing the limitations of direct measurement in noisy environments. The mathematical framework of the Kalman filter is then presented, highlighting the state-space model, prediction and update steps, and the role of covariance in quantifying uncertainty. Emphasis is placed on the recursive nature of the algorithm, which enables real-time operation with minimal computational complexity.

Practical examples illustrate how the filter balances model predictions with noisy observations to achieve optimal estimates. By the end of the presentation, attendees will understand both the theoretical foundations and practical benefits of Kalman filtering, equipping them to apply the method to a wide range of engineering and signal processing problems. This presentation will include example code and walk throughs.