The Kalman filter is one of the most important algorithms in estimation and control — underpinning everything from GPS receivers to autonomous vehicles to spacecraft navigation. This course builds the theory from first principles and takes it through to working Python implementations you can apply directly to real problems.

The focus throughout is on genuine understanding: not just how to run the algorithm, but why it works, what the parameters mean, and how to tune and debug it in practice.

What You Will Learn

• How to probabilistically express uncertainty using probability distributions and covariance
• How to simulate and describe state space dynamic systems
• How to convert a system of differential equations into state space form
• How to apply the Linear Kalman Filter to solve optimal estimation problems
• How to tune the Kalman Filter for best performance
• How to use Least Squares Estimation to solve estimation problems
• How to derive the system matrices for any estimation problem
• How to implement the complete filter in Python

Who This Course Is For

• Engineers and scientists working on navigation, tracking, or data fusion problems
• University students studying estimation, control, or signal processing
• Software developers implementing sensor fusion systems
• Engineers who know the theory but want to close the gap to practical implementation

Prerequisites

Basic linear algebra and an understanding of probability and random variables at an introductory level. No prior knowledge of Kalman filtering is required. Python experience is helpful for the implementation sections but not essential.

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