Kalman Filter for Beginners - Estimation and Prediction Process & MATLAB Example

Learn about the Kalman Filter's estimation and prediction processes in this 51-minute video lecture. Explore the filter's construction using state transition matrices, state-to-measurement matrices, and noise matrices. Discover how to apply the Kalman Filter without extensive theoretical knowledge through analogies with low-pass filters. Gain practical experience with MATLAB examples, from simple to more complex applications. Delve into dynamic attitude estimation using time-varying gyroscope data, building upon previous static attitude estimation concepts. Access accompanying MATLAB code and lecture notes to enhance your understanding of this powerful recursive filtering technique used in aerospace engineering and beyond.

Recap
Estimation Step
Comparison with Low-Pass Filter
Error Covariance = Inaccuracy of Estimate
Prediction Step
How Prediction and Estimation Fit Together
The System Model
Covariance of the System Noise
MATLAB Simple Example
More Complicated Example