Fine Tuning Kalman Filter Using Simulink Design Optimization

5 views (last 30 days)
I would like to tune my Kalman filter (Q and R matrices) using design optimization toolbox. I introduce the estimated signal and add a signal property to the optimizer. The signal property is Track Reference Signal with proper Time Vector and Amplitude. Although, the initial values of Q and R are relatively good, the second estimated signal ends up being zero. I wonder how I can prevent that, or in general tune my filter more optimaly.

Answers (1)

Kaashyap Pappu
Kaashyap Pappu on 8 Aug 2019
A similar query regarding the Q and R matrices’ optimization was addressed and elaborated upon here. It is important to model the noise characteristics prior to Kalman Filtering to obtain the Q and R matrices for better performance.


Find more on Adaptive Control in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!