Calculating Q Matrix for Extended Kalman Filter Identification

[Baumwolle]

Dear people,
I want to implement the Extended Kalman Filter for identification. I have a model of the system and I want to estimate the states. My question is, how can give the Q Matrix, I mean, how can I calculate the elements of the covariance matrix related to the dispersion of the system?
I have read that the noise of the measurement (Matrix R) depends on the resolution of the sensor used for the measurements. Is there a similar way to calculate the noise due to the system? I have read the noise of the system is withe noise, and represent the dynamics fo the states, so how can they be calculated?
Thank you for any comment.

 

[Valkarie]

When it comes to calculating the elements of the covariance matrix (Q Matrix) for the Extended Kalman Filter, there are a few different approaches that you can use. One approach is to use the system model and calculate the variance of the states from the model. This can be done by taking the variance of each state in the model and then using it to estimate the Q Matrix. Another approach is to use a knowledge-based approach, which would involve analyzing past data or using expert knowledge to determine the values for the Q matrix. Finally, you may also want to consider using a learning-based approach, where you can use machine learning techniques such as neural networks to learn the values of the Q matrix. Whichever approach you choose, it is important to remember that the Q matrix should accurately reflect the dynamics of the system in order to produce accurate results.