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andresfe24

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03 Apr 2013 Learning the Kalman-Bucy Filter in Simulink A Simulink model to learn the continuous-time Kalman-Bucy Filter Author: Yi Cao

I just noticed that if I make the measurement matrix:

C = [1 0; 0 1]

the covariances behave appropriately. It is like if somehow the error from the velocity (which was not being measured) was accumulating on the estimates of the position and the velocity.

I am not sure why this is happening since the original measurement matrix:

C = [1 0];

produces an observable system (I verified this with the "obsv" and "rank" functions in Matlab).

Thanks.

02 Apr 2013 Learning the Kalman-Bucy Filter in Simulink A Simulink model to learn the continuous-time Kalman-Bucy Filter Author: Yi Cao

Very useful. Thanks!

For the ship position/velocity example, even though it seems like the filter successfully tracks the state, I saw that the covariances (P) are actualy growing. I am not sure why this would happen. I have tried modifying the model and measurement errors (Q and R), but still the covariances keep growing. What confuses me is that the error covariance for the position estimate starts actually decreasing, and then after several simulation steps, it starts to increase to much higher values.

Covariances seem to successfully decrease for the 4-state example, indicating more confidence on the estimates.

I tested this buy placing a Scope in the Simulink model to monitor the covariances.

Any comments on this are greatly appreciated.

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