The Nyquist-Shannon theorem provides an upper bound for the sampling period when designing a Kalman filter. Leaving apart the computational cost, are there any other reasons, e.g., noise-related issues, to set a lower bound for the sampling period? And, if so, is there an optimal value between these bounds?
Dear Edgar,
I suggest you to see link and attached files in topics.
Optimal and Robust Estimation: With an Introduction to ...
www.uta.edu/utari/acs/ee5322/lectures/CTKalmanFilterNew.pdf
Best regards
I’m assuming you are considering an open-loop configuration, so there is no penalty for applying a control command.
I think that faster sampling can only improve things.
Although, if there is correlated measurement noise, a lower sampling rate may make it look more Gaussian than it really is. But that is just ”turning a blind eye”.
It is quite simple, the more measurement samples you can get, the better. The limiting point is, that statistical independency of the individual measurements must be granted.
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