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prior estimate + data → posterior estimateread paper to Kalman filters from Livi
Can i first do a KF and later extend it to an EKF? (Livi was unsure)My Kalman does not have just one variable, but multiple. how do they interact? ChatGPT: Do them all independent, and maybe later linked?
State estimation
Low Pass filtering before does not make sense
EKF might be tool of choice
Store historical data in the Filter file locally in a ring buffer
K,H, phi can be variable to have more performance
Higher Gain =higher smoothing, also more delay
Kalman does the same as Low Pass Filter (just better) → substitute at some point
EKF intuition:
Big questions:
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