Hello,
I'm currently working with a system consisting of an accelerometer, that samples in bursts of 10 seconds with a sample frequency of 3.9 Hz, before going into deep sleep for an extended (and yet undetermined) time period, then waking up again and sampling for 10 seconds and so on.
I've recently taken over this project from someone else and I can see that this person has implemented a Kalman filter to smooth the noise from the accelerometer. I don't know much about Kalman filters, but to me it seems that the long deep sleep period would make the previous states too outdated to be useful in predicting the new ones.
So my question is: can the previous states become outdated?
Dear Kristina Larson,
I suggest you to see links and attached files on topic.
https://www.mathworks.com/help/ident/ug/multirate-nonlinear-state-estimation-in-simulink.html
https://www.hindawi.com/journals/complexity/2018/1450353/
https://www.mdpi.com/1424-8220/19/1/186/pdf
Article A multi-rate sensor fusion approach using information filter...
Best regards
The application of a Kalman Filter requires four models:
1. Measurement model, expressed by the Jacobian of the measurement variables in respect to the state variables. (H)
2. The measurement error model (R)
3. The state model, which allows to extrapolate the state to the future (F)
4. The state error model which allows to model the unpredictability of the state extrapolation. (The degree of a deviation of the state from the prediction for whatever reason) (Q)
For the exact mathematical meaning of H,R,F,Q refer to Wikipedia: Kalman Filter.
- Badges
- Science topic
- Similar topics
- Biological Science
- Bioecology
- Systems Ecology
- Landscape Ecology
- Connectivity