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A comment on Kalman Filter. Parent comment
1. Figure out the initial state
2. use process model to predict state at next time step
3. adjust belief of the true state by taking account prediction uncertainty
4. get a measurement and belief according to its uncertainty/accuracy
5. compute residual
6. compute scaling factor (what's more important, measurement or prediction)
7. set state with scaling factor
8. update belief in state given uncertainty of measurement
pretty much allows plugging in a black box anywere. from process model to belief updates.
1. Figure out the initial state
2. use process model to predict state at next time step
3. adjust belief of the true state by taking account prediction uncertainty
4. get a measurement and belief according to its uncertainty/accuracy
5. compute residual
6. compute scaling factor (what's more important, measurement or prediction)
7. set state with scaling factor
8. update belief in state given uncertainty of measurement
pretty much allows plugging in a black box anywere. from process model to belief updates.