Particle filter with second order Markov model
danscr opened this issue · 3 comments
Is it possible to model the dynamics of a particle filter as
where
I assume it should be possible to have the state be a vector
Is there a way to access the previous state from within the dynamics function?
Having
There is no way to access state older than the last step, the definition of state is the complete set of information that is required to predict the future, hence,
One could implement a custom particle filter to handle this case if the use of memory becomes a bottleneck, but his would have to special case not only the dynamics update, but also handling of the initial state etc. since you'd need multiple past values of
A further complication would be in the resampling step, since it would not be enough to resample the current value of
BTW, this model is linear, is the noise Gaussian? If so, I'd use a Kalman filter instead
Thanks for the explanation! I'll then proceed as you suggested.
The noise in my current dynamics is not always Gaussian, since I have some parameters constrained to certain intervals. But later on I might simplify my model to be able to use Kalman filter.
Cool, I'll close this issue for now, feel free to continue the discussion if you have further questions :)