问题
I have been through the concept of 1D Kalman filter, but, they mostly concentrate on the equations formed from Gaussian distributions where they used the equations in the picture Gaussian Distribution equations (they can be found in the following links: Pyata 1D Kalman Filter, 1D Kalman Filter, Sensor Fusion). I have several questions:
Question 1:
How can I form predict and update states with other distributions? (for example, Bradford distribution) I looked into Bradford distribution and found this website (Link: Bardford Distribution with equations) but I am unsure if I just get the means for the states and use the equations in the links above to get the mean and variance of predict and update state, or, do I need to formulate a new equation for predict and update state, or, do I use the scipy stats formula to get the statistics values for mean and variance?
Question 2:
How can I implement bayesian filer for distributions?
Edit 1:
I looked into Kalman filter and found that the best one to work with non-Gaussian distribution is the unscented Kalman filter but it works with a non-linear system. How can is it spossible to make a non-linear system to linear in order to implement a 1D data into unscented Kalman filter?
来源：https://stackoverflow.com/questions/61568133/how-to-implement-1d-kalman-filter-with-other-distribution