> x – model state F – linear state transition model: x(t+1) = F x(t) Q – covariance matrix for process noise, so actually x(t+1) = F x(t) + N(0,Q) H – linear observation model R – covariance matrix for observation noise u, B – external control vector u, and B for how u acts on the model state. Optional.

Better formatting for this part:

x – model state

F – linear state transition model: x(t+1) = F x(t)

Q – covariance matrix for process noise, so actually x(t+1) = F x(t) + N(0,Q)

H – linear observation model

R – covariance matrix for observation noise

u, B – external control vector u, and B for how u acts on the model state. Optional.