Is a Kalman Filter still a Kalman filter if there are no inputs that explicitly predict the next state of the system?
For instance, if I have a signal where I know it has characteristics of a first order dynamic system and I have a filter configuration that can in effect discount or ignore changes it considers unlikely is this essentially a Kalman filter without explicit state estimation?
I have tried to follow the derivation for the Kalman filter and I understand that you use some prior knowledge or knowledge of how other system elements may effect your output and so you predict the next state.
- Is a sufficient prior to be able to say
this signal is from a 1st order dynamic system with a time constant of $\tau_x$?