Understanding or controlling a physical system often requires a model of the system, that is, knowledge of the characteristics and structure of the system. A model can be a predefined structure or can be determined solely through data. In the case of Kalman filtering, we create a model and use the model as a framework for learning about the system.
What is important about Kalman filters is that they rigorously account for uncertainty in a system that you want to know more about. There is uncertainty in the model of the system, if you have ...
