Hi everybody,

I am not very acquainted with the ideas of the Kalman filter, but I have been searching about it in the Internet. As far as I understand,
the Kalman filter can be used to predict future values (outputs) of a
system, by considering that the prediction error is a noise. However,
to apply a Kalman filter we need (apart from an expected model of
behaviour of the system) to know some statistics about the noise (mean
and variance). So... in this, case, as the noise is the prediction
error and the prediction error is what we want to minimize with the
Kalman filter, it seems that we can't know those statistics a priori
(they depend on how well the Kalman filter performs!).

To sum up, can the Kalman filter be used for predictions? (for example, to predict future values of the temperature of a room based on past values, or to predict future locations of a vehicle). If so, what is wrong in my previous reasoning?

I hope you can help me to clarify this doubt!

Thanks in advance,

Sergio

I am not very acquainted with the ideas of the Kalman filter, but I have been searching about it in the Internet. As far as I understand,

To sum up, can the Kalman filter be used for predictions? (for example, to predict future values of the temperature of a room based on past values, or to predict future locations of a vehicle). If so, what is wrong in my previous reasoning?

I hope you can help me to clarify this doubt!

Thanks in advance,

Sergio