Assume $X$ and $N$ are two sets of observations from two different normal distribution, where $X$ represents clean data and $N$ represents noise; and $A$ a projection matrix of a filter and the relation between $X$ and $Y$ defines as:$Y=A \times X + N$considering that $X_i$ are not sequential samples and are just different observations from a normal distribution (there is no time involved), are we allowed to use a Kalman filter to estimate $A$ and parameters of  $N$? 
Using kalman filter when samples don't have time index
machine learning;error estimation