Hi,

I have just started off with Kalman implementation. My aim is to
estimate state vectors from the obseravtions (Z).

I have doubt regarding estimation of process noise variance matrix,
Quoting the state update eqtn

Xhat = XPred + K_G ( Z - H * Xpred )

Where K_G is the Kalman Gain

This eq follows the input model :

X_n = Phi * X_n-1  +  W_n

1) Am I right stating, the co-variance matrix Q = E[ W_n  * W_n
Trnspose] should follow the last term (P) of the prediction eq  if my
guess of Q was right. (i.e)

P =E[  (  K_G ( Z - H * Xpred )   )  *    (K_G ( Z - H * Xpred
)Trnspose  )]


2) If tht is true, can I update my Q value as a function of  P.

3) Can I use the same tactic to update my measurement noise variance
matrix from the estimate of 

( Z - H * Xpred) value.

Thanx,
Hurry.