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.
Kalman
Started by ●November 24, 2006