2004, 2011), nonlinearity of the underlying physics is dealt with by incrementally minimizing a linearized cost function via the Kalman gain
2] noise matrix [MATHEMATICAL EXPRESSION NOT Kalman gain
K REPRODUCIBLE IN ASCII] Sampling period T 200 [micro]s
This factor is called Kalman gain
and it is recalculated during the process until it gets the optimal value.
Furthermore, we show that if we assume that the marginal cost of information is fixed, capacity will be elastic with respect to a change in policy; consequently, the Kalman gain
in this case will also adjust in response to the policy change.
k] is a 2 x 1 vector called Kalman gain
or blending factor that minimizes a posteriori error covariance.
a) Compute the Kalman gain
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
This estimate is updated using the observations weighted by the Kalman gain
P] are the Kalman gain
and covariance matrices, respectively, for the extended state.
In addition, the innovation form of Kalman gain
is employed for state estimation with no prior knowledge of noise properties.
Equation (18) shows that the inferred coefficients are updated using the product of the Kalman gain
and the latest forecast error.
In Table 1, subscript k indicates the discrete time step, superscript (-) indicates the predicted state, superscript (+) indicates the estimated states, F and H are the Jacobian matrices, K is the Kalman gain
, P is the covariance matrix, and Q and R are the state noise and the measurement noise covariance matrices, respectively.
k] is the optimal Kalman gain
that minimizes the posteriori error covariance.