Kalman filter

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Kalman filter

[′kal·mən ‚fil·tər]
(control systems)
A linear system in which the mean squared error between the desired output and the actual output is minimized when the input is a random signal generated by white noise.
References in periodicals archive ?
where K is the Kalman gain matrix, P is the error covariance matrix, and H is the observation matrix.
A closer look on the evolution of the Kalman gain unveils a rather unexpected behavior: unlike the innovation term, which displays no sharp transitions during stationary load regimes, the elements of the Kalman gain matrix exhibit a peculiar oscillation, with no apparent link to the load transients:
where P(n/n) is the error covariance matrix at time n; K(n) is the Kalman gain matrix, responsible for minimizing P(n/n); and [??](n/n) is the state estimate at time n, according to the previous observations of y(n).
Then, we obtain the Kalman gain in 12 via prior and posterior covariance calculated in 10 and 11.
Define the Kalman gain matrix as [mathematical expression not reproducible] Step 5.
In the ROMS implementation of 4DVAR (Moore et al., 2004, 2011), nonlinearity of the underlying physics is dealt with by incrementally minimizing a linearized cost function via the Kalman gain. 4DVAR ROMS remains computationally demanding, even for moderately sized problems.
L(k) is the Kalman gain and [??](t | k) and P(t | k) are the state estimation and covariance matrix of estimation error at time tick t when the estimation is performed using measurements taken at time tick k.
Based on the above statistics, [P.sub.e] is used to calculate the Kalman gain of discrete systems; this process has been shown by (34).
Let the processing times taken by error covariance matrix "[[PHI].sub.k]," Kalman gain "[g.sub.k]," received signal estimation rfc, estimation error "[e.sub.k]," and update filter coefficient matrix "[[??].sub.k]" be [T.sub.[PHI]], [T.sub.g], [T.sub.r], [T.sub.e], and [T.sub.[??]], respectively.
The Kalman gain K(k) is calculated as the blending factor that minimizes the a posteriori error covariance.
Here, the KF model computes the estimate state [x.sub.k|k] at time k which is evolved from the previous estimate state [x.sub.k-1|k-1]; the [F.sub.k] is the state transition model which is applied to the previous state; the [B.sub.k] is the control-input model which is applied to the control vector [u.sub.k]; the [P.sub.k|k] is the estimate of error covariance matrix; the [K.sub.k] is Kalman gain; [z.sub.k] is the observation value at the current time k.