## KFExItMu:

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One step of the extended iterated Kalman Filter measurement
update. Also can include a forgetting factor to keep the covariance
from going to zero. This can be used independently if the
state is constant. This can also be used to process measurements
one at a time by passing different hname and Hname with each call
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Form:
[x, P, K] = KFExItMu( hname, Hname, H, Hindex, R, z, x, P, f, nits, u )
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Inputs
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hname The name of the function that supplies the
constant term in the measurement equation
Hname The name of the function that supplies the
partials in the measurement equation
H Dummy H vector (for sizing only)
Hindex The location of Hname in H
R Measurement covariance matrix
z Measurement vector
x State
P Covariance matrix
f Forgetting factor (0 to 1)
nits Maximum number of iterations
u Auxiliary vector to pass to hname and Hname
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Outputs
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x State
P Covariance matrix
K Gain matrix
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References: Gelb, A., Applied Optimal Estimation, MIT Press, Cambridge,
1974, pp. 190-191.
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