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An intuitive idea is then to explore part of the state space,, using particles, and consider these instances of the state space as the modes in the filter.
set.
the importance weights, and normalize.
This "inbreeding" [13] increases with each measurement update.
Here, x ̂ k - and P k - are priori (before measurement update) and x ̂ k and P k are posteriori (after measurement update) state estimate and error covariance, respectively.
This classic approach can be found in Kalman filter's measurement update.
In the measurement update, the state is corrected by sensor information.
FurThismore, the value of dependshouldhe specific.
Figure 8 The measurement update step of the two implemented error-state Kalman filters.
The EnKF measurement update is referred to as analysis in the geoscientific literature.
In other words, localization effectively reduces the dimension of each measurement update.
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