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Previous study shows that, in the temporal mode, the optimal estimation implies equally spaced measurement updates.
Then, sequential EnKF measurement updates are performed for all y k.
The implementation is based on a partially decentralized system architecture and statistical marginalization, and sampling-based measurement updates.
Namely, measurement updates are carried out "grid point by grid point" [13, 16, 42], that is, an iteration is carried out over state rather than measurement components.
Localization techniques such as local measurement updates [13, 16, 42] or covariance tapering [14, 43] let the measurement only affect a part of the state vector.
Thereafter, in Section 4, the sampling-based measurement updates with required state space transformation and marginalization are presented and shown to give a robust and low computational cost sensor fusion.
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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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com