Decoupling of Filtering Equations
The limiting (or steady-state) Kalman filter provides a very efficient method for estimating the state vector in a time-invariant linear system in real-time. However, if the state vector has a very high dimension n, and only a few components are of interest, this filter gives an abundance of useless information, an elimination of which should improve the efficiency of the filtering process. A decoupling method is introduced in this chapter for this purpose. It allows us to decompose an n-dimensional limiting kalman filtering algorithm into n independent one-dimensional recursive so that we may drop the ones that are of little interest.
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