Prefilters perform data compression of the inputs to Kalman filters. They can be linear continuous, linear discrete, or nonlinear. They are beneficial for several purposes:
They allow a discrete Kalman filter to be used on a continuous system without “throwing away” information. For example, the integrate-and-hold prefilter shown in Figure 9.13 integrates over a period , where is a sampling time chosen sufficiently small that the dynamic states cannot change significantly between estimates.
They attenuate some states to the point that they can be safely ignored (in a suboptimal filter).
They can reduce the required iteration rate in a discrete filter, thereby saving computer time [2].
They tend to reduce the range of the dynamic variables due to added noise, so that the Kalman filter estimate is less degraded by nonlinearities.
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, where 
is a sampling time chosen sufficiently small that the dynamic states cannot change significantly between estimates.