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You can filter the input and output signals through a linear filter before estimating a model in the System Identification app or at the command line. How you want to handle the noise in the system determines whether it is appropriate to prefilter the data.
The filter available in the System Identification app is a fifth-order (passband) Butterworth filter. If you need to specify a custom filter, use the idfilt command.
Prefiltering data can help remove high-frequency noise or low-frequency disturbances (drift). The latter application is an alternative to subtracting linear trends from the data, as described in Handling Offsets and Trends in Data.
In addition to minimizing noise, prefiltering lets you focus your model on specific frequency bands. The frequency range of interest often corresponds to a passband over the breakpoints on a Bode plot. For example, if you are modeling a plant for control-design applications, you might prefilter the data to specifically enhance frequencies around the desired closed-loop bandwidth.
Prefiltering the input and output data through the same filter does not change the input-output relationship for a linear system. However, prefiltering does change the noise characteristics and affects the estimated model of the system.
To get a reliable noise model, avoid prefiltering the data. Instead, set the Focus property of the estimation algorithm to Simulation.
Note: When you prefilter during model estimation, the filtered data is used to only model the input-to-output dynamics. However, the disturbance model is calculated from the unfiltered data.
To learn how to filter data during linear model estimation instead, you can set the Focus property of the estimation algorithm to Filter and specify the filter characteristics.
For more information about prefiltering data, see the chapter on preprocessing data in System Identification: Theory for the User, Second Edition, by Lennart Ljung, Prentice Hall PTR, 1999.
For practical examples of prefiltering data, see the section on posttreatment of data in Modeling of Dynamic Systems, by Lennart Ljung and Torkel Glad, Prentice Hall PTR, 1994.