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
Choosing to Prefilter Your Data
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 noise model of the system.
To get a reliable noise model in the app, instead of prefiltering the data, set
Filter, and specify the filter.
To get a reliable noise model at the command line, instead of prefiltering the data, specify
the filter in the
WeightingFilter estimation option of the estimation
command. If the
Focus option is available, specify it as
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.