Estimate Impulse-Response Models at the Command Line

Before you can perform this task, you must have:

• Input/output or frequency-response data. See Representing Time- and Frequency-Domain Data Using iddata Objects. For supported data formats, see Supported Estimation Data.

• Performed any required data preprocessing operations. If you use time-domain data, you can detrend it before estimation. See Ways to Prepare Data for System Identification.

Use impulseest to compute impulse response models. impulseest estimates a high-order, noncausal FIR model using correlation analysis. The resulting models are stored as idtf model objects and contain impulse-response coefficients in the model numerator.

To estimate the model m and plot the impulse or step response, use the following syntax:

m=impulseest(data,N);
impulse(m,Time);
step(m,Time);

where data is a single- or multiple-output iddata or idfrd object. N is a scalar value specifying the order of the FIR system corresponding to the time range 0:Ts:(N-1)*Ts, where Ts is the data sample time.

You can also specify estimation options, such as regularizing kernel, pre-whitening filter order and data offsets, using impulseestOptions and pass them as an input to impulseest. For example:

opt = impulseestOptions('RegularizationKernel','TC'));
m = impulseest(data,N,opt);

To view the confidence region for the estimated response, use impulseplot and stepplot to create the plot. Then use showConfidence.

For example:

h = stepplot(m,Time);
showConfidence(h,3) % 3 std confidence region

Note

cra is an alternative method for computing impulse response from time-domain data only.

Next Steps

• Perform model analysis. See Validating Models After Estimation.

Related Examples

• Identify Delay Using Transient-Response Plots
• Compute Response Values
• Estimate Impulse-Response Models Using System Identification App

More About

• What Is Time-Domain Correlation Analysis?
• Supported Estimation Data
• Correlation Analysis Algorithm