It is possible to do so by specifying a time vector instead of a constant sample time, as in:
data = iddata(y, u, 'SamplingInstants', t)
However, most routines in the toolbox require constant sampling interval. So you must really try to get data on a uniform time grid (by interpolation, fillin missing values etc). An alternative is to use your own routine to convert the data into frequecy domain (see, for example, https://www.youtube.com/watch?v=O2K0ptoYpuc CAUTION: I have not tried it, can't speak to its correctness or usefulness). The frequency domain data does not have to be uniformly sampled in the frequency grid.
Intersample behavior need not by zero-order-hold (constant between samples). The toolbox supports two more options:
- First-order-hold: linear trend between samples
- band-limited: The data varies smoothly over its time span, so that there is no frequency higher that 0.5/Ts hertz (where Ts is sampling interval) in the data.
Example:
data = iddata(y, u, Ts, 'InterSample','foh')
Inter-sample behavior can be specified using the InterSample property of iddata object. Inter-sample behavior is irrelevant if you are identifying discrete-time models. For continuous-time models it helps incorporate aliasing effects when estimating.
