(Pro version only)
The Stochastic Subspace Identification techniques all uses the same estimation engine for estimation of state space realizations (models). In general, the input to this engine is a weighted version of the so-called Common SSI Input matrix that consist of compressed time series data. The difference between the three Stochastic Subspace Identification techniques is how this matrix is weighted.
The Stochastic Subspace Identification editor initialized for state space estimation using the Principal Component algorithm can be launched either from the SSI Pane of the Task Bar by pressing the button called PC Estimation or from the Project, Modal Analysis, Stochastic Subspace Identification, Principal Component menu item. This algorithm works best with data having modes with comparable energy level. In such cases it will produce good results using reasonable small state space dimensions.
See the Technical Paper on the Stochastic Subspace Identification Techniques for a more comprehensive description about how the Stochastic Subspace Identification techniques works and the specific mathematical formulation of the Principal Component algorithm.