System realization using information matrix

A generalized version of the information matrix is introduced consisting of the autocorrelation and cross correlation matrices of the shifted input and output data. Based on the concept of data correlation, a new system realization algorithm is developed to identify a model directly from input and output data. The algorithm starts with computing the information matrix to derive a special correlation matrix that in turn produces the system observability matrix and the state-vector correlation. A system model can then be identified from the observability matrix in conjunction with other algebraic manipulations. The algorithm leads to several different methods for computing system matrices to represent the system model. An experimental example is given to illustrate the validity and usefulness of these methods with some comparison.