An algorithm for pole-zero system model order estimation

In system identification, estimates of the unknown system model orders are often required. An algorithm for estimating model orders is described that looks at input/output data covariance matrix eigenvectors. When model orders are overestimated, zeros appear in the noise subspace eigenvectors. The number of zeros present can be used to estimate model orders.