Applications of singular value decomposition to system modeling in signal processing

We consider the evaluation of the order of a linear system transfer function represented as an AR or an ARMA model based on the use of the singular value decomposition technique for the efficient determination of the rank of a matrix. Inputs to the system are modeled as binary-valued random data and outputs of the system are observed in the presence of uncorrelated noises. Results are obtained for the case when the relevant statistics given in autocorrelation and crosscorrelation values are assumed to be available as well as the case when the required statistics are computed explicitly from the sequence sample values. Various numerical examples are considered and shown to be more efficient than the Woodside determinant ratio approach.