On-line model selection of nonstationary time series using Gerschgorin disks
The paper proposes a method for on-line model selection of nonstationary time series. The method is based on computation of the covariance matrix of the data, transformation of the matrix by Housholder's tridiagonalization, and application of a clustering algorithm that can separate the Gerschgorin disks of the transformed covariance matrix into disks that correspond to the signals and noise, respectively. The method is applied to on-line estimation of the the number of harmonic signals in noise. Simulation results are presented that show the performance of the proposed method.