Adaptive separation of unknown narrowband and broadband time series

Motivated by Thomson´s (1982, 1994) multiple taper spectral estimation technique, we derive a new, robust procedure for automatically separating time series data into its constituent narrowband and broadband time series components. The new procedure avoids the pitfalls of adaptive notch filters, PCI method, or other similar algorithms, of mistaking and filtering local spectral peaks of the broadband component as narrowband components by decomposing the data vector into local subbands by a bank of matrix filters. Then in a piecewise fashion, the narrowband components are estimated and then filtered from each subband using the principal component inverse (PCI) method. Finally, the filtered components are coherently recombined to obtain the narrowband and broadband time series estimates. Computer simulation results show that the new procedure works well and can have a performance close to the clairvoyant Wiener filter