Special Signal Models and Extensions

  • Peter Strobach
Part of the Springer Series in Information Sciences book series (SSINF, volume 21)


Until now, we have only considered the problem of predicting a process from its own subspace of past observations. The algorithms obtained for this simple case can, however, be extended to more involved problems. Assume that one needs to predict a process from the subspace of a related (correlated) process. This case is commonly referred to as the “joint-process” case of linear prediction. A second case of interest is system identification, where we assume not the simple AR process model, but possibly an MA (all-zero) process model, or even a more general ARMA (pole-zero) process model. This leads directly to the most general one-dimensional problem, namely, the identification of a multichannel (vector-autoregressive) process. In fact, it turns out that we can handle the MA (FIR) system identification problem with the joint-process approach, whereas the ARMA system identification problem can be embedded in a two-channel vector autoregressive process model.


Kalman Filter Power Spectral Density Joint Process Unknown System General ARMA 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Chapter 10

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Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Peter Strobach
    • 1
  1. 1.ZFE IS — Forschung für Informatik und SoftwareSIEMENS AG, Zentralabteilung Forschung und EntwicklungMünchen 83Fed. Rep. of Germany

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