Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering

Qinyao Liu, Feng Ding*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the data filtering technique and the auxiliary model identification idea, we derive a filtering-based auxiliary model recursive generalized least squares algorithm. The key is to filter the input–output data and to derive two identification models, one of which includes the system parameters and the other contains the noise parameters. Compared with the auxiliary model-based recursive generalized least squares algorithm, the proposed algorithm requires less computational burden and can generate more accurate parameter estimates. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

Original languageEnglish
Pages (from-to)590-610
Number of pages21
JournalCircuits, Systems, and Signal Processing
Issue number2
Publication statusPublished - 15 Feb 2019
Externally publishedYes


  • Auxiliary model
  • Filtering technique
  • Multivariate system
  • Parameter estimation
  • Recursive least squares

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