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

作者：Liu, Qinyao; Ding, Feng^*

来源：Circuits, Systems, and Signal Processing, 2019, 38(2): 590-610.

DOI：10.1007/s00034-018-0871-z

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

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.

• 出版日期2019-2
• 单位江南大学; 青岛科技大学