Overview of Identification Methods of Autoregressive Model in Presence of Additive Noise

Abstract

This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large number of the described methods and gives recommendations on choosing the best methods.

Keywords

• autoregressive model
• additive noise
• Yule-Walker equations
• bias-compensated least squares
• Frisch scheme
• total least squares