<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The aim of this paper is to develop a new recursive identification algorithm for autoregressive (AR) models corrupted by additive white noise. The proposed approach relies on a set of both low-order and high-order Yule–Walker equations and on a modified version of the overdetermined recursive instrumental variable method, leading to the estimation of both the AR coefficients and the additive noise variance. The main motivation behind our proposition is introducing model identification procedures suitable for implementation on edge-computing platforms and programmable logic controllers (PLCs), which are known to have limited capabilities and resources when dealing with complex mathematical computations (i.e., matrix inversion). Indeed, our development is focused on condition monitoring systems, with particular attention paid to their integration onboard industrial machinery. The performance of the recursive approach is tested using both numerical simulations and a laboratory case study. The obtained results are very promising.</p>
</abstract>

The aim of this paper is to develop a new recursive identification algorithm for autoregressive (AR) models corrupted by additive white noise. The proposed approach relies on a set of both low-order and high-order Yule–Walker equations and on a modified version of the overdetermined recursive instrumental variable method, leading to the estimation of both the AR coefficients and the additive noise variance. The main motivation behind our proposition is introducing model identification procedures suitable for implementation on edge-computing platforms and programmable logic controllers (PLCs), which are known to have limited capabilities and resources when dealing with complex mathematical computations (i.e., matrix inversion). Indeed, our development is focused on condition monitoring systems, with particular attention paid to their integration onboard industrial machinery. The performance of the recursive approach is tested using both numerical simulations and a laboratory case study. The obtained results are very promising.

Recursive Identification of Noisy Autoregressive Models Via a Noise–Compensated Overdetermined Instrumental Variable Method

Department of Electrical,  Electronic and Information Engineering “Guglielmo Marconi” (DEI)

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The aim of this paper is to develop a new recursive identification algorithm for autoregressive (AR) models corrupted by additive white noise. The proposed...

Recursive Identification of Noisy Autoregressive Models Via a Noise–Compensated Overdetermined Instrumental Variable Method

Published Online: Mar 26, 2024

Page range: 65 - 79

Received: Mar 28, 2023

Accepted: Oct 02, 2023

DOI: https://doi.org/10.61822/amcs-2024-0005

Keywords
system identification, noisy autoregressive models, recursive estimation, Yule–Walker equations, condition monitoring systems

© 2024 Matteo Barbieri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Recursive Identification of Noisy Autoregressive Models Via a Noise–Compensated Overdetermined Instrumental Variable Method

Published Online: Mar 26, 2024

Page range: 65 - 79

Received: Mar 28, 2023

Accepted: Oct 02, 2023

DOI: https://doi.org/10.61822/amcs-2024-0005

Keywordssystem identification, noisy autoregressive models, recursive estimation, Yule–Walker equations, condition monitoring systems

© 2024 Matteo Barbieri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
system identification, noisy autoregressive models, recursive estimation, Yule–Walker equations, condition monitoring systems