Power series for noise attenuation in linear regression parameter estimation

Wang, J.

doi:10.1080/23307706.2025.2587081

Power series for noise attenuation in linear regression parameter estimation

2025-11-01

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DOI：10.1080/23307706.2025.2587081

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Abstract

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The constant parameter identification problem is considered for a linear regression model assuming that the noise is sufficiently small comparing to the regressor. With the aim to attenuate the influence of the disturbance, two nonlinear transformations (filters) are proposed, and the estimation is performed for an extended regression dependent on the powers of the unknown parameters and the diminished disturbance. It is shown that such a transformation preserves the excitation of regressor under reasonable assumptions. The quality improvement is demonstrated in numerical experiments.

Keywords:

Parameter estimation

excitation

drem

linear regression

Power series for noise attenuation in linear regression parameter estimation

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