Abstract
A new bias-compensated least-squares method for identifying finite impulse response (FIR) models whose input and output are affected by additive white noise is proposed. By exploiting the statistical properties of the equation error of the noisy FIR system, an estimate of the input noise variance is obtained and the noise-induced bias is removed. The results obtained by means of Monte Carlo simulations show that the proposed algorithm outperforms other bias-compensated approaches and allows to obtain an estimation accuracy comparable to that of total least-squares without requiring the a priori knowledge of the input-output noise variance ratio.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
