A Newton-Raphson solution to the exponentially weighted least M-estimate formulation for acoustic system identification

The formulation of the exponentially weighted least M-estimate has shown significant promise in addressing acoustic system identification amidst non-Gaussian noise. A common approach to this formulation is the recursive least M-estimate algorithm. However, due to its derivation from nonlinear normal...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Applied acoustics Ročník 231; s. 110460
Hlavní autoři: Zhang, Limin, He, Hongsen, Chen, Jingdong, Yu, Yi, Benesty, Jacob
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2025
Témata:
Acoustic system identification
Adaptive filter
Newton-Raphson algorithm
Robustness
Acoustic system identification
Robustness
Adaptive filter
Newton-Raphson algorithm
ISSN:0003-682X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The formulation of the exponentially weighted least M-estimate has shown significant promise in addressing acoustic system identification amidst non-Gaussian noise. A common approach to this formulation is the recursive least M-estimate algorithm. However, due to its derivation from nonlinear normal equations, resulting in a slow approximate solution, this algorithm tends to exhibit poor convergence performance. In this paper, we present a Newton-Raphson solution, where the cost function is expanded using the second-order Taylor series to establish the adaptive algorithm. This approach bypasses the nonlinear normal equations, yielding a robust solution that more dynamically reflects changes in the cost function, ultimately leading to improved convergence and tracking performance. •A Newton-Raphson solution to the exponentially weighted least M-estimate formulation is proposed.•The cost function is expanded using the second-order Taylor series to establish the adaptive algorithm.•The proposed algorithm bypasses the nonlinear normal equations, leading to improved convergence and tracking performance.
ISSN:0003-682X
DOI:10.1016/j.apacoust.2024.110460