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Performance Optimization in Frequency Estimation of Noisy Signals: Ds-IpDTFT Estimator.
Wei, Miaomiao; Zhu, Yongsheng; Sun, Jun; Lu, Xiangyang; Mu, Xiaomin; Xu, Juncai.
Afiliación
  • Wei M; Department of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China.
  • Zhu Y; Department of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China.
  • Sun J; Department of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China.
  • Lu X; Department of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
  • Mu X; Department of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China.
  • Xu J; Department of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Sensors (Basel) ; 23(17)2023 Aug 28.
Article en En | MEDLINE | ID: mdl-37687916
ABSTRACT
This research presents a comprehensive study of the dichotomous search iterative parabolic discrete time Fourier transform (Ds-IpDTFT) estimator, a novel approach for fine frequency estimation in noisy exponential signals. The proposed estimator leverages a dichotomous search process before iterative interpolation estimation, which significantly reduces computational complexity while maintaining high estimation accuracy. An in-depth exploration of the relationship between the optimal parameter p and the unknown parameter δ forms the backbone of the methodology. Through extensive simulations and real-world experiments, the Ds-IpDTFT estimator exhibits superior performance relative to other established estimators, demonstrating robustness in noisy conditions and stability across varying frequencies. This efficient and accurate estimation method is a significant contribution to the field of signal processing and offers promising potential for practical applications.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China