Hierarchical Principle-Based Iterative Parameter Estimation Algorithm for Dual-Frequency Signals

被引：146

作者：

Liu, Siyu ^{[1
]}

Ding, Feng ^{[1
,2
,3
]}

Xu, Ling ^{[1
]}

Hayat, Tasawar ^{[3
]}

机构：

[1] Jiangnan Univ, Sch Internet Things Engn, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China

[2] Hubei Univ Technol, Sch Elect & Elect Engn, Wuhan 430068, Hubei, Peoples R China

[3] King Abdulaziz Univ, Dept Math, Fac Sci, Jeddah 21589, Saudi Arabia

来源：

CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2019年 / 38卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Iterative algorithm; Signal modeling; Hierarchical identification; Least squares; Newton search; Gradient search; OPTIMIZATION; DESIGN; STATE;

D O I：

10.1007/s00034-018-1015-1

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we consider the parameter estimation problem of dual-frequency signals disturbed by stochastic noise. The signal model is a highly nonlinear function with respect to the frequencies and phases, and the gradient method cannot obtain the accurate parameter estimates. Based on the Newton search, we derive an iterative algorithm for estimating all parameters, including the unknown amplitudes, frequencies, and phases. Furthermore, by using the parameter decomposition, a hierarchical least squares and gradient-based iterative algorithm is proposed for improving the computational efficiency. A gradient-based iterative algorithm is given for comparisons. The numerical examples are provided to demonstrate the validity of the proposed algorithms.

引用

页码：3251 / 3268

页数：18

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