Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models

Siyu Liu,Ling Xu,Feng Ding

doi:10.3390/a10040118

Siyu Liu, Ling Xu + Show 1 more

Open Access

https://doi.org/10.3390/a10040118

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Abstract

This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI) algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models.

Highlights

Parameter estimation is used widely in system identification [1,2,3] and signal processing [4,5].The existing parameter estimation methods for signal models can be classified into the following basic categories: the frequency-domain methods and the time-domain methods
The frequency-domain methods based on the fast Fourier transform (FFT) mainly include the Rife method, the phase difference method, etc
The accuracy of the Rife method is high in the case of noiseless or higher signal-to-noise ratio with adaptive sampling points; the error given by the Rife method is large if the signal frequency is near the DFT quantization frequency point

Summary

Introduction

Parameter estimation is used widely in system identification [1,2,3] and signal processing [4,5]. The iterative methods and/or the recursive methods play an important role in finding the solutions of nonlinear matrix equations, and in deriving parameter estimation algorithms for signal models [16,17,18,19,20,21,22]. The Newton method is useful for solving roots of nonlinear problems or deriving parameter estimation algorithms from observed data [28,29,30]. The basic idea is to present a gradient-based iterative (GI) algorithm and to estimate the parameters for signal models.

The Gradient-Based Iterative Parameter Estimation Algorithm

The Newton Iterative Parameter Estimation Algorithm

The Moving Data Window Gradient-Based Iterative Algorithm

Examples

Conclusions