Robust adaptive parameter estimation of sinusoidal signals

Na, Jing; Yang, Juan; Wu, Xing; Guo, Yu

doi:10.1016/j.automatica.2015.01.019

Cited by 125 publications

(68 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From (12) and (14), we have Replacing ( ), ( ) in (26) and (27) with their estimateŝ ( ) and̂( ), replacing c in (26) with its estimateĉ −1 ( ), and replacing in (27) with its estimatê( ), we havê…”

Section: The Hierarchical Gradient Based Iterative Algorithmmentioning

confidence: 99%

“…The parameter estimateŝ( ) andĉ ( ) cannot be computed by (26) and (27), because the information vectors ( ) and ( ) contain unknown variables ( − ) and ( − ), and the parameter vectors and c in (26) and (27) are unknown. We solve this problem by replacing the unknown variables ( − ) and ( − ) with their corresponding estimateŝ, −1 ( − ) and̂− 1 ( − ) at iteration −1 and define the estimateŝ( ) and̂( ) at iteration aŝ…”

Section: The Hierarchical Gradient Based Iterative Algorithmmentioning

confidence: 99%

“…In this literature, Schranz et al proposed a feasible hierarchical identification process for identifying the viscoelastic model of respiratory mechanics [25]. Xu et al developed the parameter estimation for dynamical response signals [26,27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Ding

2017

Complexity

View full text Add to dashboard Cite

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

show abstract

Section: The Hierarchical Gradient Based Iterative Algorithmmentioning

confidence: 99%

Section: The Hierarchical Gradient Based Iterative Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Ding

2017

Complexity

View full text Add to dashboard Cite

show abstract

“…System identification has a significant effect on the filtering [1][2][3], state estimation [4][5][6], system control [7][8][9] and optimization [10]. For example, Scarpiniti et al proposed a nonlinear filtering approach based on spline nonlinear functions [11]; Zhuang et al presented an algorithm to estimate the parameters and states for linear systems with canonical state-space descriptions [12]; Khan et al discussed the theoretical implementation of robust attitude estimation for a rigid spacecraft system under measurement loss [13].…”

Section: Introductionmentioning

confidence: 99%

Model Equivalence-Based Identification Algorithm for Equation-Error Systems with Colored Noise

Meng

Ding

2015

Algorithms

View full text Add to dashboard Cite

For equation-error autoregressive (EEAR) systems, this paper proposes an identification algorithm by means of the model equivalence transformation. The basic idea is to eliminate the autoregressive term in the model using the model transformation, to estimate the parameters of the converted system and further to compute the parameter estimates of the original system using the comparative coefficient way and the model equivalence principle. For comparison, the recursive generalized least squares algorithm is given simply. The simulation results verify that the proposed algorithm is effective and can produce more accurate parameter estimates.

show abstract

“…Since the presentation of such a work, several designs of spectral observers with improved features have been proposed either to deal with noise [2], disturbances, lack of data [3] or to estimate other parameters such as frequency [4]. The main goals of a spectral observer are both the estimation of a given signal and the transformation of such a signal to the frequency domain by means of the recursive identification of the coefficients of a Fourier series [5].…”

Section: Introductionmentioning

confidence: 99%

A Simple Spectral Observer

Torres

Jiménez-Cabas

Gómez-Aguilar

et al. 2018

MCA

View full text Add to dashboard Cite

Abstract:The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier Transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 1 December 2014 to 8 January 2018 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

show abstract

Robust adaptive parameter estimation of sinusoidal signals

Cited by 125 publications

References 21 publications

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Model Equivalence-Based Identification Algorithm for Equation-Error Systems with Colored Noise

A Simple Spectral Observer

Contact Info

Product

Resources

About