Errors-in-variables identification of noisy moving average models. Youcef, A., Diversi, R., & Grivel, E. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 968-972, Aug, 2015. Paper doi abstract bibtex In this paper, we propose to address the moving average (MA) parameters estimation issue based only on noisy observations and without any knowledge on the variance of the additive stationary white Gaussian measurement noise. For this purpose, the MA process is approximated by a high-order AR process and its parameters are estimated by using an errors-in-variables (EIV) approach, which also makes it possible to derive the variances of both the driving process and the additive white noise. The method is based on the Frisch scheme. One of the main difficulties in this case is to evaluate the minimal AR-process order that must be considered to have a "good" approximation of the MA process. To this end, we propose a way based on K-means method. Simulation results of the proposed method are presented and compared to existing MA-parameter estimation approaches.
@InProceedings{7362527,
author = {A. Youcef and R. Diversi and E. Grivel},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {Errors-in-variables identification of noisy moving average models},
year = {2015},
pages = {968-972},
abstract = {In this paper, we propose to address the moving average (MA) parameters estimation issue based only on noisy observations and without any knowledge on the variance of the additive stationary white Gaussian measurement noise. For this purpose, the MA process is approximated by a high-order AR process and its parameters are estimated by using an errors-in-variables (EIV) approach, which also makes it possible to derive the variances of both the driving process and the additive white noise. The method is based on the Frisch scheme. One of the main difficulties in this case is to evaluate the minimal AR-process order that must be considered to have a {"}good{"} approximation of the MA process. To this end, we propose a way based on K-means method. Simulation results of the proposed method are presented and compared to existing MA-parameter estimation approaches.},
keywords = {approximation theory;autoregressive moving average processes;AWGN;measurement errors;measurement uncertainty;parameter estimation;signal processing;errors-in-variables identification;EIV approach;noisy moving average models;MA parameter estimation;noisy observations;additive stationary white Gaussian measurement noise;autoregressive processes;high-order AR process;Frisch scheme;MA process;K-means method;Noise measurement;Approximation methods;Correlation;Signal processing;Europe;Estimation;Mathematical model;Moving average model;autoregressive model;errors-in-variables (EIV);K-means classification},
doi = {10.1109/EUSIPCO.2015.7362527},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103805.pdf},
}
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