A new technique for arma-system identification based on qr-decomposition of third order cumulants matrix

被引：0
作者：
Al-Smadi A.M. [1 ]
机构：
[1] Department of Electronics Engineering, Yarmouk University, Irbid
来源：
Al-Smadi, Adnan M. | 1607年 / North Atlantic University Union NAUN卷 / 15期
关键词：
Gaussian processes; Higher order statistics; Signal-to-noise-ratio; Time series analysis;
D O I：
10.46300/9106.2021.15.173
中图分类号：
学科分类号：
摘要：
In this paper a new technique to estimate the coefficients of a general Autoregressive Moving Average (ARMA) (p, q) model is proposed. The ARMA system is excited by an un-observable independently identically distributed (i.i.d) non-Gaussian process. The proposed ARMA coefficients estimation method uses the QR-Decomposition (QRD) of a special matrix built with entries of third order cumulants (TOC) of the available output data only. The observed output may be corrupted with additive colored or white Gaussian noise of unknown power spectral density. The proposed technique was compared with several good methods such as the residual time series (RTS) and the Q-slice algorithm (QSA) methods. Simulations for several examples were tested. The results for these examples confirm the good performance of the proposed technique with respect to existing well-known methods. © 2021, North Atlantic University Union NAUN. All rights reserved.
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页码：1607 / 1612
页数：5
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  • [1] Al-Smadi A., Cumulant-based order selection of nonGaussian autoregressive moving average models: the corner method, Signal Processing, 85, 3, pp. 449-456, (2005)
  • [2] Giannakis G.B., Mendel J.M., Identification of nonminimum phase systems using higher order statistics, IEEE Trans. Acoust., Speech, Signal processing, 37, 3, pp. 360-377, (1989)
  • [3] Swami A., Mendel J., ARMA parameters estimation using only output cumulants, IEEE Trans. Signal Processing, 38, 7, pp. 1257-1265, (1990)
  • [4] Giannakis G.B., Mendel J.M., Cumulant-based order determination of non-Gaussian ARMA models, IEEE Trans. Acoust. Speech Signal Process, 38, 8, pp. 1411-1423, (1990)
  • [5] Pillai S. U., Shim T.I., Youla D.C., A new technique for ARMA-system identification and rational approximation, IEEE Trans. Signal Process, 41, 3, pp. 1281-1304, (1993)
  • [6] Al-Smadi A., Smadi M., Study of the reliability of a binary symmetric channel under non-Gaussian disturbances, International Journal of Communication Systems, 16, 10, pp. 865-973, (2003)
  • [7] Al-Smadi A., Wilkes D.M., Robust and accurate ARX and ARMA model order estimation of non-Gaussian processes, IEEE Trans. Signal Processing, 50, 3, pp. 759-763, (2002)
  • [8] Al-Smadi A., Automatic Identification of ARMA Processes, International Journal of General Systems, 38, 1, pp. 29-41, (2009)
  • [9] Amiri Ali, Fathy Mahmood, Video Shot Boundary Detection Using QR-Decomposition and Gaussian Transition Detection, EURASIP Journal on Advances in Signal Processing, (2009)
  • [10] Poomvichid Thitapa, Patirupanusara Pantida, Ketcham Mahasak, The QR Code for Audio Watermarking using Genetic Algorithm, International Conference on Machine Learning and Computer Science (IMLCS'2012), pp. 171-174, (2012)
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