Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99174
Title: Blind identification of multi-channel ARMA models based on second-order statistics
Authors: Yu, Chengpu
Zhang, Cishen
Xie, Lihua
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Yu, C., Zhang, C., & Xie, L. (2012). Blind Identification of Multi-Channel ARMA Models Based on Second-Order Statistics. IEEE Transactions on Signal Processing, 60(8), 4415-4420.
Series/Report no.: IEEE transactions on signal processing
Abstract: This correspondence presents a new second-order statistical approach to blind identification of single-input multiple-output (SIMO) autoregressive and moving average (ARMA) system models. The proposed approach exploits the dynamical autoregressive information of the model contained in the autocorrelation matrices of the system outputs but does not require the block Toeplitz structure of the channel convolution matrix used by classical subspace methods. For the multi-channel model with the same autoregressive (AR) polynomial, sufficient conditions and an efficient identification algorithm are given such that the multi-channel model can be uniquely identified up to a constant scaling factor. Furthermore, an extension of the result to blind identification of multi-channel models with different AR polynomials is presented. Simulation results are given to show the effectiveness of the proposed approach.
URI: https://hdl.handle.net/10356/99174
http://hdl.handle.net/10220/13503
ISSN: 1053-587X
DOI: 10.1109/TSP.2012.2196698
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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