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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/230103
- Blind identification of multi-channel ARMA models based on second-order statistics
- Yu, Chengpu; Zhang, Cishen; Xie, Lihua
- 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.
- Publication type
- Journal article
- Research centre
- Swinburne University of Technology. Faculty of Engineering and Industrial Sciences
- IEEE Transactions on Signal Processing, Vol. 60, no. 8 (Aug 2012), pp. 4415-4420
- Publication year
- ARMA model; Autocorrelation matrices; Autoregressive and moving average system models; Blind channel identification; Second-order statistics
- Publisher URL
- Copyright © 2012 IEEE.
- Peer reviewed