A novel maximum-likelihood method for blind multichannel identification
Date of Issue2012
International Conference on Information Fusion (FUSION) (15th : 2012)
School of Electrical and Electronic Engineering
Deterministic blind identification algorithms of single-input and multi-output (SIMO) systems can effectively estimate channel functions and the common source signal at high signal-noise-ratio (SNR) and small available data sample scenarios. However, it is difficult for them to identify systems accurately when the noise level is high. To deal with the noise problem, this paper develops an exact Maximum-Likelihood (EML) model which is different from the two-stage Maximum-Likelihood (TSML) method or the semi-blind ML method in the literature. The EML model derived from the cross relation equation of two channels does not contain the source signal but channel functions and output observations, hence the identification performance is barely affected by the unknown source signal. In addition, an iterative optimization approach based on variable splitting technique and alternating direction method of multipliers (ADMM) is derived to minimize the negative log-likelihood function. Simulations are carried out to verify the effectiveness of the proposed method.
DRNTU::Engineering::Electrical and electronic engineering
© 2012 ISIF. This paper was published in 2012 15th International Conference onInformation Fusion (FUSION) and is made available as an electronic reprint (preprint) with permission of ISIF. The paper can be found at the following official DOI: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6289976 ]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.