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    block this user An-Ping Li

    Research Fellow

    Beijing 100085, P.R.China

    An Adaptive Blind Deconvolution Signal Subspace Method

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    A blind deconvolution algorithm is introduced. The channel parameters are identified based on Maximum Likelihood (ML) criterion and the desired input is estimated using minimum variance estimation. Simulation results show the efficacy of our method. Its estimation error is usually less than other algorithms. Its rate of convergence is sufficiently high and compete the others. Although, the algorithm is originally derived according to the assumption of white noise, but it works as well for colored noises. The method shows very good performances such as low errors and high convergence rates in FIR systems. The performance of the algorithm for IIR channels is also efficient. The arithmetic computational order of the presented algorithm is not more than other methods.

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    Description

    Title : An Adaptive Blind Deconvolution Signal Subspace Method
    Abstract : A blind deconvolution algorithm is introduced. The channel parameters are identified based on Maximum Likelihood (ML) criterion and the desired input is estimated using minimum variance estimation. Simulation results show the efficacy of our method. Its estimation error is usually less than other algorithms. Its rate of convergence is sufficiently high and compete the others. Although, the algorithm is originally derived according to the assumption of white noise, but it works as well for colored noises. The method shows very good performances such as low errors and high convergence rates in FIR systems. The performance of the algorithm for IIR channels is also efficient. The arithmetic computational order of the presented algorithm is not more than other methods.
    Subject : unspecified
    Area : Mathematics
    Language : English
    Affiliations
    Url : http://www.ece.queensu.ca/hpages/faculty/gazor/icspat2.pdf
    Doi : 10.1.1.17.8042

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    An-Ping's Peer Evaluation activity

    Emailed by 1
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