An Adaptive Blind Deconvolution Signal Subspace Method
نویسندگان
چکیده
A blind deconvolution algorithm is introduced. The channel parameters are identi ed based on Maximum Likelihood (ML) criterion and the desired input is estimated using minimum variance estimation. Simulation results show the e cacy of our method. Its estimation error is usually less than other algorithms. Its rate of convergence is su ciently 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 e cient. The arithmetic computational order of the presented algorithm is not more than other methods.
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