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http://dx.doi.org/10.3745/JIPS.04.0162

Adaptive Signal Separation with Maximum Likelihood  

Zhao, Yongjian (School of Mechanical, Electrical & Information Engineering, Shandong University)
Jiang, Bin (School of Mechanical, Electrical & Information Engineering, Shandong University)
Publication Information
Journal of Information Processing Systems / v.16, no.1, 2020 , pp. 145-154 More about this Journal
Abstract
Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.
Keywords
Density; Estimator; Framework; Kurtosis; Likelihood; Separation;
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Times Cited By KSCI : 2  (Citation Analysis)
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