Adaptive Signal Separation with Maximum Likelihood
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초록
Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approachesinfinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradientoptimization criterion. A parametric density model is introduced through a parameterized generalizeddistribution family in ML framework. After specifying a limited number of parameters, the density of specificoriginal signal can be approximated automatically by the constructed density function. Consequently, signalseparation can be conducted without any prior information about the probability density of the desired originalsignal. Simulations on classical biomedical signals confirm the performance of the deduced technique.
Abstract
Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approachesinfinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradientoptimization criterion. A parametric density model is introduced through a parameterized generalizeddistribution family in ML framework. After specifying a limited number of parameters, the density of specificoriginal signal can be approximated automatically by the constructed density function. Consequently, signalseparation can be conducted without any prior information about the probability density of the desired originalsignal. Simulations on classical biomedical signals confirm the performance of the deduced technique.
참고문헌 (17)
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