Elements of information theory
Elements of information theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Stochastic resonance in locally optimal detectors
IEEE Transactions on Signal Processing
Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors
IEEE Transactions on Signal Processing - Part I
Detection of random signals in Gaussian mixture noise
IEEE Transactions on Information Theory - Part 2
IEEE Transactions on Information Theory
Locally optimum detection of signals in a generalized observation model: the known signal case
IEEE Transactions on Information Theory
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The paper provides a blind binary detection approach in an unknown non-Gaussian noise. In our scheme, we use maximum likelihood (ML) detection rule in conjunction with maximum entropy method (MEM) for probability density function (PDF) estimation of the unknown observation noise from the samples of the received data. We constrain MEM on estimated moment generating function (MGF). The estimated PDF based on MEM-MGF is quite close to the true PDF and has a direct applicability for blind implementation. The results indicate that the new nonlinear detector outperforms conventional matched filter, and approaches the performance of the optimal ML detector which assumes the complete knowledge for the noise PDF. Then, we analyze the scheme by probability of error (P"e) calculation and interpret the results.