Stochastic resonance in sequential detectors
IEEE Transactions on Signal Processing
Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection
IEEE Transactions on Signal Processing
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We compare the performance of two detection schemes in charge of detecting the presence of a signal buried in an additive noise. One of these is the correlation receiver (linear detector), which is optimal when the noise is Gaussian. The other detector is obtained by applying the same correlation receiver to the output of a nonlinear preprocessor formed by a summing parallel array of two-state quantizers. We show that the performance of the collective detection realized by the array can benefit from an injection of independent noises purposely added on each individual quantizer. We show that this nonlinear detector can achieve better performance compared to the linear detector for various situations of non-Gaussian noise. This occurs for both Bayesian and Neyman-Pearson detection strategies with periodic and aperiodic signals.