On the use of stochastic resonance in sine detection
Signal Processing
Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection
IEEE Transactions on Signal Processing
Stochastic resonance and improvement by noise in optimal detection strategies
Digital Signal Processing
Noise enhanced hypothesis-testing in the restricted Bayesian framework
IEEE Transactions on Signal Processing
Noise Enhanced -ary Composite Hypothesis-Testing in the Presence of Partial Prior Information
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
Noise Benefits in Quantizer-Array Correlation Detection and Watermark Decoding
IEEE Transactions on Signal Processing
Wavelet analysis and synthesis of fractional Brownian motion
IEEE Transactions on Information Theory - Part 2
Weak signal detection: Condition for noise induced enhancement
Digital Signal Processing
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Noise enhanced signal detection via stochastic resonance (SR) is generally realized by white noise tuning with an optimal noise intensity. This paper explores a new mechanism of SR that is induced by the noise at multiple scales for enhanced detection of weak signals under heavy background noise. A strategy is proposed to realize the SR via multiscale noise tuning according to the property of 1/f noise. The presented new method combines the benefits of colored noise and parameter tuning to the SR phenomenon. Under the strategy, effects of noise intensity, analysis scale, and driving frequency on the SR are analyzed through numerical simulations. Three merits are displayed for the proposed multiscale noise-induced SR model: insensitivity to noise intensity, activity of multiple scale noise, and capability of detecting high frequency. A practical application to structural defect identification has confirmed the effectiveness of the proposed method in comparison with traditional methods.