Weak signal detection based on two dimensional stochastic resonance

Leonardo Barbini, Matthew O. T. Cole, Andrew J. Hillis, Jonathan L. Du Bois

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

12 Citations (SciVal)
208 Downloads (Pure)


The analysis of vibrations from rotating machines gives information about their faults. From the signal processing perspective a significant problem is the detection of weak signals embedded in strong noise. Stochastic resonance (SR) is a mechanism where noise is not suppressed but exploited to trigger the synchronization of a non-linear system and in its one-dimensional form has been recently applied to vibration analysis. This paper focuses on the use of SR in a two-dimensional system of gradient type for detection of weak signals submerged in Gaussian noise. Comparing the traditional one-dimensional system and the two-dimensional used here, this paper shows that the latter can offer a more sensitive means of detection. An alternative metric is proposed to assess the output signal quality, requiring no a priori knowledge of the signal to be detected, and it is shown to offer similar results to the more conventional signal-to-noise ratio.
Original languageEnglish
Title of host publicationProceedings of the 23rd European Signal Processing Conference, 2015
ISBN (Print)9780992862633
Publication statusPublished - 2015
EventSignal Processing Conference (EUSIPCO), 2015 - Nice, Italy
Duration: 31 Aug 20154 Sept 2015


ConferenceSignal Processing Conference (EUSIPCO), 2015


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