Redundant sensor system for stochastic resonance tuning without input signal knowledge

Stochastic resonance (SR) is a phenomenon by which the immeasurable input signals of a non-linear system can be observed in the output signals by adding a non-zero level of noise. So far, this phenomenon has been intensively studied, but no methodology for its use in engineering applications has been established yet. To exploit SR in engineering, and, in particular, to determine the appropriate noise variance that optimizes the SR performance, remains an open problem. In this study, we propose a method, which exploits the non- linear correlation between outputs from a set of redundant sensors subject to different noise sources, to tune the noise variance. Because the proposed method does not require the input signal information, it allows the exploitation of SR in realistic engineering problems. The proposed method is validated by information theory and numerical simulations. Based on the results, we developed a tactile sensing system that utilizes the method. Experimental results demonstrate that the tactile sensing system can sense immeasurable signals which are smaller than the quantization error of the sensing system.