Enhancing stochastic resonance by adaptive colored noise and particle swarm optimization: an application to steering control

In this paper, an intelligent signal processing approach is applied to enhance the detectability of weak signals - i.e., signals which are partially below a theoretical threshold of detection. Mechanical and physiological thresholds limit the capability of humans when manipulating machines via control devices, such as steering wheels. One approach to tackle the shortcomings of lost subthreshold information is stochastic resonance, which consists in adding noise to a signal, to raise its energy content over the threshold of detection. In particular, this paper shows that using adaptive colored can noise improve the detectability of steering control signals recorded from human participants. The approach converts a signal processing task to a machine learning problem; particle swarm optimization is employed to obtain the optimal color (or spectral exponent) of the injected noise, generated through fractional order filters. The results have shown that the proposed method improves the detectability of subthreshold steering control signals, which can be further applicable to many other domains, such as improving tactile sensation or acoustic perception through noise and energy harvesting from vehicle tires.