Vibrational resonance in coupled self-learning Duffing oscillators and its application in noisy radio frequency signal processing

TL;DR

This paper introduces a novel array of self-learning Duffing oscillators that adapt their natural frequencies to extend vibrational resonance, enabling effective noise reduction in RF signals surpassing traditional methods.

Contribution

The work develops a frequency-adaptive coupled oscillator system with a derived resonance condition, demonstrating enhanced noise filtering in RF signal processing applications.

Findings

Extended vibrational resonance range through learning rules

Effective denoising of RF signals in noisy environments

Superiority over wavelet and Kalman filtering methods

Abstract

This work presents a new coupled array of frequency-adaptive Duffing oscillators. Based on learning rules, the natural frequency of each oscillator changes with the external excitation to achieve the frequency-adaptive capability in the response. The frequency range of vibrational resonance in the response is greatly extended through the frequency-adaptive learning rule. Moreover, the theoretical condition for vibrational resonance is derived and its validity is verified numerically. The coupled self-learning Duffing oscillators can also perform signal denoising in strong noise environment, and its performance in signal denoising has been verified through processing the simulated signal and the wireless radio frequency signal under two scenarios. The superiority of vibrational resonance to the conventional denosing methods such as wavelet transform and Kalman filter has also been illustrated by experimental radio frequency signal processing. The combination of broadband frequency adaptability and strong noise-reduction capability suggests that these oscillators hold considerable potential for engineering applications.