A Multiscale Approach for Enhancing Weak Signal Detection

TL;DR

This paper introduces a double-threshold stochastic resonance detection system that improves weak signal detection by leveraging multiscale analysis with wavelet transforms, outperforming traditional single-threshold methods in both original and frequency domains.

Contribution

It proposes a novel double-threshold detection approach combined with multiscale wavelet analysis to enhance weak signal detection beyond existing single-threshold techniques.

Findings

Significantly improves detection accuracy in original data domain.

Requires lower noise levels in frequency domain.

Outperforms existing detection systems in experiments.

Abstract

Stochastic resonance (SR), a phenomenon originally introduced in climate modeling, enhances signal detection by leveraging optimal noise levels within non-linear systems. Traditional SR techniques, mainly based on single-threshold detectors, are limited to signals whose behavior does not depend on time. Often large amounts of noise are needed to detect weak signals, which can distort complex signal characteristics. To address these limitations, this study explores multi-threshold systems and the application of SR in multiscale applications using wavelet transforms. In the multiscale domain signals can be analyzed at different levels of resolution to better understand the underlying dynamics. We propose a double-threshold detection system that integrates two single-threshold detectors to enhance weak signal detection. We evaluate it both in the original data domain and in the multiscale domain using simulated and real-world signals and compare its performance with existing methods. Experimental results demonstrate that, in the original data domain, the proposed double-threshold detector significantly improves weak signal detection compared to conventional single-threshold approaches. Its performance is further improved in the frequency domain, requiring lower noise levels while outperforming existing detection systems. This study advances SR-based detection methodologies by introducing a robust approach to weak signal identification, with potential applications in various disciplines.