Magnetic anomaly detection method based on principal component analysis and cascaded stochastic resonance
Abstract
To address the challenge of detecting weak magnetic anomaly signals in low signal-to-noise ratio conditions, this paper proposes a novel method that integrates Principal Component Analysis (PCA) with Cascaded Bi-stable Stochastic Resonance (CB-SR), referred to as PCA-SR. The proposed approach leverages PCA to extract the principal components of the target signal while suppressing background noise. In addition, the CB-SR system enhances detection performance through the inter-well transition characteristics. Simulation and experimental results validate the effectiveness of PCA-SR. Compared to classical PCA and Stochastic Resonance (SR) methods, PCA-SR demonstrates superior detection performance and an extended detection range.
Fields of Study & Subjects
stochastic dynamics and bifurcation
Probabilistic and Robust Engineering Design
NMR spectroscopy and applications
Publication details
- Journal / source
- Review of Scientific Instruments
- Publisher
- AIP Publishing
- Volume
- 97
- Issue
- 1
- Publication date
- 1 January 2026
- Language
- EN