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A stochastic resonance etection algorithm based on orthonormalized basis function for magnetic anomaly detection
Fan Dai1, Dongliang Peng1, Zhikun Chen1
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
The Review of Scientific Instruments
|December 18, 2023
Summary
A new airborne magnetic anomaly detection (MAD) algorithm, orthonormalized basis function-stochastic resonance (OBF-SR), improves detection performance in noisy conditions. This method enhances detection probability and range compared to existing techniques.
Area of Science:
- Geophysics
- Signal Processing
Background:
- Airborne magnetic anomaly detection (MAD) performance is significantly degraded by low signal-to-noise ratio (SNR) conditions.
- Existing detection algorithms struggle to effectively identify magnetic anomalies in noisy environments.
Purpose of the Study:
- To propose a novel stochastic resonance (SR) detection algorithm, termed OBF-SR, specifically designed for MAD under low SNR.
- To enhance the detection probability and range of airborne magnetic anomaly detection systems.
Main Methods:
- Preprocessing of noisy signals using the orthonormalized basis function (OBF) method.
- Utilizing the sum of three OBF space components as the input for the SR system.
- Designing a parallel SR system with varied initial states for signal detection.
Main Results:
- The OBF-SR method demonstrated superior performance compared to standard SR and OBF methods.
- Significant improvements in detection probability were observed.
- An extended detection range was achieved under identical low SNR conditions.
Conclusions:
- The OBF-SR algorithm offers a substantial advancement for airborne magnetic anomaly detection in challenging, low SNR environments.
- This approach effectively overcomes the limitations of traditional SR and OBF methods for MAD.
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