Abstract
In order to address the problem with low signal-to-noise ratio (SNR) detection for airborne magnetic anomaly detection (MAD), an adaptive detection method based on bi-stable stochastic resonance (SR) system is proposed. The method adopts a series structure, S-SR, effectively improving detection speed. Allowing for the difficulty in parameter selection for the SR system, this method can achieve the adaptive selection of system parameters. On this basis, the effect of different sampling frequencies in the Runge–Kutta method on the numerical model of the SR system is investigated. Finally, the simulation and experiment analysis of MAD methods are performed to draw a comparison between the S-SR, orthogonal basis functions (OBF) decomposition, and high-order crossing (HOC) methods. According to the simulation and experiment results, the S-SR method outperforms the OBF and HOC methods in the outcome and range of detection under the same conditions.
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