Paper
14 October 2021 Magnetic anomaly signal detection using adaptive stochastic resonance system
Zhongyan Liu, Yujing Xu, Wang Liu, Qi Zhang, Jiafei Hu, Mengchun Pan
Author Affiliations +
Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119301J (2021) https://doi.org/10.1117/12.2611100
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China
Abstract
The SR (Stochastic Resonance) system possesses the ability to take advantage of the background noise to enhance the weak signal. And it provides a new approach to detect the weak magnetic anomaly signal embedded with complex geomagnetic environment noise. However, the system output is directly influenced by system structural parameters, the inappropriate choice of parameters will lead to a sharp decline in the detection performance of the system. Aiming at this target, we proposed an adaptive stochastic resonance (ASR) system employing the kurtosis index as the criteria to automatically adjust the system structural parameters, which can perform well in the detection of magnetic anomaly signal with background noise. The simulation and experiment are conducted, and the results indicate that it is effective. In details, compared with the traditional SR detector, it has an incremental detection probability of 12% ~20% when the input SNR was between -3dB and -1dB.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhongyan Liu, Yujing Xu, Wang Liu, Qi Zhang, Jiafei Hu, and Mengchun Pan "Magnetic anomaly signal detection using adaptive stochastic resonance system", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119301J (14 October 2021); https://doi.org/10.1117/12.2611100
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KEYWORDS
Signal to noise ratio

Magnetism

Stochastic processes

Interference (communication)

Sensors

Signal detection

Complex systems

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