Affiliation:
1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2. China and the Science and Technology on Electromechanical Dynamic Control Laboratory, Xi’an 710065, China
Abstract
In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device.
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
the Foundation of JWKJW Field
the Jiangsu Funding Program for Excellent Postdoctoral Talent
the 2021 Open Project Fund of Science and Technology on Electromechanical Dynamic Control Laboratory
the Postgraduate Research & Practice Innovation Program of Jiangsu Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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