Magnetic Anomaly Signal Detection Using Parallel Monostable Stochastic Resonance System

Liu, Wang; Liu, Zhongyan; Zhang, Qi; Xu, Yujing; Liu, Shuchang; Chen, Zhuo; Zhu, Canlin; Wang, Ze; Pan, Mengchun; Hu, Jiafei; Li, Peisen

doi:10.1109/access.2020.3020881

Cited by 13 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…W. Liu et al proposed a parallel monostable stochastic resonance system and searched for the optimal system parameters in the SR system to achieve good detection performance under different waveforms. The system improves the monostable stochastic resonance system, whose output is influenced by the peak signal and trough signal [20]. Z.Y.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System

Huang,

Zheng,

Zhou

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System

Huang,

Zheng,

Zhou

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, the weak signal detection ability of a single SR system is still limited [35], especially for weak signals with extremely low SNRs. Hence, some enhancement models based on the SR systems, such as the cascaded SR systems [36][37][38], coupled SR systems [29,39], and parallel SR systems [40,41], have been constructed to further enhance the weak signal detection ability of SR-based methods. Among these enhancement models, the cascaded SR models, which consist of several single SR systems connected in series, are of most interest.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Adaptive Weak Fault Diagnosis Based on the Global Parameter Optimization Model of a Cascaded Stochastic Resonance System

Lai

Huang

Min

et al. 2023

Sensors

View full text Add to dashboard Cite

Stochastic resonance (SR), as a type of noise-assisted signal processing method, has been widely applied in weak signal detection and mechanical weak fault diagnosis. In order to further improve the weak signal detection performance of SR-based approaches and realize high-performance weak fault diagnosis, a global parameter optimization (GPO) model of a cascaded SR system is proposed in this work. The cascaded SR systems, which involve multiple multi-parameter-adjusting SR systems with both bistable and tri-stable potential functions, are first introduced. The fixed-parameter optimization (FPO) model and the GPO models of the cascaded systems to achieve optimal SR outputs are proposed based on the particle swarm optimization (PSO) algorithm. Simulated results show that the GPO model is capable of achieving a better SR output compared to the FPO model with rather good robustness and stability in detecting low signal-to-noise ratio (SNR) weak signals, and the tri-stable cascaded SR system has a better weak signal detection performance compared to the bistable cascaded SR system. Furthermore, the weak fault diagnosis approach based on the GPO model of the tri-stable cascaded system is proposed, and two rolling bearing weak fault diagnosis experiments are performed, thus verifying the effectiveness of the proposed approach in high-performance adaptive weak fault diagnosis.

show abstract

“…H. T. Reda et al The author in [13] discuss the application of SR in spectrum sensing and propose a firefly-inspired algorithm to optimize the SR and noise parameters of the dynamic system to improve signal detection. Guo et al [14], [15] detect the multi-frequency weak signal by the cascading and paralleling of SR system. Based on the adiabatic approximation theory [16], SR theory is applicable to the signal detection of low frequency ( 1Hz).…”

Section: Introductionmentioning

confidence: 99%

A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm

Yu¹,

Li²

2022

IJACSA

View full text Add to dashboard Cite

In this paper, a new index is proposed for detecting the frequency of unknown underwater signals based on the stochastic resonance theory. When the received weak signal is input into the stochastic resonance system, first, by frequency analysis, the frequency with the highest amplitude Am of the output signal spectrum is considered as the pre-detection frequency. Then a cosine signal with the pre-detection frequency and unit amplitude is constructed. Define the pre-signal-to-noiseratio as the logarithm of the squared amplitude Am over the mean of signal amplitudes in all other frequencies. The new index is defined as the product of the pre-signal-to-noise-ratio and the correlation coefficient between the received unknown signal and the constructed cosine signal. The new index is featured by taking into account the signal characteristics in both time and frequency domain, and it will yield better signal frequency detection performance. In addition, to improve the time efficiency of the frequency detection, a method to bound the searching range, keyed to the genetic algorithm, of the stochastic resonance system parameters is proposed. The method can be used to detect the frequency of both single frequency and frequency-hopping unknown signals. With the designed new index and system parameter bounding method, the simulations and experiments for the weak underwater unknown signals are conducted. Compared to the piecewise mean value index and weighted power spectral kurtosis index, the new index yields a higher detection probability at varied input signal-to-noise ratios and signal frequencies. With bounding system parameter searching ranges, the time efficiency is improved. The main purpose of this paper is to detect the frequency of unknown underwater weak signals by stochastic resonance system with genetic algorithm. The main contributions are summarized as follows. First, the detection probability of weak signals is improved by stochastic resonance system with the proposed signal detection index than some other indexes. Second, to improve the time efficiency of the signal frequency detection, a method to bound the searching range of system parameters is proposed.

show abstract

Magnetic Anomaly Signal Detection Using Parallel Monostable Stochastic Resonance System

Cited by 13 publications

References 29 publications

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System

High-Performance Adaptive Weak Fault Diagnosis Based on the Global Parameter Optimization Model of a Cascaded Stochastic Resonance System

A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm

Contact Info

Product

Resources

About