The Novel Method of Magnetic Anomaly Recognition Based on the Fourth Order Aperiodic Stochastic Resonance

Qin, Tao; Zhou, Lingyun; Chen, Shuai; Chen, Zhengxiang

doi:10.1109/jsen.2022.3192668

Cited by 16 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where Bt is the total magnetic field vector, Be is the background magnetic field vector, and Br is the magnetic anomaly field vector. In fact, , so the magnetic distortion field can be expressed as (2) where |Bt| is the total magnetic field scalar value, |Be| is the background magnetic field scalar value, and |Br| is the scalar value of the magnetic anomaly field vector. The equivalent magnetic moment is set to M = (0, 0, 0.3)A•m 2 , the magnetic sensor moves along the θ = 30°, 200°, and 300° directions, the speed is set to v = 5 m/s, the closest contact distance between the detection path and the magnetic target is l = 5 m, and the sampling frequency is 2 kHz.…”

Section: Magnetic Anomaly Signalmentioning

confidence: 99%

See 1 more Smart Citation

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: Magnetic Anomaly Signalmentioning

confidence: 99%

“…Magnetic anomaly detection (MAD) is a widely used passive magnetic target detection method. Its applications include surface ship target detection, underwater motion target monitoring, land target detection, and metal mine seismic activity identification [1][2][3][4][5]. MAD methods can be divided into two categories.…”

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

“…However, these noise-based approaches are somewhat less robust and mainly operate by examining the differences between measured data and noise, rather than capitalizing on the features of target signals. In addition, some scholars have introduced detection methods based on nonlinear stochastic resonance systems to enhance the signal using noise energy [26,27]. But the stochastic resonance method demands stringent system parameters and exhibits limited stability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Basis Function Method for the Detection of an Undersurface Magnetic Anomaly Target

Liu,

Yuan,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The orthogonal basis functions (OBFs) method is a prevailing choice for the detection of undersurface magnetic anomaly targets. However, it requires the detecting platform or target to move uniformly along a straight path. To circumvent the restrictions, a new adaptive basis functions (ABFs) approach is proposed in this article. It permits the detection platform to search for a possible target at different speeds along any course. The ABFs are constructed using the real-time data of the onboard triaxial fluxgate, GPS module, and attitude gyro. Based on the pseudo-energy of an apparent target signal, the constant false alarm rate (CFAR) method is employed to judge whether a target is present. Moreover, by defining the pixel as a relative possibility for a target at a geographic location, a magnetic anomaly target imaging scheme is introduced by displaying the pixels onto the searching area. On-site experimental data are utilized to demonstrate the proposed approach. Compared with the traditional OBFs method, the present ABFs approach can substantially improve the detection possibility and reduce false alarms.

show abstract

“…The linear response theory is then developed by Gammaitoni et al [13], and the theory of residence time distribution is later introduced by Zhou and Moss [14]. By then, the three major theories which lay the classical theoretical framework of SR systems are formally proposed, and they are successfully applied in the fields of mechanical fault diagnosis [15][16][17][18], electromagnetism and communication [19][20][21], image processing [22][23][24] and medical signal analyses [25][26][27][28], etc.…”

Section: Introductionmentioning

confidence: 99%

Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Zong,

An,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The paper focuses on developing a stochastic resonance system designed for the detection of weak signals under Alpha-stable-distributed noises. Initially, in view of the strong impulsive characteristics of noises, a Linearly-coupled Sigmoid Bistable Stochastic Resonance (LSBSR) system is proposed, which is constructed by potential function and sigmoid function. Through formula derivation, it is theoretically proved that the output SNR of the LSBSR system is superior to that of the classical bistable stochastic resonance (CBSR) system. Then, a new signal processing strategy based on the LSBSR system is introduced. Simulation experiments have demonstrated that under the input SNR=-20dB, the detection probability of the LSBSR system exceeds 95% for the Alpha-stable-distributed noise with α=1.5. When α is reduced to 0.1, the detection probability approaches 80%, significantly outperforming other detection methods. Finally, the LSBSR system is applied to detect sea-trial signals with an SNR improvement (SNRI) of 22.5dB, which further validates the practicability of the proposed system.

show abstract

The Novel Method of Magnetic Anomaly Recognition Based on the Fourth Order Aperiodic Stochastic Resonance

Cited by 16 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

Adaptive Basis Function Method for the Detection of an Undersurface Magnetic Anomaly Target

Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Contact Info

Product

Resources

About