Magnetic sensors for buried minehunting from small unmanned underwater vehicles

Clem, T.R.; Allen, George I.; Bono, J.; McDonald, Robert J.; Overway, D.; Sulzberger, G.; Kumar, Sankaran; King, David A.

doi:10.1109/oceans.2004.1405594

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The development of perception methods for magnetic sensing is influenced by the task in which they are applied. For instance, the simple processing of detected magnetic fields can be used for buried underwater mines detection, as described in Clem et al [67]. In addition, for more advanced methods, Hu et al [68] use magnetic gradient tensors to localize multiple underwater objects.…”

Section: Other Sensorsmentioning

confidence: 99%

A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

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The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed’s features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined.

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Section: Other Sensorsmentioning

confidence: 99%

A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

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“…The traditional BSR model is expressed as follows [25]: (3) where A and B are the potential parameters. Figure 3 shows the two potential wells of the bi-stable stochastic resonance model.…”

Section: Stochastic Resonance Principlementioning

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

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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.

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“…Magnetic anomaly detection (MAD) technology usually refers to the non-contact measurement of fixed or moving targets through magnetic field detection, and this technology performs certain data processing and analysis steps on the obtained magnetic field signals to obtain the correct target information [2] and then locates and identifies the target. This technology is a magnetic technique that uses magnetic changes to detect, locate, and track hidden magnetic targets (such as vehicles [3], unexploded ordnance [4,5], mines [6,7], and submarine wrecks [8,9]).…”

Section: Introductionmentioning

confidence: 99%

A brief review of magnetic anomaly detection

Zhao

Zhang

et al. 2020

Meas. Sci. Technol.

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The geomagnetic field is the main magnetic field on the surface of the Earth, and its value is generally much larger than that of ferromagnetic objects. The existence of a geomagnetic field makes the ferromagnetic material magnetized, and the magnetized field will make the local total magnetic field abnormal, so it is called an anomalous magnetic field. This unusual magnetic field is a necessary condition for conducting magnetic anomaly detection (MAD). MAD is a widely used passive method for magnetic target detection, and its applications include surface ship target detection, the monitoring of underwater moving targets, land target detection and the identification of seismic activity for metal mining. MAD technology uses a high-sensitivity magnetometer to measure the target magnetic field. The magnetic field data are used to calculate the position, velocity, volume and other parameters of the target to identify and localize the ferromagnetic target. It is of great significance to study MAD data based on geomagnetic background. This paper reviews the MAD methods proposed by researchers in recent years and summarizes them into two categories. One is target based, and the other is noise based. The target-based group of detection methods involves typical magnetic search systems based on the assumption that the magnetometer and the target move relative to each other, which applies to the case where the target motion obeys a specific tracking time mode. The noise-based detection methods are based on statistical analyses of magnetometer noise and are suitable for situations in which assumptions about the mutual motion of the target and the magnetometer cannot be made. The magnetic dipole model is introduced in the second part of the paper, and then an algorithm based on the standard orthogonal basis function (OBF) decomposition is proposed. The algorithm parallels the target to a magnetic dipole and decomposes it into a linear combination of several standard OBFs. Solving for the coefficients of the basis function yields the signal energy function in the basis function space. The results show that the signal-to-noise ratio of the data processed by the OBF algorithm is significantly improved. The OBF can be further optimized; for example, when using a single magnetometer to conduct MAD, the five OBFs can be simplified to three OBFs; to locate the target more accurately when using two magnetometers to form the gradient magnetometer, the five OBFs can be simplified into four OBFs. The OBF algorithm is not very effective in the detection of non-Gaussian white noise, so

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Magnetic sensors for buried minehunting from small unmanned underwater vehicles

Cited by 6 publications

References 13 publications

A Survey of Seafloor Characterization and Mapping Techniques

A Survey of Seafloor Characterization and Mapping Techniques

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

A brief review of magnetic anomaly detection

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