Sensor technologies for hunting buried sea mines

Clem, T.R.

doi:10.1109/oceans.2002.1193312

Cited by 22 publications

(16 citation statements)

References 30 publications

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“…acoustic color (described in Section III.B) [1]. Synthetic aperture techniques are being utilized for each of these sonars to reduce sensor length as compared to the lengths required for real aperture sonars.…”

Section: Search-classify-map (Scm) System Prototypesmentioning

confidence: 99%

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Progress in the development of buried minehunting systems

Clem

Lopes

2003

Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492)

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-In FY 2002, the Office of Naval Research (ONR) initiated a project to provide a deliberate capability for hunting buried sea mines. The objectives and approaches for this buried minehunting (BMH) project and the sensor technologies being pursued have been reported [1]. In this paper, we will describe progress and current status of this project, including developments in sensors, platforms, system concepts, and testing. Discussion of relevant tests that have been conducted for individual sensors and sensor combinations will be included. Future plans to test and demonstrate these technologies and concepts are discussed.

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Section: Search-classify-map (Scm) System Prototypesmentioning

confidence: 99%

“…The objectives and approaches for this buried minehunting (BMH) project and the sensor technologies being pursued have been reported [1]. In this paper, we will describe progress and current status of this project, including developments in sensors, platforms, system concepts, and testing.…”

mentioning

confidence: 99%

Progress in the development of buried minehunting systems

Clem

Lopes

2003

Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492)

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“…MAD sensors detect disturbances in the Earth's magnetic field that indicate the presence of nearby objects such as land and sea mines [1], ore and mineral deposits [2], and vehicles [3]. In all of these applications, the task is to determine if there are any objects of interest within the search area and, if any targets exist, the number of and locations of the targets.…”

Section: Introductionmentioning

confidence: 99%

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Dames

Schwager

Rus

et al. 2016

IEEE Robot. Autom. Lett.

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Abstract-Magnetic Anomaly Detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.

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“…This method can also be used for vehicle identification, traffic density observations, [11], geological studies [12], health [13], archeological investigations [14], mines, and buried explosives over the military zones [15], [16]. In recent years, the studies have been concentrated on the improvement of detection distances, accuracy, and power consumption.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis for Remote Identification of Materials With Magnetic Characteristics

Ege

Şensoy

Kalender³

et al. 2011

IEEE Trans. Instrum. Meas.

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Abstract-There is a variety of methods used for remote sensing of objects such as acoustic, ground penetration radar detection, electromagnetic induction spectroscopy, infrared imaging, thermal neutron activation, core four-pole resonance, neutron backscattering, X-ray backscattering, and magnetic anomaly. The method that has to be used can be determined by the type of material, geographical location (underground or water), etc. Recent studies have been concentrated on the improvement of the criteria such as sensing distances, accuracy, and power consumption. In this paper, anomalies created by materials with magnetic characteristics at the perpendicular component of the Earth magnetic field have been detected by using a KMZ51 anisotropic magnetoresistive sensor with high sensitivity and low power consumption, and also, the effects of physical properties of materials on magnetic anomaly have been investigated. By analyzing the graphics obtained by 2-D motion of the sensor over the material, the most appropriate mathematical curves and formulas have been determined. Based on the physical properties of the magnetic material, the variations of the variables constituting the formulas of the curves have been analyzed. The contribution of this paper is the use of the results of these analyses for the purpose of identification of an unknown magnetic material. This is a new approach for the detection and determination of materials with magnetic characteristics by sensing the variation at the perpendicular component of the Earth magnetic field. The identification process has been explained in detail in this paper.Index Terms-Anisotropic magnetoresistive sensor (AMR), magnetic anomaly, magnetic materials, power consumption, remote sensing.

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Sensor technologies for hunting buried sea mines

Cited by 22 publications

References 30 publications

Progress in the development of buried minehunting systems

Progress in the development of buried minehunting systems

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Numerical Analysis for Remote Identification of Materials With Magnetic Characteristics

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