Assessment of an active electromagnetic sensor for hunting buried naval mines

Purpura, J.W.; Wynn, W.M.; Carroll, P.J.

doi:10.1109/oceans.2004.1405587

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…For our measurements of sensor response while it moved past a stationary target, we modified the design of the sensortranslation apparatus used in our earlier work [2] to accommodate the larger size of the GEM53D and to incorporate some general improvements. The redesigned apparatus, consisting of two tall support towers bridged by an overhead rail transited by a trolley carrying the sensor holder, is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…GEM53D MODEL The GEM53D sensor described above (see Fig. 1) is geometrically more complicated than the GEM3 sensor described in previous work [2]. There are two additional sense axes at the primary-bucking coil center (GEM3Y and GEM3Z), and two new coils physically displaced along the axis of the primary (GEM5 fore and aft).…”

Section: Methodsmentioning

confidence: 99%

“…The procedure for modeling the target signals for the GEM53D is an extension of the method used for the GEM3, which was reported in a previous paper [2]. The underlying model is that of scattering time-harmonic dipole source fields from a spherical shell of arbitrary composition, with the shell imbedded in a uniform medium of arbitrary composition (typically air or seawater) [3].…”

Section: Methodsmentioning

confidence: 99%

“…The results of the 2004 Navy assessment of the performance of a Geophex GEM3 sensor in detecting and identifying underwater buried mine targets was reported in a previous OCEANS proceeding [2]. The results were encouraging but several challenges remained which are addressed in a Phase II sensor development and test project during 2005 and 2006.…”

Section: Introductionmentioning

confidence: 91%

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Assessment of an Active Electromagnetic Sensor for Hunting Buried Naval Mines, Part II

2006

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-The US Navy is investigating the use of broadband electromagnetic (EM) sensors as a candidate technology for its Buried Minehunting (BMH) sensor suite. New Navy tactics are being pursued, where short-range sensors are incorporated into reacquisition minehunting systems for shortrange classification. The current approach includes study of use of active EM sensors that employ multi-frequency sources for target illumination which can provide valuable multi-spectral discrimination for the classification of buried naval mines.In 2004, the Navy evaluated the detection/classification performance of a Geophex, Ltd. GEM3 sensor by performing underwater tests versus selected ideal targets and simulated mine targets. Test results demonstrated that GEM3 can detect metallic ordnance buried in the ground and under the sea bottom at short ranges. GEM3 tests demonstrated that electromagnetic induction spectroscopy (EMIS) techniques could be used to measure the spectral "fingerprint" of a target, which assists in target identification. FY04 GEM3 tests also showed that the active EM detection range must be increased if these sensors are to be a useful identification sensor in an autonomous underwater vehicle (AUV) reacquisition system.In this paper, we report results measuring the performance of a new Geophex active EM sensor that employs new coil configurations designed to extend the detection range and increase background noise rejection. The new coil configuration which contains multiple active EM sensors is designed at a comparable size with the 12 ¾" AUVs that the BMH program is developing for its reacquisition minehunting systems. Results from rail experiments with the new sensor operating on land are described.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

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Assessment of an Active Electromagnetic Sensor for Hunting Buried Naval Mines, Part II

2006

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“…Metallic object classification was done using spectral matching of a metallic object data library collected in air. An underwater GEM-3 sensor was also used in [73] to detect a 12 inch hollow stainless steel sphere suspended in seawater.…”

Section: Statistical Classification Of Metallic Objects In Seawater U...mentioning

confidence: 99%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawaterirradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated datawere generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the spherein 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeablematerials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receivercoil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms tonoise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression,which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models.Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram dataused for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heavisidestep-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects,including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified usingfinite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion ofelectromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without theuse of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallicspheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paperpresents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

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Nanotechnology: Improvement in Agricultural Productivity

2021

Nano‐Technological Intervention in Agricultural Productivity

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Assessment of an active electromagnetic sensor for hunting buried naval mines

Cited by 4 publications

References 5 publications

Assessment of an Active Electromagnetic Sensor for Hunting Buried Naval Mines, Part II

Assessment of an Active Electromagnetic Sensor for Hunting Buried Naval Mines, Part II

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Nanotechnology: Improvement in Agricultural Productivity

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