Anomaly detection with low magnetic flux: A fluxgate sensor network application

Ege, Yavuz; Çoramık, Mustafa; Kabadayı, Murat; Çıtak, Hakan; Kalender, Osman; Yürüklü, Emrah; Kurt, Unal; Nazlibilek, Sedat

doi:10.1016/j.measurement.2015.12.004

Cited by 15 publications

(11 citation statements)

References 30 publications

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“…In our study, passive measurements were made, explosives were detected, and real time imaging was performed. * Ege et al used a fluxgate sensor network in their study and reported that buried bodies having ferromagnetic content could be detected by passive measurements [33]. However, the position of the sensor network affected the results negatively.…”

Section: Discussionmentioning

confidence: 99%

“…In our study, independent measurements without being effected by position were made with fluxgate sensors. * In the previous studies [29,33,37] the measurement results changed depending on the sensor position, which made it difficult to make real-time measurements. By the measuring circuit developed in our study, position-independent measurements can be performed.…”

Section: Discussionmentioning

confidence: 99%

“…In other words, different anomalies occur in the region where the object is buried compared to the region where the object is not buried. This measurement is a passive technique, and BEs and IEDs can be detected by evaluating these anomalies [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…Fluxgate or anisotropic magneto resistive (AMR) sensor arrays have been integrated to three-axis motion systems in explosive and buried object detection studies with the MA method in the literature, and data have been collected. Although the sensor arrays were moved along the x, y, and z axes during the data acquisition phase, the positions of the sensors were not changed [33][34][35][36][37]. However, even if there is no MA, changing the position of the sensor creates voltage variations at the output of the sensors.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

2019

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Active and passive techniques are two different techniques with which to detect buried explosives. In practice, the most preferred active method works by broadcasting a signal underground. This signal may stimulate the buried explosive and cause it to explode. It is important to eliminate or minimize this drawback to ensure the safety of the detector operator. In this respect, it is important to increase the studies on the passive detection technique which is not currently used in practice. The aim of this study was to passively detect improvised explosive devices without stimulating them, and to classify underground objects as explosive or non-explosive. A fluxgate sensor array having 33 components was used for passive magnetic field measurements, and the nearest neighborhood algorithm was preferred for classifying the resulting data. In experimental studies, 33 different samples having different amounts of ferromagnetic properties were used. Successful imaging and classification were achieved for the measurements up to 20 cm below the surface of soil. Data were recorded as 32 × 25 matrices, and then they were reduced to 32 × 2 matrices having the same features. Samples having explosive properties were distinguished from other underground objects with success rates of 86% and 95% for 32 × 25 and 32 × 2 data matrices, respectively. Classification times for 32 × 25 and 32 × 2 data matrices were 42 ms and 3.62 ms, respectively. For data groups where the best results were obtained for the data matrices, frame numbers classified in one second were calculated as 23.80 and 276.2, respectively. False alarm rate achieved was 5.31%. The experimental results proved the successes of the matrices reduction and classification approach. One of the most common problems encountered in passive detecting techniques is that the sensor position affects the measurements negatively. In this paper, a solution has been proposed for this important problem.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection and Imaging of Underground Objects for Distinguishing Explosives by Using a Fluxgate Sensor Array

2019

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“…The features that are important in all sensing methods are low power consumption, low price, being unaffected by environmental changes, high precision, high reliability and stability. Some remote sensing methods used from past to present include electromagnetic induction (EMI) [1][2][3][4][5], ground effect radar (GPR) [6][7][8][9][10][11] and magnetic anomaly [12][13][14][15]18]. Magnetic anomaly is the most widely used remote sensing method due to its low price, low number of equipment and changeable sensitivity.…”

Section: Introductionmentioning

confidence: 99%

A Fluxgate Sensor Application : Coin Identification

Çıtak¹,

Çoramık²,

Ege³

2016

SIPIJ

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Today, coins are used to operate many electric devices that are open to the public service. Washing machines, play stations, computers, auto brooms, foam machines, beverage machines, telephone chargers, hair dryers and water heaters are some examples of these devices These devices include coin recognition systems. In these systems, there are coils at two different radius, which become electromagnets when the current is passed through them. The AC current supplied to the coils creates a variable magnetic field, which induces the eddy current on the coil during the passing of money. The magnetic field generated by the Eddy current reduces the current passing through the coil. The amount of change of current in the coil gives information about the coin; the type of metal (element) and the amount of metal (element). In this study, a new coin identification system (magnetic measurement system) is designed. In this system, the magnetic anomaly generated by the coin as a result of applying direct current to the coils is tried to be detected by fluxgate sensor. In this study, sensor voltages are acquired in computer environment by using developed electronic unit and LabVIEW based software. In the paper, experimental results have been discussed in detail.

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