Mine Identification and Classification by Mobile Sensor Network Using Magnetic Anomaly

Nazlibilek, Sedat; Kalender, Osman; Ege, Yavuz

doi:10.1109/tim.2010.2060220

Cited by 22 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our novel study, 10 different types of explosives were classified with 95% performance. * In the study of Nazlıbilek et al, active measurements were made the MA method was used, and the explosives were detected and visualized [29]. In our study, passive measurements were made, explosives were detected, and real time imaging was performed.…”

Section: Discussionmentioning

confidence: 79%

“…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%

“…(i) The BE is exposed to a high level, constant magnetic field, and measurements are made with magnetic field measurement sensors over it. MA is detected, and the BE is interpreted by means of the data obtained [29][30][31][32]. Since an external fixed magnetic field is applied to the BE, the measurement made in this way is named an active measurement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2019

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2019

View full text Add to dashboard Cite

show abstract

“…The reason for selecting them is the ease of implementation and their high performance. It differs from the algorithm used in [5] and improves the performance of our new system. There are a lot of techniques and methods to detect, identify, and classify buried magnetic materials, such as antipersonal and antitank mines in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…There are so many techniques for mine detection that we prefer the cheapest and less power consuming one among the others. It is the use of magnetic anomalies created by the magnetic materials at the Earth's magnetic field [1], [5], [8]. In our studies, we use a recently developed sensor that is so-called anisotropic resistive sensor KMZ51 AMR.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Performance of Magnetic Material Identification System by SIFT–BRISK and Neural Network Methods

et al. 2015

Self Cite

View full text Add to dashboard Cite

Industry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained. Index Terms-Binary robust invariant scalable keypoint (BRISK), identification, mine detection, neural networks, principal component analysis (PCA), scale-invariant feature transform (SIFT).Yavuz Ege received the M.S. and Ph.D. degrees from the

show abstract

Interpretation of signature waveform characteristics for magnetic anomaly detection using tunneling magnetoresistive sensor

Shen

Wang

Shi

et al. 2019

Journal of Magnetism and Magnetic Materials

View full text Add to dashboard Cite

Mine Identification and Classification by Mobile Sensor Network Using Magnetic Anomaly

Cited by 22 publications

References 19 publications

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

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

A Study on the Performance of Magnetic Material Identification System by SIFT–BRISK and Neural Network Methods

Interpretation of signature waveform characteristics for magnetic anomaly detection using tunneling magnetoresistive sensor

Contact Info

Product

Resources

About