Buried land mine detection using complex natural resonances on GPR data

Kolba, Mark P.; Jouny, Ismail

doi:10.1109/igarss.2003.1293909

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The mass analysis technique is preferred more often for military purposes because it is faster. Ground penetrating radar [5][6][7][8][9][10][11][12][13], electromagnetic induction [1,14,15], nuclear quadrupole resonance [4,[16][17][18], acoustic seismic [1,[19][20][21][22], and neutron-based measurement [23][24][25][26][27][28] are the most important methods using mass analysis technique. In all of these methods, an underground signal is broadcast and the reflected signal from the buried object is evaluated by the BED system.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

2019

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“…The landmine detection is essential for mankind but also expensive because of its unclear position [8,9]. Several methods are utilized to detect landmines including video impulse GPR system [8], multiple landmine detection [1], the Acoustic laser method [10], clutter modelling [11], Prony's algorithm [12], Complex-valued neural networks [13], Bispectrum method [14] and Karhunen-Loeve (KL) transform [15,16].…”

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confidence: 99%

“…Prony's algorithm finds the domain complex natural resonances (CNRs) for vectors of the time-domain. In this method, the distance-based detectors process an unknown image with CNR features known objects [12].…”

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confidence: 99%

Landmine Detection by Correlation Method in Different Environments

2022

Trans. Mach. Intell.

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In this paper, the purpose is the detection of a landmine in an environment using the scattering parameter. The Ground Penetrating Radar (GPR) measures the scattering parameter. The scattering parameter is taken from a simple environment in which a landmine exists alone as a reference signal. Signals taken from the complex environment are compared with the reference signal. In fact, the presence of a landmine influences the scattering parameter. The similarity between measured signal and reference signal determines the presence of the landmine. The correlation function calculates similarity. This method is very applicable because the scattering parameter is unique.

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Performance Analysis of Techniques Used for Determining Land Mines

Ege¹,

Kakilli²,

Kılıç³

et al. 2014

IJG

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Today, remote sensing is used for different methods and different purposes. In all of the detection methods, some considerations such as low energy consumption, low cost, insensitivity to environmental changes, high accuracy, high reliability and robustness become important. Taking into account these facts, remote sensing methods are used in applications such as geological and archeological research, engineering areas, health services, preserving and controlling natural life, determination of underground sources, controlling air, sea and road traffic, military applications, etc. The method to be used is based on the object type to be detected, material to be made, and location to be found. The remote sensing methods from the past up to today can be listed as acoustic and seismic, ground penetration radar (GPR) detection, electromagnetic induction, infrared (IR) imaging, neutron quadrupole resonance (NQR), thermal neutron activation (TNA), neutron back scattering, X-ray back scattering, and magnetic anomaly detection. In these methods, detected raw images have to be processed, filtered and enhanced. In order to achieve these operations, some algorithms are needed to be developed. In this study, the methods used in detecting land mines remotely and their performance analysis have been given. In this way, the last situation on the advantages and disadvantages of methods used, application areas and detection accuracies are determined. Furthermore, the algorithms such as transmission line matrix (TLM), finite difference time-domain (FDTD), the method of moment (MoM), split step parabolic equation (SSPE) and image processing and intelligent algorithms are presented in detail.

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Buried land mine detection using complex natural resonances on GPR data

Cited by 3 publications

References 3 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

Landmine Detection by Correlation Method in Different Environments

Performance Analysis of Techniques Used for Determining Land Mines

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