Discrimination Mode Processing for EMI and GPR Sensors for Hand-Held Land Mine Detection

Ho, K. C.; Collins, Leslie M.; Huettel, Lisa G.; Gader, Paul D.

doi:10.1109/tgrs.2003.817804

Cited by 65 publications

(32 citation statements)

References 37 publications

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“…And the continuous model performed a little better than the discrete model. These results can be compared to methods reported on in [10] generated by a hand-held GPR on the JUXOCO grid. The HMM with the EFGPR performs better than the baseline hand-held method that uses pointwise processing but not as well as the spatial processing method.…”

Section: Fusion Resultsmentioning

confidence: 99%

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Hidden Markov Models for Sensor Fusion of EMI and GRP

Gader¹

2003

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Paul Gader, Principal InvestigatorStatement of problem studied. An investigation of hidden Markov models (HMMs) as a means of processing Ground Penetrating Radar signals for discrimination between anti-tank (AT) and antipersonnel (AP) landmines and discrete clutter objects was conducted. Hidden Markov models had been used successfully previously in AT landmine detection with GPR but had not been used as a discriminant between mines and clutter nor had they been used on AP mines. Experiments were conducted on data collected at the JUXOCO calibration grid. Summary of the most important results.Continuous and discrete HMMs were trained and tested and evaluated on the grid using both the Baum-Welch and discriminative training algorithms. Experimental results suggest that discriminative training algorithms should be used for training HMMs for landmine detection, fusion of continuous and discrete models provided improved performance, and that, although HMMs with the existing feature sets perform well for detection of AT mines, new feature sets should be developed for discriminating between mines and clutter objects.

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Section: Fusion Resultsmentioning

confidence: 99%

“…However, there are several differences in experimental design that make the comparison less than conclusive. For example, in [10] the positioning was much more precise, which is a significant issue in detection and discrimination of AP mines.…”

Section: Fusion Resultsmentioning

confidence: 99%

Hidden Markov Models for Sensor Fusion of EMI and GRP

Gader¹

2003

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“…In addition, an attempt to use a metal detector and a GPR together in the landmine detection has been made [34].…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Park¹,

Park²,

Kim³

et al. 2013

PIER

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Abstract-In this paper, we present a method for detecting antitank or anti-personnel landmines buried in the ground. A set of data generated by a ground penetrating radar is processed to remove the surface reflection and clutter, yielding signals for possible landmines. In order to detect landmines in the signals, features are computed and compared against a database, which contains those of various landmines. Three features are proposed to use; principal components from principal component analysis, Fourier coefficients and singular values from singular value decomposition method, each of which is chosen to represent each landmine uniquely. Detection is performed using Mahalanobis distance-based method. Examples show that the proposed method can effectively detect landmines in various burial condition.

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“…In this context, the objectives can be the obtaining of a detection warning signal (DWS) along the scanning path, 2D depth imaging of the scanning line or 3D imaging of the suspicious region in both depth and moving direction. Identification processes [2][3][4][5][6][7] can be applied after the buried object location is determined. There are numerous methods to detect buried objects utilizing GPR; linear prediction [8][9][10], principal component analysis [11,12], independent component analysis [11], wavelet domain [13], frequency domain correlation [14,15], time domain correlation [16], linear minimum mean square error estimation, [17], Gumbel distribution [18], and least square-based [19] methods can be given in this scope.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Sezgin

2011

EURASIP J. Adv. Signal Process.

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In this article, a simultaneous buried object detection and imaging method is proposed for time domain ground penetrating radar (GPR) data. Fuzzy weighted background removal is applied to the data through a sliding window and then target energy functions are obtained by means of convolution summations of consecutive A-scan signals in an appropriate manner. An auxiliary detection function is proposed as an emphasized detection test statistic and then an automatic detection warning signal creation method is devised. The proposed method has been tested over a set of small-sized surrogate anti-personnel (AP) mines which are not easily detectable and medium-sized surrogate AP and Anti-tank mines. The results are promising as nearly full detection performance. Zero false alarm rate is achieved in this dataset without remarkable corruption in estimated target GPR images. Moreover, it is observed that the noise immunity of the proposed method is highly satisfactory in terms of detection probability.

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Discrimination Mode Processing for EMI and GPR Sensors for Hand-Held Land Mine Detection

Cited by 65 publications

References 37 publications

Hidden Markov Models for Sensor Fusion of EMI and GRP

Hidden Markov Models for Sensor Fusion of EMI and GRP

Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

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