Single-sensor processing and sensor fusion of GPR and EMI data for land mine detection

Gao, Ping; Tantum, Stacy L.; Collins, Leslie M.

doi:10.1117/12.356994

Cited by 15 publications

(13 citation statements)

References 11 publications

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“…Bayesian analysis has also been applied to time-domain and frequency-domain EMI sensor data, with frequency-domain data shown to have better land mine detection capability [1,5]. A Bayesian approach has been applied to discriminating buried metallic landmines from buried metallic background based on time-and frequency-domain EMI sensors signal energy and exponential decay rates [6].…”

Section: Metal Detector-based Land Mine Detection Algorithmmentioning

confidence: 99%

Advances in EMI and GPR algorithms in discrimination mode processing for handheld landmine detectors

Stanley

Gader

et al. 2004

Detection and Remediation Technologies for Mines and Minelike Targets IX

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This paper presents some advancement in the detection algorithms using EMI sensor, GPR sensor and their fusion. In the EMI algorithm, we propose the application of the weighted distributed density (WDD) functions on the wavelet domain and the time domain of the EMI data for feature based detection. A multilayer perceptron technique is then applied to discriminate between mine and clutter objects based on the wavelet domain and time domain features separately. When the results from the two domains are fused together, the probability of false alarms is reduced by a factor of two. The enhancement in the GPR algorithm includes the depth processing which selects a certain data segment below the ground surface for detection, as well as utilizing the phase variation of the signal return across a mine to achieve better detection. Finally, we present fusion results from EMI and GPR sensors to demonstrate that the two sensors provide complementary information and when they are properly fused together the probability of false alarm can be reduced significantly.

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Section: Metal Detector-based Land Mine Detection Algorithmmentioning

confidence: 99%

Advances in EMI and GPR algorithms in discrimination mode processing for handheld landmine detectors

Stanley

Gader

et al. 2004

Detection and Remediation Technologies for Mines and Minelike Targets IX

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“…To a certain component of the electronic equipment, we can define the assignment of the membership function m ij by formula (6).…”

Section: Correlation Coefficientmentioning

confidence: 99%

Sensor Fusion in Integrated Circuit Fault Diagnosis Using a Belief Function Model

Zhu

2008

International Journal of Distributed Sensor Networks

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In this paper, a multi-senor information fusion method based on the D-S (DempsterShafer) evidence theory is presented for fault diagnosis in an integrated circuit. By measuring the temperature and voltages of circuit components, the fault belief function assignment of two sensors to circuit components is calculated, and the fusion fault belief function assignment is obtained by using the D-S evidence theory. Then the actual fault component is precisely found according to the fusion data. Comparing the diagnosis results based on separate original data with the ones based on D-S theory fusion method, it is shown that the D-S information fusion fault diagnosis method is more accurate. Finally, two fault diagnosis examples of the simple signal magnification circuit and the integrated circuit of industrial case are given.

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“…Meanwhile, the reliability of remote sensing may be improved by fusing synergistic measurements of different types of detectors [8,10,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…The probes are in the form of hollow tubes that not only measure the stiffness of the soil but also scratch the surface of the buried objects and transfer the dust to a miniature onboard mass spectrometer to determine whether the surface is a plastic, metal, wood, or other material that can be used in landmines. Remote sensing is the other methodology in which the presence of an unexpected object on or underneath the surface is examined using sensors such as electromagnetic induction sensors (EMI) [8][9][10], X ray backscatter radiography [11], ground penetrating radar (GPR) [8,10,12], infrared cameras (IR) [13], and thermal neutron analyzers. Although prodding may yield more reliable results, remote sensing is considered more appropriate for robotics applications because it is significantly faster, safer, and more attainable.…”

Section: Introductionmentioning

confidence: 99%

Landmine detection using an autonomous terrain‐scanning robot

Najjaran

Goldenberg

2005

Industrial Robot: An International Journal

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/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1108/01439910510593938Industrial Robot Journal, 32, 3, pp. 240-247, 2005-05-01 Landmine detection using an autonomous terrain-scanning robot Najjaran, H.; Goldenberg, A. AbstractThis paper describes the software of a terrain scanning robot capable of autonomously manipulating a typical handheld detector for remote sensing of buried landmines in a manner similar to a human operator. The autonomous manipulation of the detector on unknown terrain requires an online terrain map to generate an obstacle free path for the end effector of the robot. The software includes a twofold process of map building and path planning that is implemented into a real-time software platform to take place in parallel to the other functions of the robot.Map building features a distributed sensor fusion system to tackle the uncertainties associated with the sensor data. It provides local terrain maps by fusing the redundant measurements and complementary data obtained from competitive rangefinders and joint position sensors, respectively.The fusion takes place in a multi-step data processing module that includes a batch processing filter, a static filter, and a fuzzy adaptive Kalman filter. The latter requires the dynamic model of the process so that a stochastic model is introduced for the terrain undulations. An important parameter of the model, which significantly influences the output of the filter, is the standard deviation of the probability distribution of the process disturbances. A systematic fuzzy modeling technique is used to determine the standard deviation based on the terrain type and to adapt the filter, accordingly. The outlier rejection is carried out using the Mahalanobis distance between the estimated states of the system and the new measurements.Path planning is carried out based on the terrain map to move the detector at a constant distance and parallel to the ground. Unlike the traditional methods, the path is generated in the non-Cartesian coordinate frame of the sensors to avoid a great deal of transformations involved in reproducing the terrain map in a Cartesian coordinate frame.

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Single-sensor processing and sensor fusion of GPR and EMI data for land mine detection

Cited by 15 publications

References 11 publications

Advances in EMI and GPR algorithms in discrimination mode processing for handheld landmine detectors

Advances in EMI and GPR algorithms in discrimination mode processing for handheld landmine detectors

Sensor Fusion in Integrated Circuit Fault Diagnosis Using a Belief Function Model

Landmine detection using an autonomous terrain‐scanning robot

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