A review of GPR for landmine detection

Daniels, David J.

doi:10.1007/s11220-006-0024-5

Cited by 108 publications

(75 citation statements)

References 4 publications

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“…The purpose for deploying a GPR robotic sensor in W ⊂ R 2 is to infer the classification of targets that are either mines or clutter objects, and are buried in heterogeneous soils, under non-uniform environmental conditions [48]. The classification variable of every target, y i , is non-observable and has two mutually-exclusive possible values Y = y 1 i , y 2 i .…”

Section: Implementation Of Information Roadmap Deployment For Roboticmentioning

confidence: 99%

An Information Roadmap Method for Robotic Sensor Path Planning

Zhang

Ferrari

Qian

2009

J Intell Robot Syst

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A new probabilistic roadmap method is presented for planning the path of a robotic sensor deployed in order to classify multiple fixed targets located in an obstacle-populated workspace. Existing roadmap methods have been successful at planning a robot path for the purpose of moving from an initial to a final configuration in a workspace by a minimum distance. But they are not directly applicable to robots whose primary objective is to gather target information with an on-board sensor. In this paper, a novel information roadmap method is developed in which obstacles, targets, sensor's platform and field-of-view are represented as closed and bounded subsets of an Euclidean workspace. The information roadmap is sampled from a normalized information theoretic function that favors samples with a high expected value of information in configuration space. The method is applied to a landmine classification problem to plan the path of a robotic ground-penetrating radar, based on prior remote measurements and other geospatial data. Experiments show that paths obtained from the information roadmap exhibit a classification efficiency several times higher than that of existing search strategies. Also, the information roadmap can be used to deploy non-overpass capable robots that must avoid targets as well as obstacles.

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Section: Implementation Of Information Roadmap Deployment For Roboticmentioning

confidence: 99%

An Information Roadmap Method for Robotic Sensor Path Planning

Zhang

Ferrari

Qian

2009

J Intell Robot Syst

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“…A number of methods have been suggested over the years, from the frequently used and one of the first demining methods, metal detector (MD) [3] to trained dogs, chemical methods [4] and others. GPR is considered as a mature and well established approach for demining which has been extensively used and validated [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Numerical modelling and neural networks for landmine detection using ground penetrating radar

Giannakis

Giannopoulos

Warren

et al. 2015

2015 8th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)

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Abstract-A numerical modelling case study is presented aiming to investigate aspects of the applicability of artificial neural networks (ANN) to the problem of landmine detection using ground penetrating radar (GPR). An essential requirement of ANN and machine learning in general, is an extensive training set. A good training set should include data from as many scenarios as possible. Therefore, a training set consisting of simulated data from a diverse range of models with varying: topography, soil inhomogeneity, landmines, false alarm targets, height of the antenna, depth of the landmines, has been produced and used. Previous approaches, have employed limited training sets and as a result they often have underestimated the capabilities of ANN. In this preliminary study, a 2D Finite-Difference TimeDomain (FDTD) model has been used as the training platform for ANN. Although a 2D approach is clearly a simplification that cannot directly translate to a practical application, it is a computationally efficient approach to examine the performance of ANN subject to an extensive training set. The results are promising and provide a good basis to further expand this approach in the future by employing even more realistic, but computationally expensive, 3D models and well-characterised, real data sets.

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“…BOD has been extensively used in civil engineering [1,2] and military applications [3], such as landmine or buried water pipe detection, etc.…”

Section: Introductionmentioning

confidence: 99%

Iterative Time-Reversal Mirror Method for Imaging the Buried Object Beneath Rough Ground Surface

Zhu¹,

Zhao²,

Yang³

et al. 2011

PIER

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Abstract-An iterative Time-Reversal Mirror (TRM) method is proposed to Detect and Image the buried target beneath ground surface. Unlike the conventional TRM methods which treat the information of the ground as clutters and directly delete them, the iterative TRM imaging method proposed in this paper utilizes the information of rough ground surface as a useful knowledge. The new approach is consisted of two TRM procedures. In the first TRM procedure, it aims to image the rough surface where the propagation environment for electromagnetic wave is free space. The second TRM procedure aims to image the buried target. In this step, the information of the rough surface estimated by the first TRM procedure will be treated as newly updated propagation environment. Then conventional TRM is applied to image the buried target. By applying this iterative TRM method, the information of the rough ground can be well considered in the whole TRM procedure. Numerical simulations prove that this method performs significantly better image contrast comparing with the results obtained by using conventional TRM. 4-5 dB improvement on the imaging SNR has been achieved. Furthermore, the target can be located more accurately.

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A review of GPR for landmine detection

Cited by 108 publications

References 4 publications

An Information Roadmap Method for Robotic Sensor Path Planning

An Information Roadmap Method for Robotic Sensor Path Planning

Numerical modelling and neural networks for landmine detection using ground penetrating radar

Iterative Time-Reversal Mirror Method for Imaging the Buried Object Beneath Rough Ground Surface

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