Sensor For Landmine Detection Research Articles

Voting fusion adaptation for landmine detection

This paper presents a voting fusion application for use with a remotely controlled multisensor vehicle platform for antitank landmine detection. Data from three landmine detection sensors mounted at the front of the vehicle enhance the probability of detection and, when combined via data fusion, limit the false alarm density to practical levels. The performance of the voting fusion scheme presented in this paper is contrasted with a heuristic data fusion approach developed by General Dynamics Canada.

Feature and decision level sensor fusion of electromagnetic induction and ground penetrating radar sensors for landmine detection with hand-held units

Abstract Strategies for fusion of electromagnetic induction (metal detector (MD)) and ground penetrating radar (GPR) sensors for landmine detection are investigated. Feature and decision level algorithms are devised and compared. Features are extracted from the MD signals by correlating with weighted density distribution functions. A multi-frequency band linear prediction method generates features for the GPR. Feature level fusion combines MD and GPR features in a single neural network. Decision level fusion is performed by using the MD features as inputs to one neural network and the GPR features as inputs to the geometric mean and combining the output values. Experimental results are reported on a very large real data set containing 2315 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions at three distinct geographical locations.

Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection

We present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the full benefits of fusion. A novel algorithm for feature-level fusion of noncommensurate, noncoincidently sampled data is described, in which a model is fitted to the sensor data and the model parameters are used as features. Formulations for both feature-level and decision-level fusion are described, along with some practical simplifications. A closed-form expression is available for feature-level fusion of normally distributed data and this expression is used with simulated data to study requirements for sample position accuracy in multisensor data. The performance of feature-level and decision-level fusion algorithms are compared for experimental data acquired by a metal detector, a ground-penetrating radar, and an infrared camera at a challenging test site containing surrogate mines. It is found that fusion of binary decisions does not perform significantly better than the best available sensor. The performance of feature-level fusion is significantly better than the individual sensors, as is decision-level fusion when detection confidence information is also available ("soft-decision" fusion).

MODELING TRANSIENT WATER DISTRIBUTIONS AROUND LANDMINES IN BARE SOILS

Sensors for landmine detection are often affected by soil water content, temperature, electrical conductivity, and/or dielectric constant. The most important of these is water content because it influences the three other properties directly. Using HYDRUS-2D, we modeled water distributions around antitank mines buried in six soil textures varying from sandy loam to clay loam under the climatic conditions of Bosnia and Kuwait. The modeling results demonstrate that soil water content regimes around landmines are strongly affected by the interaction between climate, soil type, and landmine geometry. The occasional short-term accumulation or loss of soil water around landmines depends greatly on weather conditions and soil types. Results also show that steady-state analysis of water flow around buried objects and time averaging of observed water contents may lead to unrealistic conclusions regarding the transient behavior of soil water distributions around land-mines.

A complete electromagnetic simulation of the separated-aperture sensor for detecting buried land mines

The detection of buried land mines is a problem of military and humanitarian importance. Electromagnetic (EM) sensors (ground-penetrating radars) use signals at radio and microwave frequencies for this purpose. In the past, EM sensors for land-mine detection have been empirically developed and optimized. This has involved experimental tests that are complicated, time consuming, and expensive. An alternative is to carry out initial development and optimization using accurate numerical simulations. One objective of this paper is to show, for the first time, that such simulations can be done using the finite-difference time-domain (FDTD) method. The separated-aperture sensor has been under investigation by the United States Army for land-mine detection for many years. It consists of two parallel dipole antennas housed in corner reflectors that are separated by a metallic septum. It is a continuous-wave sensor tuned to a particular frequency (typically 790 MHz). When the sensor is over empty ground, the coupling between the antennas is very small. As the sensor is moved over a buried mine, the coupling between the antennas increases indicating the presence of the mine. In this paper, the complete EM system composed of the separated-aperture sensor, air and soil, and buried land mine is modeled using the FDTD method. The finite computational volume is truncated with an absorbing boundary condition: the generalized perfectly matched layer. Detailed studies made with the simulation increase the understanding of this sensor. Results computed from the simulation are in good agreement with experimental measurements.