Feature and Decision Level Sensor Fusion of Electromagnetic Induction and Ground Penetrating Radar Sensors for Landmine Detection with Hand-Held Units
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.
R. J. Stanley et al., "Feature and Decision Level Sensor Fusion of Electromagnetic Induction and Ground Penetrating Radar Sensors for Landmine Detection with Hand-Held Units," Information Fusion, vol. 3, no. 3, pp. 215-223, Elsevier, Sep 2002.
The definitive version is available at http://dx.doi.org/10.1016/S1566-2535(02)00071-4
Electrical and Computer Engineering
Keywords and Phrases
Data structures; Electromagnetism; Feature extraction; Ground penetrating radar systems; Metal detectors; Neural networks; Signal processing; Landmine detection; Sensor data fusion; Electromagnetic induction; Ground penetrating radar; Hand-held unit; Image processing; Pattern recognition; Sensor fusion
International Standard Serial Number (ISSN)
Article - Journal
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