Abstract
In this paper we propose a neural network based approach to sensor fusion, to detect mine locations from electromagnetic induction (EMI) data. Our results use the Random Neural Network (RNN) model [2, 4, 5] which is closer to biophysical reality and mathematically more tractable than standard neural methods. The network is trained to produce an error minimizing non-linear mapping from three sensor output images to the fused image. The result is thresholded to point to likely mine locations.
This research is support by the MURI on Demining, under U.S. Army Research Office Grant No. DAAH04-96-1-0448.
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© 1997 Springer-Verlag Berlin Heidelberg
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Gelenbe, E., Koçak, T., Collins, L. (1997). Sensor fusion for mine detection with the RNN. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020273
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DOI: https://doi.org/10.1007/BFb0020273
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