Sensor fusion for mine detection with the RNN

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.