The Mine Hunter-Killer (MH/K) Close in Detector (CID) system employs ground penetrating radar (GPR), forward looking IR and metal detectors that are individually and collectively processed to generate automatic target detections. The detection preprocessing is initially being accomplished on a sensor by sensor basis. For the IR sensor, we apply digital filtering techniques and morphology to detect the mines and a separate filter to characterize the background level. For preprocessing the GPR returns, we apply an inverse Fourier transform to the complex frequency return signal to obtain depth information and apply digital filtering techniques to remove fixed pattern noise and provide contrast enhancement. Metal detector returns are preprocessed using a distance measure of the return compared with the averaged background. A representative data set is extracted from the preprocessed data for each of the sensor types. This paper addresses the status of our developmental efforts for a neural net based sensor data fusion system. The sensor data fusion techniques currently being developed use fuzzy logic and neural network technology to provide a modular fusion architecture. Features extracted from GPR preprocessing are used as an input to a probabilistic neural network classifier. For IR processing, we plan to use a backpropagation neural net with a fast algorithm to learn patterns in the data. The sensor data fusion techniques, currently under development, use fuzzy logic and neural network methodologies. The probability of detection and false alarm in each approach are quantified. Performance comparisons using data collected by the MH/K system are presented for work that has been completed thus far.


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