Optimizing fusion architectures for limited training data sets

A method is described to improve the performance of sensor fusion algorithms. Data sets available for training fusion algorithms are often smaller than desired, since the sensor suite used for data acquisition is always limited by the slowest, least reliable sensor. In addition, the fusion process expands the dimension of the data, which increases the requirement for training data. By using structural risk minimization, a technique of statistical learning theory, a classiÞer of optimal complexity can be obtained, leading to improved performance. A technique for jointly optimizing the local decision thresholds is also described for hard-decision fusion. The procedure is demonstrated for EMI, GPR and MWIR data acquired at the US Army mine lanes at Fort A.P. Hill, VA, Site 71A. It is shown that fusion of features, soft decisions, and hard decisions each yield improved performance with respect to the individual sensors. Fusion decreases the overall error rate (false alarms and missed detections) from roughly 20% for the best single sensor to roughly 10% for the best fused result.