Intelligent multi-classifier fusion for decision making in ballistic missile defense applications

In the context of a large project on reliable real-time identification of ballistic missile type targets through fusion of information from disparate sensors, a variety of decisions in-decision out (DEI-DEO) fusion processing techniques are being evaluated. This paper describes the experience of employing several conventional as well as approximate reasoning based classifiers and fusing the resulting decisions through these DEI-DEO fusion processors for identification of lethal targets in the presence of decoys and debris using simulated infrared sensor data. While no final conclusions can as yet be drawn based on this limited evaluation, the study confirms the basic premise that intelligent multiclassifier fusion offers the potential for more robust performance in a dynamic environment wherein a priori assumptions made regarding the individual classifiers may not always hold