Proceedings PaperParadigms for information processing in multisensor environments
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Optimality of decisions in multi-sensor environments calls for processing and management of large volumes of data with differing resolutions, varied noise/clutter background conditions and rapidly changing environmental scenarios. This represents a potentially challenging task especially in view of the real-time constraints imposed by environments such as those encountered in defense applications. The study starts with a discussion of how the fusion process can be conceived at different levels. This is followed by a presentation of the alternative categorizations of the multisensor environment based on the characteristics of the knowledge available therein. Different paradigms corresponding to these categorizations are presented starting with traditional paradigms for learning in completely known environments. But these do not always meet the challenge posed by real world mult.isensor environments. This calls for more adaptive learning strategies to efficiently tackle the dynamic nature of the environment. Accordingly, a specirum of paradigms are presented which are designed to aid synergistic learning and reliable decision making and serve as a guide to the conceptual design of information processing systems in multi-sensor environments. In addition, several avenues for further research and developmental efforts are identified during the presentation of these paradigms.