An Evidential Model of Multisensor Decision Fusion for Force Aggregation and Classification

Abstract

This paper describes airborne sensor networks for target detection and identification in military applications. One challenge is how to process and aggregate data from many sensor sources to generate an accurate and timely picture of the battlefield. The majority of research in data fusion has focused primarily on level 1 fusion, e.g., using multisensor data to determine the position, velocity, attributes, and identity of individual targets. In this paper we present a novel approach to military force aggregation and classification using the mathematical theory of evidence and doctrinal templates. Our approach helps commanders understand operational pictures of the battlefield, e.g., enemy force levels and deployment, and make better decisions than adversaries in the battlefield. A simple application of our approach is illustrated in the OTBSAF simulation testbed and RETSINA system.