Uncertain Information Fusion for Force Aggregation and Classification in Airborne Sensor Networks

Abstract

The paper describes airborne sensor networks for target tracking and identification in military applications. The raw information about targets from airborne sensors is uncertain and often noisy. One challenge in airborne sensor networks is how to effectively fuse enormous amounts of uncertain and noisy information for better battlefield situation assessment. In this paper we present a novel approach to military force aggregation and classification using Dempster-Shafer theory 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 sample application of our approach is illustrated in the simulated testbed OTBSAF and RETSINA system.