Evidence accrual for the level 1 fusion classification problem
Stephen Craig Stubberud, Kathleen Ann Kramer, J. Antonio Geremia · 2005
Target classification is an important part of the Level 1 Fusion problem. This is particularly true for the military application of Level 1 Fusion. With precision warfare, localization of a target is often not enough and the use of sensor information to provide an estimate to what a potential target is has grown increasingly in value. A good estimate of the classification of a potential target can determine whether it will be prosecuted. To provide this estimate, a fusion system must combine pieces of information provided by various sensors and other sources over a period of time. The most common automated techniques for the classification problem provide a probability measure of the possible classes. Another concept in classification is the use of evidence accrual. As opposed to the creation of scoring techniques that represent a random variable representation of the classification, the evidence accrual technique builds scores based on the information that can be compared to other scores or thresholds of the decision process. Since evidence affects the various potential classes differently, the technique developed is based on decoupled fuzzy-logic-based Kalman filters, similar to the concept of first-order observers. The proposed technique addresses three key problems in the classification problem. It is designed to incorporate both numeric and nonnumeric sensor reports. It incorporates measurement uncertainty and provides a level of uncertainty for each class. Finally, the technique allows for evidence to be applied independently to each potential class.