Introducing a new model of information fusion for optimized allocation of electronic warfare resources at sensor level

Military operations are done in environment which is combined with electromagnetic spectrum and its complexity is increased. In the electronic warfare, integrated view to electromagnetic spectrum, through using and combining information of different sensors, is necessary for recognition and tracing of targets. Models of information fusion can answer these necessities. Error reduction, yielding to better approximation of environment, uncertainty reduction, answering to ambiguities and solving complexities are examples of information fusion tasks. In the recent decades a lot of models of information fusion are introduced. A useful model such as JDL model which is introduced by data fusion group of Joint Directors of Laboratories in the Department of Defense of U.S., is revised and extended later by expanding the new requirements. In the cases with resource restrictions, yielding to information goals with optimum resource allocation is very important, but how to manage these realizations of goals and resources allocation in the model. In order to select the appropriate model, first the models and reasons for their modification and extension are examined. The Information Technology field has faced with this challenge, and great progresses are achieved by using Control Objectives for IT processes (COBIT) for realization of goals with optimal resource assignment, so, this approach is revised according to essential requirements of electronic warfare in the optimal resources assignment and is utilized in the proposed model. According to compatibility of extended JDL model with using of COBIT, this approach is added to the model and a new model of information fusion for optimal assignment of electronic warfare resources in the sensor level is introduced. In the final section, a battle scene is implemented and the proposed model is compared with other models.
Journal of Command and Control Communications Computer Intelligence, Volume:2 Issue: 1, 2018
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