Machine learning and radio emitter threat degree judgment based on fuzzy neural network

Modern electronic warfare system must judge the threat degree of coming radio emitters correctly in order to counter them by the limited jamming resource effectively. This article puts forward a new strategy to judge the radio emitter threat degree (RETD) based on machine learning. It firstly gets the membership degrees of the input data. Then the input data is classified. A trained fuzzy neural network (FNN) with approaching ability gives the threat degree. The RETD judgment rules could be mined from the network. The correctness and effectiveness are proved in the experiment.