Methodology of classification and recognition of the radar emission sources based on Bayesian programming

Abstract

This study considers the Bayesian programming methodology for recognition and classification of radio emission sources. A mathematical model of Bayesian programming proposes forming a family of probability distributions based on known parameters contained in a training sample (database). The correlations between the object classes have been estimated according discussed methodology. The received assessment has been used to separate procedures of recognition and classification of radar emission sources. The simulation of methodology has carried out for four parameters of radar signals (frequency range, pulse width, pulse repetition interval and radar rotation frequency) by used database with 346 classes and 16 types of radar. Based on the existing database of radar emission sources, it is possible to predict the probability of class recognition for the general population of objects, if its distribution is known. This study demonstrates the consistency of the Bayesian programming methodology for object identification in ELINT systems.