A New Paradigm for Knowledge-Data Driven Electromagnetic Target Representation
Electromagnetic target representation is a common fundamental problem in electromagnetic space situational awareness.Early target representation was based on expert empirical knowledge,which required de-signers to have strong professional background and prior knowledge,and is performed poorly in complex signal environments.Deep learning,which has been developed in recent years,provides a new way for signal repre-sentation in complex electromagnetic environments.It simulates the deep structure of the human brain to build a machine learning model to automatically represent and process target data in an end-to-end manner,and shows good performance in perception tasks such as electromagnetic target detection,classification,identification,pa-rameter estimation,and behavioral cognition.However,deep learning relies heavily on massive amounts of high-quality labelled data,and has certain limitations in the real electromagnetic environment.Incorporating know-ledge into intelligent systems has always been the research direction of artificial intelligence.Combining know-ledge and data for electromagnetic target representation will hopefully improve target perception accuracy and generalization ability,and is becoming a new direction in electromagnetic target representation.This paper re-views the development process of electromagnetic target representation techniques,and provide an outlook on the new paradigm of electromagnetic target perception driven by joint knowledge-data.
国家科技期刊平台
NSTL
万方数据
