电磁目标表征:知识-数据联合驱动新范式
A New Paradigm for Knowledge-Data Driven Electromagnetic Target Representation
杨淑媛 1杨晨 1冯志玺 1潘求凯1
作者信息
摘要
电磁目标表征是电磁空间态势感知中的一项共性基础性问题.早期目标表征基于专家经验知识,需要设计者具有较强的专业背景与先验知识,其在复杂信号环境下的性能不佳.近年来发展起来的深度学习为复杂电磁环境下的目标信号表征提供了新的途径,它通过模拟人脑的深层结构建立机器学习模型,以端到端的方式自动表征和处理目标数据,在电磁目标检测、分类、识别、参数估计、行为认知等感知任务中显示出良好的性能.然而,深度学习严重依赖海量高质量标注数据,在现实电磁环境中存在一定局限.将知识融入智能系统一直是人工智能的研究方向,结合知识与数据进行电磁目标表征,将有望提升目标感知精度与泛化能力,正在成为电磁目标表征中新的方向.本文回顾了电磁目标表征技术的发展过程,对新的知识-数据联合驱动的电磁目标感知范式进行了展望.
Abstract
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.
关键词
目标表征/专家知识/深度学习/知识-数据联合驱动/知识图谱Key words
target representation/expert knowledge/deep learning/joint knowledge-data-driven/know-ledge graph引用本文复制引用
基金项目
国家自然科学基金(U22B2018)
国家自然科学基金(62276205)
出版年
2024
国家科技期刊平台
NSTL
万方数据