摘要
人工智能技术应用带来军事态势感知能力极大增强,日趋成为联合全域指挥控制的关键使能因素。然而,由于战场边缘环境的受限特性,人工智能和机器学习在战场物联网应用中面临新的挑战。针对战场边缘特性及其智能化应用需求,总结了美陆军面向战场物联网的边缘智能发展现状,分析了其在该领域的研究重点,介绍了其在加速边缘智能处理异构智能资源融合方面的典型解决方案,最后得出提升分布式智能处理效率、增强网络韧性、优化异构资源融合是增强战场边缘智能的发展思路的结论,以期为战场边缘智能相关研究提供参考。
Artificial intelligence(AI)technology applications have greatly enhanced military situational awareness,which is increasingly becoming a key enabler for Joint All-Domain Command and Control(JADC2).However,due to the limited characteristics of battlefield edge environment,artificial intelligence and machine learning are facing new challenges in Internet of Battlefield Things(IoBT)applications.According to the characteristics of tactical edge and AI application requirements,this paper summarizes the status of IoBT edge intelligence developed by U.S.Army,introduces research focuses in this field,and explores typical solutions in accelerating edge intelligence processing and integrating heterogeneous intelligent resources.Finally,it is concluded that improving distributed intelligence processing efficiency,enhancing network resilience and optimizing heterogeneous resource integration are the development ideas,which provides reference for battlefield edge intelligence related research.
作者
李琨
姜典辰
LI Kun;JIANG Dianchen(Southwest China Institute of Electronic Technology,Chengdu 610036,China;China Academy of Electronics and Information Technology,Beijing 100041,China;CETC Research Center for Development and Strategy,Beijing 100043,China)
出处
《电讯技术》
北大核心
2023年第2期300-306,共7页
Telecommunication Engineering
基金
军内重点科研项目。
关键词
战场物联网
边缘智能
联合全域指挥控制
韧性网络
分布式架构
Internet of Battlefield Things(IoBT)
edge intelligence
joint all-domain command and control(JADC2)
resilient network
distributed architecture
