The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep le...The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection(EMES-AD).Firstly,the convolutional kernel extracts the static features of different regions of the EMES.Secondly,the dynamic features of the region are obtained by using a recurrent neural network(LSTM).Thirdly,the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES.The structural similarity algorithm(SSIM) is used to determine whether it is anomalous.We developed the detection framework,de-signed the network parameters,simulated the data sets containing different anomalous types of EMES,and carried out the detection experiments.The experimental results show that the proposed method is effective.展开更多
采用高阶矩量法(method of moment,MoM)结合高频射线法对复杂城市环境中电磁分布进行仿真计算.利用高精度的高阶MoM对辐射源进行精细仿真,利用一致性几何绕射理论的射线模型描述电磁波在电大场景中的传播,并给出了两种方法之间的接口方...采用高阶矩量法(method of moment,MoM)结合高频射线法对复杂城市环境中电磁分布进行仿真计算.利用高精度的高阶MoM对辐射源进行精细仿真,利用一致性几何绕射理论的射线模型描述电磁波在电大场景中的传播,并给出了两种方法之间的接口方案.在实际城市环境中对电磁分布和电磁态势进行了有效的计算,选取西安市某城区一平方千米区域进行了实地测量,实测数据与仿真计算进行了对比,取得了比较好的结果,证明了文中方法的有效性和实用性.展开更多
为解决未来现代化城市作战面临的作战场景环境复杂多样、电磁数据难以接入、缺乏直接有效的电磁可视化指挥系统、指挥人员难以快速获取对城市空间电磁频谱态势的直观认知等问题,并满足城市进攻作战方案拟定、关键技术研究等需求,文中深...为解决未来现代化城市作战面临的作战场景环境复杂多样、电磁数据难以接入、缺乏直接有效的电磁可视化指挥系统、指挥人员难以快速获取对城市空间电磁频谱态势的直观认知等问题,并满足城市进攻作战方案拟定、关键技术研究等需求,文中深入剖析典型城市场景构成要素,利用现代系统仿真技术,基于三维GIS(Geographic Information Service)技术,结合多种电磁频谱绘制手段,构建一种面向未来城市电磁频谱认知的可视化系统。对系统的功能工作流程、软件架构进行详细介绍,并对系统的具体应用效果进行具体阐述分析。从应用效果上来看,所构建的城市电磁环境可视化系统可以使指挥作战人员从宏观层面上准确、快速、直观地认知城市电磁频谱环境态势,从而为后续的城市电磁进攻活动提供有力保障。展开更多
基金funded by the National Natural Science Foundation of China, grant number 11975307the National Defense Science and Technology Innovation Special Zone Project, grant number 19-H863-01-ZT-003-003-12。
文摘The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection(EMES-AD).Firstly,the convolutional kernel extracts the static features of different regions of the EMES.Secondly,the dynamic features of the region are obtained by using a recurrent neural network(LSTM).Thirdly,the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES.The structural similarity algorithm(SSIM) is used to determine whether it is anomalous.We developed the detection framework,de-signed the network parameters,simulated the data sets containing different anomalous types of EMES,and carried out the detection experiments.The experimental results show that the proposed method is effective.
文摘采用高阶矩量法(method of moment,MoM)结合高频射线法对复杂城市环境中电磁分布进行仿真计算.利用高精度的高阶MoM对辐射源进行精细仿真,利用一致性几何绕射理论的射线模型描述电磁波在电大场景中的传播,并给出了两种方法之间的接口方案.在实际城市环境中对电磁分布和电磁态势进行了有效的计算,选取西安市某城区一平方千米区域进行了实地测量,实测数据与仿真计算进行了对比,取得了比较好的结果,证明了文中方法的有效性和实用性.
文摘为解决未来现代化城市作战面临的作战场景环境复杂多样、电磁数据难以接入、缺乏直接有效的电磁可视化指挥系统、指挥人员难以快速获取对城市空间电磁频谱态势的直观认知等问题,并满足城市进攻作战方案拟定、关键技术研究等需求,文中深入剖析典型城市场景构成要素,利用现代系统仿真技术,基于三维GIS(Geographic Information Service)技术,结合多种电磁频谱绘制手段,构建一种面向未来城市电磁频谱认知的可视化系统。对系统的功能工作流程、软件架构进行详细介绍,并对系统的具体应用效果进行具体阐述分析。从应用效果上来看,所构建的城市电磁环境可视化系统可以使指挥作战人员从宏观层面上准确、快速、直观地认知城市电磁频谱环境态势,从而为后续的城市电磁进攻活动提供有力保障。
