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
It is significantly important to have the knowledge of electromagnet situation of battlefield,because it contains a lot of useful information to understand the situation of the whole battlefield.Electromagnet situatio...It is significantly important to have the knowledge of electromagnet situation of battlefield,because it contains a lot of useful information to understand the situation of the whole battlefield.Electromagnet situation visualization would be very useful to help people to understand the situation,and handle battlefield situation,such as the size and quality of the electromagnetic radiation source,target objects,and operational purposes by the two parties engaged in combat scale.This paper proposes two electromagnet situation visualization methods,one is an isoline tracking algorithm based on floating rectangle and the other is an isoline filling algorithm based on linear interpolation,which also solves the problem of low accuracy and low efficiency.This paper also implements and displays the visualization results using different ways of demonstration modules,which show that the visualization results are more intuitive.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It is significantly important to have the knowledge of electromagnet situation of battlefield,because it contains a lot of useful information to understand the situation of the whole battlefield.Electromagnet situation visualization would be very useful to help people to understand the situation,and handle battlefield situation,such as the size and quality of the electromagnetic radiation source,target objects,and operational purposes by the two parties engaged in combat scale.This paper proposes two electromagnet situation visualization methods,one is an isoline tracking algorithm based on floating rectangle and the other is an isoline filling algorithm based on linear interpolation,which also solves the problem of low accuracy and low efficiency.This paper also implements and displays the visualization results using different ways of demonstration modules,which show that the visualization results are more intuitive.
