Deep learning-based method for detecting anomalies in electromagnetic environment situation

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.