A Method for Detecting Anomalies in an Electromagnetic Environment Situation Using a Dual-Branch Prediction Network

Electromagnetic environment situation anomaly detection is a prerequisite for electromagnetic threat level assessment, and its research is of great practical value. However, because of the complexity of the electromagnetic environment, electromagnetic environment situation anomaly detection is not efficient. Therefore, we propose a dual-branch prediction network-based electromagnetic environment situation anomaly detection method to predict the future and achieve anomaly detection by fusing different development characteristics of electromagnetic environment situations learned by other branches. We extract the electromagnetic environment situation state and trend features using the manual feature extraction module and mine the electromagnetic environment situation in-depth data distribution features using ConvLSTM, improve the dynamic time regularization model according to the physical characteristics of electromagnetic space, and then provide the anomaly detection method. We experimentally demonstrate the effectiveness of the proposed method in electromagnetic environment situation prediction and anomaly detection accuracy.