Single Target Situational Awareness in Naval Warfare Based on Environmental Feature Fusion

Situational awareness is the inference and judgment of enemy combat intentions in a real-time battlefield environment. In order to improve the accuracy of predicting the situation of single targets in naval warfare, we propose a CNN-BiGRU (CNN-Bidirectional Gated Recurrent Unit) naval warfare single target situational awareness model based on environmental feature fusion. Firstly, we propose a method of integrating environmental features with temporal features to address the impact of environmental information redundancy on intention prediction. Secondly, we combine the convolutional neural network with the BiGRU network to extract and fuse environmental and temporal features. The experimental results show that the situational awareness model that integrates environmental features, improves the accuracy of intent recognition and solves the problem of intent prediction in single target naval battles.