A real-time battle situation intelligent awareness system based on Meta-learning & RNN

As an important part of modern war, battlefield situation intelligent perception technology plays a crucial role in enhancing operational effectiveness and decision-making quality. In the context of informationized war, the complexity and uncertainty of the battlefield environment are increasing, and the traditional battlefield situational awareness methods have been difficult to meet the needs of modern war (1, 2). Therefore, exploring new battlefield situational awareness technologies to improve the accuracy and real-time perception has become a hot spot in current research. Intelligent battlefield situational awareness technology mainly researches how to effectively acquire, process and parse battlefield information to provide a scientific basis for operational decision-making. With the rapid development of science and technology, the battlefield environment has become more and more complex, and the accuracy and real-time requirements for battlefield situational awareness have become higher and higher. Traditional battlefield situational awareness methods, such as manual analysis and simple model prediction, have been difficult to meet the needs of modern warfare (3, 4). These methods often suffer from low computational efficiency and poor prediction accuracy when dealing with large-scale, high-dimensional and dynamically changing battlefield data.