Optimizing the automatic navigation and target recognition systems of drones can be achieved through a variety of AI algorithms and technologies to improve combat efficiency. Here are a few key areas and methods: 1. **Deep learning model**: - Use convolutional neural networks (CNN) for target recognition. Through large-scale data training, the model can identify various targets on the battlefield, such as enemy vehicles, buildings, and personnel. - Use transfer learning to improve model performance on limited data sets. 2. **Reinforcement learning**: - Use reinforcement learning to optimize drone navigation strategies so that it can autonomously choose routes and avoid obstacles in complex environments. Learn efficient paths through interaction with the environment to maximize mission success. 3. **Edge computing**: - Implementing edge computing on drones and transferring data processing to the drone itself can reduce latency and improve real-time response capabilities. AI models can process sensor data in real time and make decisions quickly. 4. **Multi-sensor fusion**: - Comprehensively use data from multiple sensors such as LiDAR, cameras, infrared sensors, etc., and improve target recognition accuracy and navigation reliability through data fusion technology. 5. **Distributed AI System**: - Deploy multiple drones to form a self-organizing network system, and use collective intelligence to achieve more complex tasks such as regional monitoring, target tracking and data sharing. 6. **Map Building and Path Planning**: - Use SLAM (Simultaneous Localization and Mapping) technology to update the environmental map in real time to assist drones in self-navigation in a changing battlefield environment. Combine A* algorithm or Dijkstra algorithm for dynamic path planning. 7. **Anomaly Detection and Adaptive Decision-making**: - Detect potential threats or abnormal behaviors through machine learning algorithms and implement adaptive strategies to quickly respond to dynamic changes in the battlefield. 8. **Simulation and Training**: - Use virtual environments for training to simulate different battlefield scenarios and tasks to improve the performance of drones in real battlefield environments. Use Generative Adversarial Networks (GANs) to generate more diverse training data. 9. **Task Prioritization and Scheduling**: - Implement an intelligent scheduling system to prioritize tasks based on real-time intelligence and target importance to make the most efficient use of drone resources. Through the above methods, combined with actual application needs, the automatic navigation and target recognition systems of drones can be continuously optimized and iterated, which can significantly improve their combat efficiency and provide strong technical support for military operations.