Machine learning technology can optimize the performance of UAV clusters in military reconnaissance through two aspects: path planning and target recognition, as follows： ### Path planning -**Environmental perception and modeling** -**The use of machine learning algorithms to process the sensor data carried by drones**: Drones can be equipped with cameras, lidar, millimeter wave radar and other sensors. Deep learning algorithms in machine learning, such as convolutional neural networks (CNN), can analyze the images captured by the cameras and identify terrain, obstacles and other information;At the same time, combined with lidar data, it accurately perceives the three-dimensional structure of the surrounding environment and provides basic data for path planning. -**Build an environmental model**: Through machine learning methods, the perceived environmental information is integrated to build an environmental model including topography, potential threat areas, etc. -**Path generation and optimization** -**Based on search algorithm combined with machine learning**: Traditional path search algorithms such as A* algorithm and Dijkstra algorithm have limitations in complex environments. Introducing enhanced learning algorithms in machine learning, drones can continuously try different paths in the simulated environment, and obtain reward or punishment signals based on environmental feedback (such as whether to collide, whether to approach the threat area, etc.), so as to learn the optimal path strategy. -**Real-time path adjustment**: During the flight process, the UAV continuously senses environmental changes, and the machine learning model adjusts the path in real time based on the newly acquired data. If you encounter sudden enemy activity or terrain changes, you can quickly re-plan your path, avoid dangerous areas, and ensure the completion of reconnaissance missions. ### Target recognition -**Feature extraction and classification** - **Machine learning algorithms extract target features**: Using target detection algorithms in deep learning, such as Faster R-CNN, YOLO, etc., the images captured by drones are processed to automatically extract the characteristic information of the target, such as shape, texture, color, etc., and compare it with known target categories. -**Multi-modal data fusion recognition**: In addition to visual image data, it can also integrate other sensor data carried by drones, such as radar reflected signals, infrared thermal imaging, etc. Machine learning algorithms can comprehensively analyze these multimodal data to improve the accuracy and reliability of target recognition. -**Target tracking** -**Using machine learning to achieve target tracking**: Using tracking algorithms in machine learning, such as Kalman filtering, particle filtering, etc., combined with the historical location of the target and the current image information, the target is continuously tracked. When the target temporarily loses its visual characteristics in a complex environment, the tracking algorithm can still predict the target's position based on other sensor data and motion models to ensure that the target is not lost. -**Dynamic update of the target model**: As the reconnaissance process advances, new images and data are continuously transmitted, and the machine learning model updates the characteristic model of the target in real time to adapt to the possible attitude and appearance changes of the target, and ensure the accuracy and stability of tracking.