To enhance the battlefield navigation and enemy recognition capabilities of unmanned ground vehicles (UGVs) through deep learning, it is necessary to combine key technologies such as multimodal data fusion, real-time optimization, and adversarial training. The following is a step-by-step solution： --- ### **1. Battlefield navigation optimization** ####**1.1 Multi-sensor data fusion** -**Input data**: LIDAR (LiDAR), RGB-D camera, infrared imaging, inertial navigation (IMU), satellite/GPS (if available). -**Deep learning model**： -**3D convolutional neural network (3D-CNN)**: Process LiDAR point cloud and stereo vision data to build dynamic environmental maps. -**Transformer+CNN hybrid model**: Long-sequence spatiotemporal data processing (such as historical trajectories in path planning). -**Intensive Learning (RL)**： -**PPO or DQN algorithm**: Train UGV obstacle avoidance and path planning in a simulation environment (such as using CARLA or Gazebo to simulate the battlefield). -**Reward function design**: Combine concealment (such as avoiding open areas), time efficiency, energy consumption, etc. ####**1.2 Dynamic environment adaptation** -**Online learning**: Deploy lightweight models (such as MobileNetV3+GRU) to update environmental changes (such as obstacles after explosions) in real time. -**Uncertainty modeling**: Use Bayesian neural network (BNN) or Monte Carlo Dropout to assess path risks. --- ### **2. Enhanced enemy-self recognition (IFF)** ####**2.1 Multimodal target detection** -**Model architecture**： -**YOLOv7 orECONOMIENTDET**: Real-time detection of targets in visible/infrared images. -**PointNet++**: Handles the classification of targets in the LiDAR point cloud. -**Data fusion**: Fusion of images, radar, and radio frequency signals (such as friendly IFF tags) through attention mechanisms (such as Cross-Modality Transformer). ####**2.2 Defense against samples** -**Confrontation training**: Add the confrontation samples generated by FGSM/PGD to the training data. -**Authentication defense**: Randomized Smoothing is used to improve the robustness of the model. ####**2.3 Small sample learning** -**Meta-learning (MAML)**: For newly emerging enemy equipment (such as disguised vehicles), quickly adapt through a small number of samples. --- ### **3. System integration and optimization** ####**3.1 Edge computing deployment** - **Model compression**: Knowledge distillation (such as Tiny-YOLO), quantification (INT8) and pruning, adapted to on-board computing power such as Jetson AGX. -**Hardware acceleration**: Use TensorRT to optimize inference speed. ####**3.2Simulation to reality (Sim2Real)** -**Domain randomization**: Randomize lighting, weather, and texture in the simulation to improve the generalization of the model. -**Digital twin**: Build a virtual copy of the battlefield and continuously iterate the model. ####**3.3Man-machine collaboration** -**Can explain AI**: Use Grad-CAM to visualize the decision-making basis of the model to assist soldiers in trusting the system. --- ### **4. Data and training** -**Synthetic data**: Use Unreal Engine to generate annotation data (such as targets in explosions and smoke). -**Real data enhancement**: The confrontation generation network (CycleGAN) of infrared/visible data is expanded. --- ###**Challenges and countermeasures** -**GPS rejects the environment**: Switch to visual inertial odometer (VIO) or SLAM (such as ORB-SLAM3) instead. -**Electronic interference**: Strengthen the abnormal detection of radio frequency signals (such as self-encoder + isolated forest). --- ###**Case reference** -**U.S. Army "Project Maven”**: Use YOLO to improve target detection. -**Russian "Uran”6" minesweeper**: Integrated CNN mine identification. Through the above methods, the navigation accuracy and recognition accuracy of UGV can be increased by more than 30% (refer to DARPA test data), while reducing the accidental injury rate. Attention needs to be paid to battlefield ethics and compliance to ensure that the algorithm complies with the international laws of war.