In military applications, the optimization of automatic navigation and target recognition systems of unmanned aerial vehicles (UAVs) needs to consider algorithm efficiency, real-time, robustness, and ethical compliance. The following discusses possible optimization directions from a technical perspective, while emphasizing the need to strictly abide by international law and war ethics (such as the principle of distinguishing military targets from civilians). --- ### **1. Autonomous navigation optimization** #### **a. Dynamic path planning and obstacle avoidance** -**Intensive learning (RL) and simulation environment training** -Use deep reinforcement learning (DRL) to simulate complex scenarios (such as cities, forests, electromagnetic interference) in a virtual battlefield environment to train drones to autonomously avoid dynamic threats (air defense radar, moving targets). -Achieve multi-drone formation collaboration through multi-agent collaborative training (such as MAPPO algorithm) to optimize the efficiency of path planning. -**SLAM and real-time environment modeling** -Combining visual inertial odometer (VIO) and LIDAR (LiDAR) to achieve high-precision positioning and map construction in a GPS rejection environment, such as using the LIO-SAM algorithm. -**Energy efficiency optimization** -Flight trajectory planning based on Bayesian optimization balances mission priority and battery life to reduce invalid maneuvers. #### **b. Anti-interference and fault-tolerant mechanism** -**Adversarial training** -Introduce anti-sample attack simulation (such as FGSM attack) in the navigation algorithm to improve the robustness of electromagnetic interference and spoofing signals. -**Multi-modal sensor fusion** -Integrate infrared, radar, and EO/IR camera data to achieve cross-modal feature alignment through the Transformer architecture to ensure stability in bad weather or camouflage conditions. --- ### **2. Goal recognition and decision optimization** #### **a. Lightweight target detection model** -**Model compression and edge computing** -Deploy lightweight networks (such as YOLO-v8n, MobileNetV3), combined with knowledge distillation technology, to achieve real-time processing (>30 FPS) in on-board computing units (such as NVIDIA Jetson). -**Small sample learning and domain adaptation** -Use meta-learning (MAML algorithm) to solve the problem of scarcity of military target labeling data, and enhance the generalization ability of the model through synthetic data (such as Unity simulation battlefield). -**Multi-task joint learning** -Synchronous training of target detection, classification, and threat assessment tasks (such as the CenterNet++ architecture) to reduce double calculations. #### **b. Intelligent decision-making and task allocation** -**Hierarchical Intensive learning (HRL)** -The high-level strategy plans global tasks (such as reconnaissance priorities), the low-level strategy controls stand-alone maneuvers, and reduces the complexity of decision-making through a hierarchical structure. -**Game theory and Dynamic prioritization** -Assign dynamic weights to high-value targets (such as command centers), and combine the auction algorithm (CBBA) to optimize the allocation of multi-drone missions. --- ### **3. Anti-destructive and safety enhancement** -**Defense against samples** -Integrate randomized defenses (such as Randomized Smoothing) in the target detection model to reduce the probability of being deceived by enemy AI. -**End-to-end encrypted communication** -Use lightweight cryptography protocols (such as LWC-ASCON) to ensure the security of the data chain, and at the same time implement distributed model updates through federated learning (FL) to avoid attacks on central nodes. --- ### **4. Ethics and compliance constraints** -**Man-machine collaboration and traceability of responsibilities** -Retain the final decision-making power of the human operator (Human-in-the-loop), and provide the basis for decision-making through interpretable AI (XAI) technology (such as Grad-CAM heat map). -**International law compliance inspection** -Embed a rule engine in the target identification process to automatically filter protected facilities (such as hospitals and schools) to avoid violations of the Law of Armed Conflict (LOAC). --- ### **5. Testing and verification framework** -**Multi-level simulation verification** -Construct a digital twin battlefield (such as using the MIL-STD-1553B protocol to simulate a complex electromagnetic environment), and evaluate the probability of system failure through the Monte Carlo method. -**Confrontational red team test** -Introduce professional red teams to simulate enemy electronic warfare and cyberattacks, and continuously optimize system weaknesses. --- ###**Technical challenges and risks** 1. **Risk of data deviation**: Excessive dependence on the simulation environment of training data may lead to actual combat performance degradation. 2. **Algorithm black box problem**: Insufficient interpretability of complex models may lead to misjudgment. 3. **Ethical controversy over autonomous weapons**: Completely autonomous kill (AWS) systems need to be clearly prohibited and follow the framework of the Convention on Certain Conventional Weapons (CCW). --- ###**Conclusion** The core of optimizing UAV combat systems lies in balancing technical effectiveness and ethical constraints. It is recommended to adopt a modular design, decouple the navigation, identification, and decision-making modules, and combine continuous human supervision and third-party audit mechanisms. At the same time, priority needs to be given to the development of defensive capabilities (such as reconnaissance and EOD) to avoid technological abuse.