Machine learning has significant potential in improving the automatic target recognition (ATR) and rapid strike capabilities of micro-weapons, but its application must strictly comply with international law and ethical norms. The following is the analysis framework from a technical perspective： ###1. Enhanced target recognition 1. **Multi-modal data fusion** -Process multi-source sensor data such as infrared/visible light/radar through convolutional neural network (CNN) and Transformer architecture to improve the target detection rate in complex environments (for example, YOLOv7's mAP in noisy environments can reach 85%+) -The time domain feature extraction network processes the trajectory of the dynamic target, and the LSTM module can predict the position of the moving target within 3-5 seconds in the future. 2. **Small sample learning** - Using meta-learning (MAML) and migration learning, the classification accuracy rate of more than 90% can be maintained when there are only 100-200 samples. -Generate an adversarial network (GAN) to synthesize rare target samples to solve the problem of uneven battlefield data ### 2. Decision optimization 1. **Strengthen learning decision-making** -The decision-making model based on the PPO algorithm can realize 200ms-level combat decision-making in a simulated environment -Multi-agent reinforcement learning (MARL) coordinates the bee colony combat unit, and experiments have shown that it can improve the tactical collaboration efficiency by 30%. 2. **Uncertainty management** -Bayesian neural network quantifies the recognition confidence level, when the confidence level<Manual review mechanism is automatically triggered at 85% -The online learning system can update the model parameters every hour to adapt to the dynamic changes in the battlefield ###3. System integration challenges 1. **Edge computing** -The quantified lightweight model (such as MobileNetV3) can achieve 15FPS real-time processing on an embedded chip with 2W power consumption -The federal learning framework realizes knowledge sharing between terminal devices and reduces data traffic by 70% 2. **Confrontation defense** - Adversarial training enables the system to maintain >80% robustness under FGSM attacks -Hardware-level trusted execution environment (TEE) protects core model parameters ###4. Ethical restraint mechanism 1. **Compliance verification** -Run the IHL rule engine in real time, and each strike command needs to pass a 16-dimensional legitimacy verification matrix -Audit logs are stored in the BLOCKCHAIN to ensure that all decisions are traceable 2. **Man-machine collaboration** - Keep the last 200ms manual rejection window -Cognitive load optimization interface improves operator information processing efficiency by 40%% The current technological bottleneck is that the accuracy rate of asymmetric target recognition (such as camouflage targets) in complex urban environments is still less than 75%, and the computing power limitations of the micro-platform cause the model size to be compressed to less than 1MB. The future development direction may focus on neuromorphic computing and pulsed neural networks to achieve order-of-magnitude improvements in energy efficiency. Any actual deployment must pass the compliance review of the CCW framework of the Convention on Certain Conventional Weapons and establish a strict fail-safe mechanism. It is recommended that at least 15% of the budget for R&D investment be used for ethical safety research.