The use of deep learning to enhance the autonomous search and attack capabilities of torpedoes is a highly complex and sensitive subject, involving military technology and national security. Under the premise of legal compliance, academia and industry can explore potential applications of technology, but they must strictly abide by international law and national defense policies. The following is a general discussion on the optimization direction of autonomous systems from a technical point of view, and does not involve specific military details.： --- ### 1. **Environmental perception and target recognition** -**Multi-modal sensor fusion**: Combining sonar, magnetic anomaly detection, optical/infrared and other data, through deep learning (such as convolutional neural networks CNN, Transformer) to achieve target feature extraction and classification, reduce noise interference. -* *Dynamic target tracking**: Use recurrent neural networks (RNN) or LSTM to process time series data to predict the trajectory of the target (such as Kalman filtering combined with deep learning). -**Small sample learning**: In view of the scarcity of underwater target data, migration learning (pre-training model + fine-tuning) or confrontation network (GAN) synthesis training data is used. ### 2. **Autonomous decision-making and path planning** -**Reinforcement learning (RL) framework**: Design reward functions to simulate the battlefield environment, and train the model to optimize the path (such as PPO and DQN algorithms) in complex scenarios (such as avoiding decoys and obstacles). -**Multi-agent collaboration**: Achieve multi-agent collaborative combat through distributed RL, share environmental information and assign attack targets (communication delay problem needs to be solved). -**Adversarial training**: Introduce adversarial sample training to improve the robustness of the system in the environment of electronic interference or deception. ### 3. **Real-time and computational optimization** -**Edge computing deployment**: Use lightweight models (such as MobileNet, Efficientlynet) or model tailoring/quantification techniques to adapt to the limited computing power of the system. -**Hardware acceleration**: Integrated FPGA/ASIC chip accelerates neural network inference to meet the needs of millisecond response. ### 4. **Simulation and test verification** -**Digital twin technology**: Build a high-fidelity underwater battlefield simulation environment and accelerate algorithm iteration (such as Unity3D/ROS-Gazebo simulation fluid dynamics). -**Migration to physical objects**: Through Domain Adaptation technology (Domain Adaptation) to narrow the gap between simulation and real scenes. --- ### Ethics and Safety considerations -**Compliance**: It is necessary to comply with international regulations such as the Convention on Certain Conventional Weapons to avoid abuse of fully autonomous attack systems. - **Human-computer collaboration**: The “Human-in-the-loop” mechanism is retained to ensure that the final decision-making power is controlled by humans. -**Anti-interference and reliability**: Reduce the risk of false triggering through redundant design and fault detection algorithms (such as self-supervised anomaly detection). --- ### Research boundaries Research on such technologies should be limited to defensive or counter-productive purposes (such as anti-submarine training simulators, mine clearance), and must be carried out within a legal framework. The actual military application must be led by the state authorized agency and strictly confidential.