The improvement of submarine concealment is a complex system engineering, which needs to be optimized in multiple dimensions by combining AI technology and underwater combat characteristics. The following professional analysis is carried out from the technical implementation level： ####1. Strengthening of dynamic autonomous navigation system 1. Quantum reinforcement learning path planning -Develop a Quantum-enhanced DRL model based on quantum computing optimization, use Grover algorithm to accelerate optimal path search, and realize nanosecond decision response in a threat parameter space of more than 1000 dimensions -Integrate the ocean current profile database and real-time acoustic tomographic data to construct a three-dimensional hydrodynamic field prediction model to achieve the optimal energy gliding path at a speed of 0.1 knots 2. Multimodal sensor fusion architecture -Design a federal learning framework for heterogeneous sensors, and use a gated circulation unit (GRU) to realize inertial navigation, geomagnetic matching, and cross-domain feature extraction of gravity anomalies -Develop a long-term error compensation model based on Transformer-XL to suppress the cumulative INS error to the level of 0.01°/h and realize 72-hour passive navigation #### 2. Adversarial concealment enhancement technology 3. Generation of underwater acoustic characteristics -Construct a deep convolutional confrontation network (DCGAN) to generate adversarial acoustic fingerprints through Wasserstein distance optimization -Realize the phase modulation of the active sonar echo, generate a random phase shift of ±15° in the 1-10kHz frequency band, and reduce the accuracy of enemy LOFAR spectrum recognition to less than 35% 4. Intelligent suppression of electromagnetic radiation -Deploy an LSTM network with a spatiotemporal attention mechanism to predict ionospheric disturbances during the communication window and dynamically optimize the ultra-low frequency (ELF) transmission time slot -The neural radiation field (NeRF) technology is used to reconstruct the electromagnetic environment of the sea surface to realize the combined stealth of multiple physical fields during the satellite over-peak period ####3. Environmental intelligent perception system 5. Enhanced cognition of seabed topography -Build a multi-scale residual network (MS-ResNet), integrate multi-beam bathymetry and side scan sonar data, and realize 0.5-meter resolution seabed substrate classification -Develop a meta-learning algorithm for terrain contour matching (TERCOM) to achieve more than 95% terrain adaptability in uncharted waters 6. Optimization of biological cover strategy -Use graph neural network (GNN) to analyze the behavior of marine biota and establish a predictive model of cetacean acoustic activity -Design an active masking strategy based on bioacoustic characteristics, superimposing a bionic pulse sequence in the frequency band of sperm whale vocalization (2-4kHz) ####4. System-level reliability guarantee 7. Digital twin health Management -Construct a physical information neural network (PINN) of submarine power system to monitor the status of propeller bearings in real time -Realize fault prediction through migration learning, advance the early warning time of mechanical abnormalities to 72 hours, and reduce vibration and noise by 12dB 8. Intelligent energy distribution system -Develop a multi-objective particle swarm optimization algorithm to balance the detection load and the energy consumption of the propulsion system -Realize the fuzzy PID control of the Sterling engine in silent mode to increase the thermal efficiency to 46% #### Technical challenges and countermeasures 1. Underwater data scarcity problem: Using the framework of migration learning and generative pre-training (GPT) to enhance the generalization ability of the model through the virtual marine environment 2. Real-time bottleneck: Deploy Neural architecture search (NAS) to optimize edge computing units to achieve 50ms inference latency on the NVIDIA Jetson AGX platform 3. Countering the risk of escalation: construct a dynamic defense mechanism based on game theory, and use meta-reinforcement learning to realize the online evolution of counter strategies At present, the U.S. Navy has tested an AI-assisted decision-making system on Virginia-class submarines, and data from actual military exercises in 2023 show that the success rate of concealed breakthrough defense has increased by 27%. In the future, it is necessary to focus on breaking through the integrated application of quantum magnetometers and neuromorphic acoustic arrays to build a new paradigm of underwater intelligent combat.