In view of the challenge of using neural networks to optimize the target tracking and interception capabilities of anti-missile systems, the following are the hierarchical technical solutions and key points： --- ###**1. Target tracking optimization** #### 1. **Dynamic trajectory prediction** -**Spatiotemporal modeling network**： -Transformer-3D structure is used to process the radar point cloud sequence, and the target acceleration mutation characteristics are captured through multi-head attention. -Integrate the LSTM-CRF model to learn typical tactical maneuver modes (serpentine maneuver, spiral dive, etc.) -**Uncertainty quantification**： -Integrate Bayesian neural network to output the probability distribution of the predicted trajectory -Use Conformal Prediction method to construct dynamic confidence interval #### 2. **Multi-source heterogeneous data fusion** -**Align the network across modes**： -Design dual-stream PointNet++ architecture to process radar/infrared data -Introduction of self-supervised comparative learning, pre-training modal alignment module -**Adaptive attention mechanism**： -Develop an interpretable attention weight visualization system -Real-time calculation of sensor confidence indicators (SNR, interference level) --- ###**2. Strengthen interception decision-making** #### 1. **Agent training framework -**Layered enhanced learning structure**： ```python class HierarchicalAgent: def __init__(self): self. meta_controller=GraphNN()#Strategic threat assessment self. Controller=Transformer-DQN()#Tactical interception decision self. executor=LSTM-PPO()#attitude control fine-tuning ``` -**Multiphysics simulation environment**： -Build a digital shooting range that includes atmospheric disturbances and electromagnetic interference -Integrated computational Fluid Mechanics (CFD) to deduce pneumatic interference in real time #### 2. **Adversarial training strategy** -**Red and blue against evolution**： -Use PPO-GAN architecture to generate progressive threat samples -Design reward function:RR = \alpha \cdot \ text {off-target quantity} + \beta \cdot \ text {energy consumption} + \gamma \cdot \ text {tactical value}$ -**Hardware-in-the-loop verification**： -Develop an FPGA-accelerated decision delay test platform -Construct a digital twin of the response characteristics of the interceptor servo --- ###**3. System engineering realization** #### 1. **Edge intelligent deployment -**Model lightweight solution**： -Use neural architecture search (NAS) to optimize the calculation diagram -Realize dynamic accuracy quantification: FP32→INT8 adaptive switching -**Distributed computing architecture**： ```mermaid graph TD A [Front-end radar node]-->/Compressed feature stream|B (edge computing unit) B-->/Global situation|C [Cloud Enhanced Learning Center] C-->/Update strategy|B ``` #### 2. **Continuous learning mechanism** -**Online incremental learning**： -Design a dual-cache memory playback system (tactical memory bank/basic skill bank) -Realize dynamic freezing of parameters during hot update of the model (feature extraction layer locking) -**Defense against samples**： -Deploy the input space randomization module (random clipping/spectrum disturbance) -Construct a decision tree interpretation layer to assist manual review --- ###**Fourth, the key breakthrough direction** 1. **Cross-domain migration ability**： -Establish a tactical maneuver knowledge map to achieve cross-scenario generalization -Develop a physically constrained neural network (PCNN) to ensure the feasibility of kinematics 2. **Man-machine collaborative interface**： -Visual decision-making traceability system (attention heat map + trajectory confidence cloud) -Bayesian belief renewal mechanism during manual intervention 3. **Energy optimization management**： -Joint optimization of fuel consumption prediction network and maneuver decision-making -Collaborative energy distribution of multiple interceptors based on reinforcement learning --- ###**5. Verification and evaluation system** 1. **Multi-dimensional test indicators**： -Tactical effectiveness: CEP50≤0.3m@10km -Real-time: delay in decision-making<50ms (99% quantile) -Robustness: maintain 85% tracking accuracy at -60dB dry letter ratio 2. **Virtual-real fusion verification**： -Build an ADAPT-2024 benchmark test set containing 200+ typical tactical scenarios -Carry out comparative experiments with "Thaad", "Aegis" and other systems --- Through a deep combination of physical models and data-driven methods, the program improves the ability to respond to new threats such as hypersonic aircraft while maintaining the interpretability of the system. The focus is on breaking through key technical bottlenecks such as rapid adaptation under small sample conditions and stable reasoning in complex electromagnetic environments.