The use of neural network technology to optimize the target tracking and interception capabilities of the anti-missile system can be started from the following aspects. Especially when dealing with highly maneuverable threats, neural networks can provide the system with stronger adaptive prediction capabilities and real-time decision-making capabilities. 1. Target detection and feature extraction • Convolutional neural networks (CNNs) can be used to extract and fuse features of data from different spectra (such as radar, infrared, and optical), thereby accurately detecting small targets in complex backgrounds. • The fusion of multi-sensor data can automatically capture complementary information between different sensors with the help of neural networks, reducing the impact of target occlusion and noise interference on detection accuracy. 2. Motion trajectory prediction • The trajectory of highly maneuverable targets is usually nonlinear and highly dynamic. The introduction of recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) can learn the target's motion pattern from historical motion data, thereby predicting future trajectories. • These prediction results can assist the decision-making system to adjust the interception strategy in advance, shorten the reaction time, and thus improve the interception success rate. 3. Data Fusion and State Estimation • Combining neural networks with traditional state estimation algorithms (such as extended Kalman filters and particle filters) can form a hybrid model to achieve more accurate estimation of target states (position, velocity, acceleration, etc.). • The fault tolerance of neural networks to abnormal data and nonlinear characteristics helps maintain tracking accuracy in high-mobility environments. 4. Autonomous Decision-making and Strategy Optimization • High-mobility targets require the system to make decisions in a very short time. Methods such as deep reinforcement learning (DRL) can train agents to continuously trial and error in a simulated environment to learn the optimal interception strategy. • By adjusting the interception path and control parameters in real time, the neural network can quickly adapt to complex interference and transient conditions, improving the system response speed and interception hit rate. 5. Model Training and Simulation System • High-quality training data is particularly critical for neural networks. A large amount of high-mobility target data, including various battlefields and interference scenarios, can be generated with the help of a high-fidelity simulation platform to ensure that the model covers complex actual situations. • Continuous online learning and adaptive mechanisms enable the system to continuously improve performance in actual operations and respond to new threats and environmental changes. 6. Guarantee of real-time and robustness • In real-time tracking and interception tasks, the delay problem is crucial. Therefore, in hardware design, it is possible to consider using dedicated accelerators (such as GPU, FPGA or dedicated AI chips) to ensure that the neural network can respond in milliseconds. • At the same time, the model needs to have strong robustness and be able to continue to operate normally in the event of signal loss or abnormal data from some sensors; to this end, redundant networks can be designed or integrated learning methods can be used to improve the system's fault tolerance. In summary, the use of neural networks to optimize anti-missile systems is mainly achieved through key links such as multimodal data fusion, target feature extraction, motion prediction, and autonomous decision-making. The integration of these technologies can greatly improve the system's target tracking accuracy and interception success rate when facing highly maneuverable threats, and achieve more intelligent and efficient anti-missile operations.