The use of neural network technology to optimize the target tracking and interception capabilities of the anti-missile system to deal with high-mobility threats mainly starts from the two aspects of target tracking and interception capabilities. ### Target Tracking - **Data Preprocessing** - **Denoising**: The data generated by high-mobility targets on detection equipment such as radars are often accompanied by noise. Using neural network structures such as autoencoders, the network learns the distribution of data, thereby automatically extracting noise-free or low-noise data features. - **Feature Enhancement**: For the weak features of the target, a generative adversarial network (GAN) is used to generate clearer and more representative target feature samples to enhance feature recognition during subsequent tracking. - **Establish a target motion model** - **Improvement of traditional models**: Traditional target motion models such as uniform motion models and uniform acceleration motion models are difficult to adapt to high-mobility targets. The adaptive motion model based on neural networks, through recurrent neural networks (RNN) or long short-term memory networks (LSTM), predicts the future state based on the historical motion data of the target, and can better capture the complex motion laws of the target. - **Real-time model update**: As the target's motion state changes, the reinforcement learning mechanism is used. When the error between the predicted result and the actual observation value exceeds a certain threshold, the neural network automatically adjusts the model parameters to ensure that the model always accurately reflects the target's motion. - **Feature extraction and matching** - **Deep feature extraction**: Use convolutional neural networks (CNNs) to extract the target's deep features from multi-source data such as radar echoes and optical images. These features contain rich information such as the target's shape, texture, and motion pattern. - **Multi-feature fusion matching**: Fusion of features extracted by different sensors, weighted summation and other operations through the fusion layer of the neural network to obtain a more comprehensive and accurate feature representation. Then, the nearest neighbor algorithm is used to match the target in the feature space to improve the accuracy and stability of tracking. ### Interception capability- **Interception strategy optimization** - **Reinforcement learning strategy**: Build a reinforcement learning model based on deep Q network (DQN) and other reinforcement learning models to allow the anti-missile system to continuously try and error in simulated interception scenarios. According to reward signals such as the success rate of interception and interception time, learn the optimal interception strategy, such as the best interception time and interception trajectory adjustment. - **Adversarial learning strategy**: Introduce adversarial neural network to simulate the evasion strategy of highly maneuverable targets. The interception strategy of the anti-missile system will continue to evolve in the confrontation with the target, improving the effectiveness of interception. - **Interception trajectory control** - **Neural network prediction**: Use neural network to make high-precision predictions of the future position and motion trajectory of the target, and combine the dynamic model of the interception weapon to predict the flight trajectory of the interception weapon. - **Real-time adjustment**: During the interception process, according to the actual motion changes and prediction errors of the target, the attitude and trajectory of the interception weapon are adjusted in real time through the neural network control algorithm to ensure accurate hits on the target. - **Multi-weapon collaborative interception** - **Task allocation**: For scenarios where multiple interception weapons collaborate to intercept highly maneuverable targets, the neural network algorithm is used to intelligently allocate interception tasks based on factors such as weapon performance, position, and target status, so that each weapon can maximize its effectiveness. - **Collaborative control**: Use neural network to build a collaborative control model to achieve information sharing and collaborative actions among multiple interception weapons. For example, when one weapon conducts preliminary interception interference on the target, other weapons can adjust their own interception strategies based on feedback information to form an organic interception whole.