The use of deep learning algorithms to enhance the autonomous search and attack capabilities of torpedoes is a complex and cutting-edge research field that involves the cross-integration of multiple disciplines. The following describes the possible ways to achieve it from the aspects of target detection and identification, path planning, decision-making and control.： -**Target detection and identification** 1. **Sonar signal processing**: The torpedo is equipped with a sonar system for detecting targets, and deep learning algorithms can be used to process sonar echo signals to identify the characteristics of the target, such as the shape, size, and material of the target. By learning the sonar data of a large number of known targets, a classification model is established to improve the accuracy of identifying different targets. 2. **Image recognition technology**: If the torpedo is equipped with optical or other types of imaging sensors, deep learning image recognition algorithms can be used to analyze the image and identify the target object. For example, identify targets such as maritime ships and submarines to provide more intuitive and accurate target information for torpedoes. -**Path planning** 1. **Environmental perception modeling**: Deep learning algorithms are used to perceive and model the marine environment in which the torpedo is located, including factors such as seabed topography, water flow, and water temperature. Through the learning of a large amount of marine environmental data, it predicts the changes in water flow and the distribution of obstacles under different environmental conditions, and provides accurate environmental information for path planning. 2. **Intelligent path search**: An enhanced learning algorithm based on deep learning allows torpedoes to search for the optimal attack path autonomously in a complex marine environment. Through continuous interaction with the environment, we learn how to avoid obstacles, use water currents and other factors, and quickly approach the target. The reinforcement learning algorithm can continuously adjust the path strategy based on the reward feedback obtained by the torpedo's actions to achieve efficient goal approach. -**Decision-making and control** 1. **Target threat assessment**: Deep learning algorithms can comprehensively analyze the type, speed, heading and other information of the target, as well as the status of the torpedo itself, to evaluate the threat level of the target. For example, it is necessary to determine whether the target is taking evasive actions, whether it has a high attack ability, etc., so as to determine the torpedo's attack priority and strategy. 2. **Adaptive control**: According to the dynamic changes of the target and environmental factors, the deep learning algorithm can adjust the navigation parameters of the torpedo in real time, such as speed, depth, heading, etc. Through continuous learning and adaptation, the torpedo can track the target more accurately and improve the success rate of the attack. -**Data fusion and training** 1. **Multi-source data fusion**: The data of various sensors on the torpedo, such as sonar, radar, optics, etc., are fused and processed. Deep learning algorithms can learn the correlation and complementary information between different sensor data, and improve the overall perception and recognition ability of the target. 2. **Simulation training**: Computer simulation technology is used to generate a large number of virtual training scenarios, allowing deep learning models to train in these scenarios. By simulating different target behaviors, marine environments, etc., the generalization ability of the model and the ability to cope with complex actual situations are improved. At the same time, the actual test data can be used to verify and optimize the model, and the autonomous search and attack performance of the torpedo can be continuously improved.