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Two bearing-only maneuver detection methods based on deep learning are proposed to address the problems of long detection delay and low accuracy of existing bearing-only maneuver detection methods for underwater targets. The bearing observations of the target in the constant velocity (CV) motion state and constant turning (CT) motion state are used as the training data set. The target motion pattern classification and bearing prediction are realized through the Long short-term memory (LSTM) neural network, and then realize the maneuver detection of underwater targets based on motion pattern classification and bearing prediction. The simulation results show that compared with the traditional bearing prediction maneuver detection method, this method reduces the bearing observation error and has a lower sensitivity of target maneuver magnitude, and has a higher maneuver detection accuracy and reduces the maneuver detection delay.

This study prepared copper (Cu) matrix composites with different graphene contents (0∼1.0 wt-%) and an equal amount of alumina (Al2O3) whisker by vacuum hot pressing sintering. The microstructures of the Cu/C and Cu/Al2O3 interfaces were characterised. The electrochemical corrosion behaviour of the composites was evaluated to study the influence of graphene content on the corrosion performance of the Cu matrix composites. The results showed that incorporating uniformly dispersed graphene with a small amount (∼0.25 wt-%) enables close integration with the Cu matrix, slows down the corrosion rate, and increases the impedance of the composites, thus effectively improving the corrosion resistance of Cu matrix composites. With an increase in graphene content (∼1.0 wt-%), defects tend to generate, leading to the formation of multiple corrosive microcell cavities in the composite, thus accelerating the corrosion of the composite. The Gr/Cu-0.25 composite exhibited better corrosion resistance in acidic, neutral, and alkaline corrosive solutions. [ABSTRACT FROM AUTHOR]