Bearing-only underwater target maneuver detection based on deep learning

Abstract

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.

Keywords

• bearing-only|maneuver detection|lstm network|movement pattern|bearing prediction