基于深度学习的纯方位水下目标机动检测  

Bearing-only underwater target maneuver detection based on deep learning

作　　者：陈建润 毛卫宁[1] CHEN Jianrun;MAO Weining(School of Information Science and Engineering,Southeast University,Nanjing 210096,China)

机构地区：[1]东南大学信息科学与工程学院,江苏南京210096

出　　处：《指挥控制与仿真》2024年第3期95-101,共7页Command Control & Simulation

摘　　要：针对现有水下目标纯方位机动检测方法存在的检测时延长、准确率低等问题,提出了基于深度学习的目标运动模式分类和方位预测两种纯方位机动检测方法。将目标处于匀速(CV)运动状态和匀转弯(CT)运动状态的方位观测作为训练数据集,通过长短时记忆(Long Short-Term Memory,LSTM)神经网络实现目标运动模式分类和方位预测,进而实现基于运动模式分类和方位预测的水下目标机动检测。仿真结果表明,相比传统方位预测的机动检测方法,该方法降低了对方位观测误差和目标机动幅度的敏感度,具有更高的机动检测准确率和更小的机动检测延迟。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.

关 键 词：纯方位 机动检测 LSTM网络 运动模式 方位预测 

分 类 号：E911[军事]