Research on Deep Learning Method of Underwater Weak Target Tracking

doi:10.12382/bgxb.2022.0842

Abstract

Abstract:

The tracking error of the traditional Kalman method is significantly increased or even divergent due to the influence of interference and noise. A neural network-based target tracking method is proposed. The proposed method uses the deep neural network to solve the nonlinear change of target azimuth with time in different motion modes. The neural network model of underwater target tracking can generate a large number of measurement data through the motion model for full training, which effectively solves the problems of insufficient underwater acoustic target data and insufficient labeled samples. A new loss function is proposed to enhance the robustness of target tracking model under the condition of measurement discontinuity. The unlearned simulation data and measured sea trial data were tested. The results show that the convolutional neural network (CNN) is applicable for a target in 3 different motion modes,and can stably track targets when the platform is stationary or moving. Compared with the traditional Kalman filtering method, the tracking error of the neural network model is reduced by 7.75° and 1.41°, respectively, for the unlearned simulation data and the measured sea trial data, which verifies the robustness and scalability of the model.

Key words: underwater target tracking, deep learning, weak target tracking, bearings-only tracking

CLC Number:

TB566

YANG Jiaming, PAN Yue, WANG Qiang, CAO Huaigang, GAO Sunpei. Research on Deep Learning Method of Underwater Weak Target Tracking[J]. Acta Armamentarii, 2024, 45(2): 385-394.

Figures/Tables 18

References 25

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运动模式	参数	数值
	目标与观测平台距离/km	0.05~10
CV	目标初始方位角/(°)	0~360
	目标速度/(m·s^-1)	0.1~10
	目标速度方向/(°)	0~360
	目标与观测平台距离/km	0.05~10
	目标初始方位角/(°)	0~360
CA	目标速度/(m·s^-1)	0.1~10
	目标速度方向/(°)	0~360
	目标加速度/(m·s^-2)	0.1~2
	目标与观测平台距离/km	0.05~10
	目标初始方位角/(°)	0~360
CV+CT	目标速度/(m·s^-1)	0.1~10
	目标速度方向/(°)	0~360
	目标角速度/((°)·s^-1)	-2~2

运动模式	参数	数值
	目标与观测平台距离/km	0.05~10
CV	目标初始方位角/(°)	0~360
	目标速度/(m·s^-1)	0.1~10
	目标速度方向/(°)	0~360
	目标与观测平台距离/km	0.05~10
	目标初始方位角/(°)	0~360
CA	目标速度/(m·s^-1)	0.1~10
	目标速度方向/(°)	0~360
	目标加速度/(m·s^-2)	0.1~2
	目标与观测平台距离/km	0.05~10
	目标初始方位角/(°)	0~360
CV+CT	目标速度/(m·s^-1)	0.1~10
	目标速度方向/(°)	0~360
	目标角速度/((°)·s^-1)	-2~2

事件	概率/%
目标被探测	97.82
出现野值	2.31

事件	概率/%
目标被探测	97.82
出现野值	2.31

网络层	卷积核	节点数	输出维数	参数数量
	512×2		199×512	1536
卷积层	256×2		198×256	262400
	256×2		197×256	131328
	128×2		196×128	65664
最大值池化层	128×2		98×128	0
		200	1×200	2509000
全连接层		1000	1×1000	201000
		200	1×200	200200