Research on Deep Learning Methods of UUV Maneuvering Target Tracking
Tao Dai, Hongjian Wang, Li Ruan, Haiyan Tong, Haibin Wang · Global Oceans 2020: Singapore – U.S. Gulf Coast · 2020
This paper studies a single target tracking method based on deep learning. If the target's motion model is known, then we can use appropriate filtering methods to achieve target tracking based on the target's motion state. However, when the target motion is complex and the motion model is unknown, the filtering method is difficult to apply. To this end, we have designed two single target tracking methods based on recurrent neural networks, that is, a single target tracking algorithm based on Long and Short-Term Memory Network (LSTM) and Bidirectional Long and Short-Term Memory Network (Bi-LSTM). LSTM is a variant network based on RNN, and Bi-LSTM is based on LSTM plus One hidden layer, both LSTM and Bi-LSTM networks can process data on time series. Finally, tracking training and testing are performed for UUV under different maneuvering states to verify the effectiveness of the method, and the tracking effects and accuracy of the two models are analyzed.