ABiLSTM Based Prediction Model for AUV Trajectory

Author:

Liu Jianzeng12ORCID,Zhang Jing12ORCID,Billah Mohammad Masum12ORCID,Zhang Tianchi3ORCID

Affiliation:

1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China

2. Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, University of Jinan, Jinan 250022, China

3. School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Abstract

On 25 July 2021, the AUV of the Marine Science and Technology Research Center was lost under the sea due to a fracture of the wire rope when it was performing a mission offshore of China. A model is presented in the paper for predicting the trajectory of a lost AUV based on ABiLSTM. To increase the precision of model prediction, the model incorporates the soft attention mechanism and is based on the bidirectional Long Short-Term Memory (BiLSTM) network. In comparison to LSTM, BiLSTM, and attention-LSTM models, experiments have demonstrated that the proposed model enhanced prediction accuracy in terms of longitude, latitude, and altitude by 0.009° E, 0.008° N, and 2 m using representative root mean squared error as an assessment indicator. The findings of the study can improve marine rescue efforts and aid in the search and recovery of AUVs that have crashed.

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

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