Classifying motion states of AUV based on graph representation for multivariate time series

Motion state monitoring and recognition are the important issues to be dealt to improve the reliability of Autonomous Underwater Vehicle (AUV). In this work, we transform the motion state classification into Multi-variate Time Series Classification (MTSC). By combining two kinds of MTSC methods, including the methods based on feature representation transformation and Deep Neural Network (DNN), we propose a new classifi-cation method for Multivariate Time Series (MTS). Multivariate monitoring data of AUV are fused to construct complex networks as graphs to represent the motion states. Then, Graph Convolutional Neural Network (GCNN) is used to extract the features of the graphs and classify the graphs. The effectiveness of our method is validated through sea experiments, whose data are from three classes of navigational motions: near the surface, at fixed depth, and influenced by unknown ocean currents at fixed depth. The experimental results show that the graphical representation based on complex networks can effectively describe the motion states. Compared with Support Vector Machine (SVM), the graphical features are extracted automatically by GCNN to get a higher accuracy of classification of the motion states. The experiments also show that the classification accuracy of our method is higher than that of other two DNNs.

Automated summary

Motion state monitoring and recognition are crucial for improving the reliability of Autonomous Underwater Vehicles (AUVs). This work proposes a novel method for classifying the motion states of AUVs by transforming the problem into Multi-variate Time Series Classification (MTSC). By combining feature representation transformation and Deep Neural Network (DNN), the proposed method utilizes Graph Convolutional Neural Network (GCNN) to extract the features of complex networks representing the motion states and achieve higher classification accuracy compared to Support Vector Machines (SVM) and other DNNs.