Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks

Abstract To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths.

[1]  Khashayar Khorasani,et al.  A single dynamic observer-based module for design of simultaneous fault detection, isolation and tracking control scheme , 2018, Int. J. Control.

[2]  Chu Zhenzhong.  Research on the method of fuzzy qualitative modeling for AUV , 2013 .

[3]  Shi Xiao-cheng.  Simulation of AUV Heading Control System Using Integral Variable Structure Control Principle , 2005 .

[4]  John Skvoretz,et al.  Node centrality in weighted networks: Generalizing degree and shortest paths , 2010, Soc. Networks.

[5]  Zheping Yan,et al.  Path Following Control of an AUV under the Current Using the SVR-ADRC , 2014, J. Appl. Math..

[6]  Mogens Blanke,et al.  Navigation System Fault Diagnosis for Underwater Vehicle , 2014 .

[7]  Chun-Biu Li,et al.  Multiscale complex network of protein conformational fluctuations in single-molecule time series , 2008, Proceedings of the National Academy of Sciences.

[8]  Richard Dearden,et al.  Automated Fault Diagnosis for an Autonomous Underwater Vehicle , 2013, IEEE Journal of Oceanic Engineering.

[9]  Jia Guo,et al.  Intelligent assistance positioning methodology based on modified iSAM for AUV using low-cost sensors , 2018 .

[10]  José Jaime da Cruz,et al.  AUV Control in the Diving Plane Subject to Waves , 2012 .

[11]  Pere Ridao,et al.  Intervention AUVs: The Next Challenge , 2014 .

[12]  Chao Zhao,et al.  Clinical-decision support based on medical literature: A complex network approach , 2016 .

[13]  Stephanie Rendón de la Torre,et al.  On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia , 2016 .

[14]  Julio M. Ottino,et al.  Complex networks , 2004, Encyclopedia of Big Data.

[15]  Haizhong An,et al.  The role of fluctuating modes of autocorrelation in crude oil prices , 2014 .

[16]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Qin Zhang,et al.  On intelligent risk analysis and critical decision of underwater robotic vehicle , 2017 .

[18]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[19]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Jia Guo,et al.  Shallow-sea application of an intelligent fusion module for low-cost sensors in AUV , 2018 .