A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

In general, the strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)-integrated navigation method can provide continuous and accurate navigation information for autonomous underwater vehicles (AUV). This SINS/DVL fusion is the loosely integrated method, in which DVL may contain large error or does not work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this article, a novel tightly integrated navigation method composed of an SINS, a DVL, and a pressure sensor (PS) is proposed, in which beam measurements are used without transforming them to 3-D velocity. The simulation and vehicle test show that the proposed method can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for a complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%.

[1]  Shun Min Wang,et al.  Vertical obstacle avoidance and navigation of autonomous underwater vehicles with H∞ controller and the artificial potential field method , 2019 .

[2]  Lubin Chang,et al.  Initial Alignment by Attitude Estimation for Strapdown Inertial Navigation Systems , 2015, IEEE Transactions on Instrumentation and Measurement.

[3]  Sajad Saeedi,et al.  AUV Navigation and Localization: A Review , 2014, IEEE Journal of Oceanic Engineering.

[4]  Yiqing Yao,et al.  An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System , 2017, Sensors.

[5]  Itzik Klein,et al.  Inertial Navigation System/Doppler Velocity Log (INS/DVL) Fusion with Partial DVL Measurements , 2017, Sensors.

[6]  Wei Gao,et al.  Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems , 2014, Journal of Navigation.

[7]  Li Ning,et al.  A Tightly Integrated SINS/DVL Navigation Method for Autonomous Underwater Vehicle , 2013, 2013 International Conference on Computational and Information Sciences.

[8]  Sven G. Bilén,et al.  Quantifying Long-Term Accuracy of Sonar Doppler Velocity Logs , 2018, IEEE Journal of Oceanic Engineering.

[9]  Yan Wei-sheng Cooperative Localization for Multi-UUVs Based on Time-of-flight of Acoustic Signal , 2009 .

[10]  M. Karimi,et al.  A comparison of DVL/INS fusion by UKF and EKF to localize an autonomous underwater vehicle , 2013, 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM).

[11]  Xiaosu Xu,et al.  A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System , 2015 .

[12]  O Hegrenaes,et al.  Model-Aided INS With Sea Current Estimation for Robust Underwater Navigation , 2011, IEEE Journal of Oceanic Engineering.

[13]  Lei Wang,et al.  A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression , 2016, Journal of Navigation.

[14]  Hongji Xu,et al.  A Robust Single GPS Navigation and Positioning Algorithm Based on Strong Tracking Filtering , 2018, IEEE Sensors Journal.

[15]  Zhen Guo,et al.  Research on integrated navigation method for AUV , 2005 .

[16]  Baiqing Hu,et al.  Robust Initial Attitude Alignment for SINS/DVL , 2018, IEEE/ASME Transactions on Mechatronics.

[17]  Hailiang Xiong,et al.  Robust GPS/INS/DVL Navigation and Positioning Method Using Adaptive Federated Strong Tracking Filter Based on Weighted Least Square Principle , 2019, IEEE Access.

[18]  Lin Zhao,et al.  A Novel Grid SINS/DVL Integrated Navigation Algorithm for Marine Application , 2018, Sensors.

[19]  Yan Guo,et al.  Square-Root Unscented Information Filter and Its Application in SINS/DVL Integrated Navigation , 2018, Sensors.

[20]  Ling Zhou,et al.  A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems , 2017 .

[21]  Lin Zhao,et al.  Research on Wavelet Singularity Detection Based Fault-Tolerant Federated Filtering Algorithm for INS/GPS/DVL Integrated Navigation System , 2014, J. Appl. Math..

[22]  Narjes Davari,et al.  An Asynchronous Adaptive Direct Kalman Filter Algorithm to Improve Underwater Navigation System Performance , 2017, IEEE Sensors Journal.

[23]  Fangjun Qin,et al.  A Novel Autonomous Initial Alignment Method for Strapdown Inertial Navigation System , 2017, IEEE Transactions on Instrumentation and Measurement.