A Conceptual Architecture for Resilient UUV Navigation in Long Baseline (LBL) and Ultra-Short Baseline (USBL) Degraded Environments

Dharani Jaganathan

Abstract


Underwater navigation for Unmanned Underwater Vehicles (UUVs) is challenged by the absence of GPS signals and the variable reliability of acoustic positioning systems such as Long-Baseline (LBL) and Ultra-Short Baseline (USBL). This paper proposes a resilient navigation architecture that integrates inertial navigation systems (INS), Doppler velocity logs (DVL), acoustic ranging, and terrain-relative navigation into a hierarchical fusion framework. The architecture introduces “Sensor Trust Modulation,” dynamically adjusting the confidence in each sensor based on environmental conditions and signal quality. A fault detection and isolation (FDI) layer is incorporated to identify sensor anomalies and mitigate their impact on state estimation. Theoretical foundations for observability analysis in multi-sensor fusion systems are presented, emphasizing the importance of persistent excitation and redundancy. This conceptual framework aims to maintain accurate and robust navigation in challenging underwater environments, such as cluttered seabeds or noisy acoustic channels, enabling reliable autonomous operation for scientific, commercial, and defense applications.

References


Batista, Pedro, Carlos Silvestre, and Paulo Oliveira. “Sensor-based Long Baseline Navigation: Observability Analysis and Filter Design”. Asian Journal of Control 16, no. 4 (2014): 974–94. https://doi.org/10.1002/asjc.778.

Hegrenaes, Øyvind, and Oddvar Hallingstad. “Model-Aided INS With Sea Current Estimation for Robust Underwater Navigation”. IEEE Journal of Oceanic Engineering 36, no. 2 (2011): 316–37. https://doi.org/10.1109/JOE.2010.2100470.

Meduna, Deborah K., Stephen M. Rock, and Robert S. McEwen. “Closed-Loop Terrain Relative Navigation for AUVs with Non-Inertial Grade Navigation Sensors”. In 2010 IEEE/OES Autonomous Underwater Vehicles, 1–8, 2010. https://doi.org/10.1109/AUV.2010.5779659.

Miller, Paul A., Jay A. Farrell, Yuanyuan Zhao, and Vladimir Djapic. “Autonomous Underwater Vehicle Navigation”. IEEE Journal of Oceanic Engineering 35, no. 3 (2010): 663–78. https://doi.org/10.1109/JOE.2010.2052691.

Morgado, Marco, Pedro Batista, Paulo Oliveira, and Carlos Silvestre. “Position USBL/DVL Sensor-Based Navigation Filter in the Presence of Unknown Ocean Currents”. Automatica 47, no. 12 (2011): 2604–14. https://doi.org/10.1016/j.automatica.2011.09.024.

Paull, Liam, Sajad Saeedi, Mae Seto, and Howard Li. “AUV Navigation and Localization: A Review”. IEEE Journal of Oceanic Engineering 39, no. 1 (2014): 131–49. https://doi.org/10.1109/JOE.2013.2278891.

Reis, Joel, Marco Morgado, Pedro Batista, Paulo Oliveira, and Carlos Silvestre. “Design and Experimental Validation of A USBL Underwater Acoustic Positioning System”. Sensors 16, no. 9 (2016). https://doi.org/10.3390/s16091491.

Sun, Yu-Shan, Yue-Ming Li, Guo-Cheng Zhang, Ying-Hao Zhang, and Hai-Bo Wu. “Actuator Fault Diagnosis of Autonomous Underwater Vehicle Based on Improved Elman Neural Network”. Journal of Central South University 23, no. 4 (1 April 2016): 808–16. https://doi.org/10.1007/s11771-016-3127-8.

Teixeira, Francisco Curado, João Quintas, and António Pascoal. “AUV Terrain-aided Navigation using A Doppler Velocity Logger”. Annual Reviews in Control 42 (2016): 166–76. https://doi.org/10.1016/j.arcontrol.2016.10.002.

Zhang, Tao, Hongfei Shi, Liping Chen, Yao Li, and Jinwu Tong. “AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation”. Sensors 16, no. 3 (2016). https://doi.org/10.3390/s16030357.

Zhang, Tao, Liping Chen, and Yao Li. “AUV Underwater Positioning Algorithm Based on Interactive Assistance of SINS and LBL”. Sensors 16, no. 1 (2016). https://doi.org/10.3390/s16010042.

Zhu, Daqi, and Bing Sun. “Information Fusion Fault Diagnosis Method for Unmanned Underwater Vehicle Thrusters”. IET Electrical Systems in Transportation 3, no. 4 (2013): 102–11. https://doi.org/10.1049/iet-est.2012.0052.


Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.