A Filtered-Marine Map-Based Matching Method for Gravity-Aided Navigation of Underwater Vehicles

Wang, Bo; Ma, Zixuan; Huang, Liu; Deng, Zhihong; Fu, Mengyin

doi:10.1109/tmech.2022.3159596

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…To achieve this objective, GAINS utilizes a specially designed navigation algorithm to compare the gravimeter's measurements of gravity anomalies at the current position with the stored gravity field data to effectively correct the INS position. [4][5][6][7]. However, the variability of the gravity field significantly affects the performance of GAINS, making the selection of suitable navigation areas crucial [8,9].…”

Section: Autonomous Underwater Vehicles (Auv) Usually Use Inertial Na...mentioning

confidence: 99%

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Wang¹

2023

Preprint

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Gravity-aided inertial navigation system (GAINS) is an important development in autonomous underwater vehicle (AUV) navigation. An effective path planning algorithm plays an important role in the performance of navigation in long-term underwater missions. By combining the gravity information obtained at each position with the error information from the INS, the posterior Cram\'er-Rao bound (PCRB) of GAINS is derived in this paper. The PCRB is the estimated lower bound of position variance for navigation along the planned trajectory. And the sum of PCRB is used as the minimum cost from the initial state to the current state in the state space, and the position error prediction variance of inertial navigation system (INS) is used as the minimum estimated cost of the path from the current state to the goal state in the A* algorithm. Thus, a path planning method with optimal navigation accuracy is proposed. According to simulation results, traveling along the path planed by the proposed method can rapidly improve the positioning accuracy while consuming just slightly more distance. Even when measuring noise changes, the planned path can still maintain optimal positioning accuracy, and high positioning accuracy is possible for any trajectory located within a certain range of the planned path.

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Section: Autonomous Underwater Vehicles (Auv) Usually Use Inertial Na...mentioning

confidence: 99%

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Wang¹

2023

Preprint

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“…The Kalman filter was improved by nonlinearization to estimate the state vector and the observation vector more accurately. Reference [97] proposed a mapping matching method based on the sliding window iterative nearest contour point (ICCP) algorithm, combining ICCP with SITAN to improve the matching navigation accuracy, which reduces the longitude error by nearly 60% and improves the overall matching accuracy by 50.3%.…”

Section: ) Recursive Bayesian Estimationmentioning

confidence: 99%

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

et al. 2023

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The prerequisite for underwater vehicles to accomplish various underwater tasks is to obtain real-time position information about themselves in the underwater environment using internal or external sensors, which is known as underwater positioning and navigation technology. This paper presents a general review of underwater vehicle positioning and navigation technology and illustrates the importance of underwater terrain-aided navigation technology. It also summarizes its general structure and describes the principles and functions of each component. The paper mainly analyzes various mainstream methods applied to underwater terrain-aided navigation, focuses on terrain suitability analysis and terrain matching algorithms, which play a central role in the analysis. Additionally, the research progress of terrain-aided navigation technology is described, and the research methods of terrain suitability analysis, terrain matching navigation, and the latest technology applications are compared. Some shortcomings of the current underwater terrain-aided navigation technology are presented, and future research directions are foreseen to provide a basis for subsequent research.

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“…In contrast, the Gravity-Aided Inertial Navigation System (GAINS) is an advanced technology used for underwater navigation that enables highly accurate position estimation without emitting or receiving signals. To achieve this objective, GAINS utilizes a specially designed navigation algorithm to compare the gravimeter's measurements of gravity anomalies at the current position with the stored gravity field data to effectively correct the INS position [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Wang

2023

JMSE

Self Cite

View full text Add to dashboard Cite

Gravity-aided inertial navigation system (GAINS) is an important development in autonomous underwater vehicle (AUV) navigation. An effective path planning algorithm plays an important role in the performance of navigation in long-term underwater missions. By combining the gravity information obtained at each position with the error information from the INS, the posterior Cramér-Rao bound (PCRB) of GAINS is derived in this paper. The PCRB is the estimated lower bound of position variance for navigation along the planned trajectory. And the sum of PCRB is used as the minimum cost from the initial state to the current state in the state space, and the position error prediction variance of inertial navigation system (INS) is used as the minimum estimated cost of the path from the current state to the goal state in the A* algorithm. Thus, a path planning method with optimal navigation accuracy is proposed. According to simulation results, traveling along the path planned by the proposed method can rapidly improve the positioning accuracy while consuming just slightly more distance. Even when measuring noise changes, the planned path can still maintain optimal positioning accuracy, and high positioning accuracy is possible for any trajectory located within a certain range of the planned path.

show abstract

A Filtered-Marine Map-Based Matching Method for Gravity-Aided Navigation of Underwater Vehicles

Cited by 13 publications

References 23 publications

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

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