Comparative Analysis of Underwater Positioning and Navigation Systems

Alamleh, Hosam; AlQahtani, Ali Abdullah S.; Smadi, Baker Al

doi:10.1109/uemcon53757.2021.9666718

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Ref. [17] conducts a comprehensive analysis and comparison of underwater positioning and navigation systems. It initially introduces various types of underwater positioning and navigation systems, including acoustic, multi-sensor, GPS buoys, vision-based, SLAM, and cooperative systems.…”

Section: Introductionmentioning

confidence: 99%

A Sound Velocity Profile Stratification Method Based on Maximum Density and Maximum Distance Clustering

Li,

Pan,

et al. 2023

Applied Sciences

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In the field of deep-sea positioning, this paper aims to enhance accuracy and computational efficiency in positioning calculations. We propose an improved method based on layered clustering of sound velocity profiles, where the profiles are stratified according to maximum distance and maximum density. Subsequently, a secondary curve fitting is applied to the stratified data. Ultimately, the underwater positioning is conducted using the sound velocity profiles’ post-layered fitting. We compare our approach with traditional methods such as k-means clustering, layered clustering, and gradient-based stratification. Experimental results demonstrate that, in the application scenario of a USBL system with a transducer tilted at 30°, and under the premise of autonomously controlling the number of layers, our method significantly improves positioning accuracy.

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Section: Introductionmentioning

confidence: 99%

A Sound Velocity Profile Stratification Method Based on Maximum Density and Maximum Distance Clustering

Li,

Pan,

et al. 2023

Applied Sciences

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“…To realize mapping based on the environment sensor, a proper description of the environment is needed. The main presentation includes a feature map, a topology map, and a grid map [14,15]. Among them, The description of the feature map is the most intuitive, and it can better describe the appearance information of the observed features [16,17].…”

Section: Introductionmentioning

confidence: 99%

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Guan

Zhou

et al. 2022

JMSE

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Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on forward-looking sonar for AUV. The algorithm uses an extended Kalman filter (EKF) to estimate the AUV motion states. First, the SLAM method fuses the data coming from the navigation sensors to predict the motion states. Subsequently, a novel particle swarm optimization genetic algorithm (PSO-GA) scan matching method is employed for matching the sonar scan data and grid map, and the matching pose would be used to correct the prediction states. Lastly, the estimated motion states and sonar scan data would be used to update the grid map. The experimental results based on the field data have validated that the proposed SLAM algorithm is adaptable to underwater conditions, and accurate enough to use for ocean engineering practical applications.

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Positioning of Autonomous Underwater Vehicles using Machine Learning Techniques

Carrera,

Soria

2023

2023 IEEE Seventh Ecuador Technical Chapters Meeting (ECTM)

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Comparative Analysis of Underwater Positioning and Navigation Systems

Cited by 4 publications

References 30 publications

A Sound Velocity Profile Stratification Method Based on Maximum Density and Maximum Distance Clustering

A Sound Velocity Profile Stratification Method Based on Maximum Density and Maximum Distance Clustering

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Positioning of Autonomous Underwater Vehicles using Machine Learning Techniques

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