Clustering of sound speed profiles using a parameterization technique

Vicen-Bueno, R.; Fabbri, Tommaso; Eleftherakis, Dimitrios

doi:10.1109/ieeeconf38699.2020.9389280

Cited by 3 publications

(1 citation statement)

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“…Ref. [5] presents a clustering method based on Sound Speed Profile (SSP), which is divided into two stages: linearization and clustering. In the linearization stage, the SSP is represented as a set of line segments and inflection points through piecewise linear fitting.…”

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%