<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper focuses on enhancing target tracking techniques for unmanned underwater vehicles (UUVs) used for deep-sea exploration, especially for the latest advances in digital technology. The article begins with an innovative UUV target tracking algorithm improvement based on the correlation filtering algorithm. Introducing the spatio-temporal regularization method and penalty function optimizes the filter parameters and enhances the algorithm’s performance. Then, a new objective function is constructed by combining the optimal feature weights and baseline function calculated by the dynamic feature weighting method, which effectively excludes the influence of interference peaks in the algorithm. In addition, by applying the Gaussian correlation method, the article further improves the algorithm’s robustness. When the obstacle avoidance ability of the algorithm is analyzed, it is found that the control error of the obstacle avoidance speed in both X and Y directions is less than 0.1 m/s, and the difference between the actual angle and the desired angle of the attitude angle is less than 0.5 degrees. This indicates that the algorithm can effectively avoid all obstacles and accurately track the target. The target tracking experiments show that the tracking performance scores of the algorithm are generally higher than 9, and the tracking accuracy is more than 95%, which is significantly better than other existing algorithms. The optimized tracking algorithm proposed in this paper not only provides essential technical support for the development of unmanned underwater vehicle technology, but also provides valuable reference for the future application and practice of the algorithm.</p>
</abstract>

This paper focuses on enhancing target tracking techniques for unmanned underwater vehicles (UUVs) used for deep-sea exploration, especially for the latest advances in digital technology. The article begins with an innovative UUV target tracking algorithm improvement based on the correlation filtering algorithm. Introducing the spatio-temporal regularization method and penalty function optimizes the filter parameters and enhances the algorithm’s performance. Then, a new objective function is constructed by combining the optimal feature weights and baseline function calculated by the dynamic feature weighting method, which effectively excludes the influence of interference peaks in the algorithm. In addition, by applying the Gaussian correlation method, the article further improves the algorithm’s robustness. When the obstacle avoidance ability of the algorithm is analyzed, it is found that the control error of the obstacle avoidance speed in both X and Y directions is less than 0.1 m/s, and the difference between the actual angle and the desired angle of the attitude angle is less than 0.5 degrees. This indicates that the algorithm can effectively avoid all obstacles and accurately track the target. The target tracking experiments show that the tracking performance scores of the algorithm are generally higher than 9, and the tracking accuracy is more than 95%, which is significantly better than other existing algorithms. The optimized tracking algorithm proposed in this paper not only provides essential technical support for the development of unmanned underwater vehicle technology, but also provides valuable reference for the future application and practice of the algorithm.

Modeling and tracking control of underwater unmanned underwater vehicles for deep-sea marine environment detection

Key Laboratory of Instrumentation Science & Dynamic Measurement of Ministry of Education

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper focuses on enhancing target tracking techniques for unmanned underwater vehicles (UUVs) used for deep-sea exploration, especially for the latest...

Modeling and tracking control of underwater unmanned underwater vehicles for deep-sea marine environment detection

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 22 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0583

Palabras clave
Correlation filtering method, Spatio-temporal regularization, Dynamic feature weighting, Gaussian correlation method, Target tracking

© 2024 Qiusheng Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modeling and tracking control of underwater unmanned underwater vehicles for deep-sea marine environment detection

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 22 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0583

Palabras claveCorrelation filtering method, Spatio-temporal regularization, Dynamic feature weighting, Gaussian correlation method, Target tracking

© 2024 Qiusheng Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Correlation filtering method, Spatio-temporal regularization, Dynamic feature weighting, Gaussian correlation method, Target tracking