Journal is indexed in following databases:
- SCOPUS
- Web of Science Core Collection - Journal Citation Reports
- EBSCOhost
- Directory of Open Access Journals
- TRID Database - Transportation Research Board
- Index Copernicus Journals Master List
- BazTech
- Google Scholar
2024 Journal Impact Factor - 0.6
2024 CiteScore - 1.9
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
Processing of Heading Data with Machine Learning for MBES Survey
1 Maritime University of Szczecin, Szczecin, Poland
ABSTRACT: The paper presents research on using machine learning algorithms for heading signal smoothing recorded during MBES surveys. Several numerical methods, typically used for time series smoothing and prediction in Data Science applications are tested, like moving average, Gauss filter, Holt Winters filter and Wittaker filter. Additionally, recurrent neural networks are analyzed. Data from real use cases are used and parameters of the methods are verified. The methods are validated against smoothing performance (with variance analysis) and against original function fitting (with RMSE), allowing the qualitative and quantitative assessment. Open source python libraries are used. The results shows efficiency of such approach for this problem.
KEYWORDS: Machine Learning (ML), Heading Data Filtering, Multibeam Echosounder (MBES), Time Series Smoothing, Whittaker & Gaussian Filters, Recurrent Neural Networks, Real Survey Data Processing, Filtration Performance Metrics
REFERENCES
U. Kocamaz and I. Aydin, “Denoising GPS trajectories using support vector regression,” Journal of Navigation, vol. 72, no. 1, pp. 25–41, 2019.
F. F. Mojtahedi, N. Yousefpour, S. H. Chow, et al., “Deep Learning for Time Series Forecasting: Review and Applications in Geotechnics and Geosciences,” Archives of Computational Methods in Engineering, 2025. - doi:10.1007/s11831-025-10244-5
X. Ma, C. Chen, H. Xu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,” Transportation Research Part C: Emerging Technologies, vol. 54, pp. 187–197, 2015. - doi:10.1016/j.trc.2015.03.014
L. Ling, Y. Xie, N. Bore, and J. Folkesson, “Score-Based Multibeam Point Cloud Denoising,” arXiv preprint arXiv:2409.13143, 2024. - doi:10.1109/AUV61864.2024.11030792
R. Snijder and H.-J. Lekkerkerk, “MBES feature detection using machine learning,” Hydro International, Sep. 23, 2022.
A. Minelli, A. N. Tassetti, B. Hutton, G. N. Pezzuti Cozzolino, T. Jarvis, and G. Fabi, “Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder,” Sensors, vol. 21, no. 9, p. 2999, Apr. 2021. - doi:10.3390/s21092999
Y. Fu, X. Luo, X. Qin, H. Wan, J. Cui, and Z. Huang, “Deep Learning-Based Feature Matching Algorithm for Multi-Beam and Side-Scan Sonar Images,” Remote Sensing, vol. 17, no. 4, p. 675, Feb. 2025. - doi:10.3390/rs17040675
D. J. Hughes, M. Patel, and T. Nguyen, “Comparison of Manual Editing and Automated Outlier Detection in MBES Data,” Ocean Engineering, vol. 278, 2023.
J. J. Williams, M. Patel, and T. Nguyen, “Using 3D CNNs for Denoising Multibeam Echosounder Point Cloud Data,” Methods in Oceanography, vol. 36, 2020.
S. Williams, L. Chen, and Y. Zhao, “Denoising MBES Point Clouds Using 3D Convolutional Neural Networks,” 2023.
J. P. Gauchia, et al., “A review of data cleaning approaches in a hydrographic framework,” Geosciences, vol. 10, no. 7, p. 254, 2020. - doi:10.3390/geosciences10070254
Q. Bai, A. Amiri-Simkooei, S. Mestdagh, D. G. Simons, and M. Snellen, “Mussel culture monitoring with semi-supervised machine learning on multibeam echosounder data using label spreading,” Journal of Environmental Management, vol. 369, p. 122250, Oct. 2024. - doi:10.1016/j.jenvman.2024.122250
R. V. Garone, et al., “Seabed classification of multibeam echosounder data into bedrock/non-bedrock using deep learning,” Frontiers in Earth Science, 2023. - doi:10.3389/feart.2023.1285368
N. Dahan and I. Klein, “GHNet: Learning GNSS Heading from Velocity Measurements,” arXiv preprint arXiv:2309.09696, 2023. - doi:10.1109/JSEN.2023.3348692
D. Engelsman and I. Klein, “Towards Learning-Based Gyrocompassing,” arXiv preprint arXiv:2312.12121, 2023.
M. Łącki, “Speciation of Population in Neuroevolutionary Ship Handling,” TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 14, no. 2, pp. 223–228, Jun. 2020.
S. Thompson, H. Li, and M. Garcia, “Assessment of preprocessing strategies for echosounder data in machine learning applications,” 2023.
S. M. Thompson, et al., “Evaluation of echosounder data preparation strategies for modern machine learning applications in fisheries acoustics,” Fisheries Research, vol. 243, p. 106088, 2022.
L. Ling, J. Zhang, N. Bore, J. Folkesson, and A. Wåhlin, “Benchmarking classical and learning-based multibeam point cloud registration methods,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 2024. - doi:10.1109/ICRA57147.2024.10610118
S. W. Smith, The Scientist and Engineer’s Guide to Digital Signal Processing. San Diego, CA, USA: California Technical Publishing, 1997.
F. Planchet and O. Durand, “Part 5: Smoothing and fitting methods,” Duration Models – Lecture Notes, Univ. Lyon, 2016.
R. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Physica D: Nonlinear Phenomena, vol. 404, art. 132306, 2020. - doi:10.1016/j.physd.2019.132306
P. Żerdziński, D. Połap, N. Wawrzyniak, G. Zaniewicz, and A. Zielonka, “Attention-Enhanced Temporal LSTM Network as a Sonar Position Predictor for Side-Scanning Sonar Waterfall Image,” IEEE Access, vol. 13, pp. 148792–148802, 2025, doi: 10.1109/ACCESS.2025.3601188. - doi:10.1109/ACCESS.2025.3601188
M. Włodarczyk-Sielicka, D. Połap, K. Prokop, K. Połap, and A. Stateczny, “Spatial Visualization Based on Geodata Fusion Using an Autonomous Unmanned Vessel,” Remote Sensing, vol. 15, no. 7, p. 1763, 2023, doi: 10.3390/rs15071763. - doi:10.3390/rs15071763
Citation note:
Kazimierski W., Włodarczyk-Sielicka M.: Processing of Heading Data with Machine Learning for MBES Survey. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 19, No. 3, doi:10.12716/1001.19.03.22, pp. 879-885, 2025
Authors in other databases:
24829115600
X7m2hjsAAAAJOther publications of authors:
File downloaded 3 times
