Implementation of Modified Ensemble and Unscented Kalman Filters for Diving Trajectory Estimation of Remote Operated Vehicles
Corresponding author: Rachman Sinatriya Marjianto
Abstract
Remotely Operated Vehicles (ROVs) are underwater vehicle widely researched, developed, and utilized to perform various tasks. The primary functions of ROVs include coral reef exploration, oil refinery inspection, underwater monitoring, and sea accident rescue operations. The ROV typically has six Degrees of Freedom (DoFs), consisting of a combination of translational and rotational motions. To ensure that the ROV moves according to a predetermined trajectory without unwanted rotation or roll, a reliable navigation and position estimation system is required. Several position estimation methods have been proven effective for ROV applications. Among these, the Ensemble Kalman Filter (EnKF) and the Unscented Kalman Filter (UKF) are recognized for their reliability. In this study, a modified version of the EnKF, known as the Square Root Ensemble Kalman Filter (SR-EnKF), is compared with the UKF method. Simulation results indicate that the SR-EnKF provides higher accuracy than the UKF, achieving approximately 99.8% accuracy compared to 99.6% obtained with the UKF.
Keywords:
estimation, ROV, SR-EnKF, Trajectory, UKFDownloads
References
D. Zhang, B. Zhao, Y. Zhang, and N. Zhou, "Numerical simulation of hydrodynamics of ocean-observation-used remotely operated vehicle," Frontiers in Marine Science, vol. 11, Apr. 2024. DOI: https://doi.org/10.3389/fmars.2024.1357144
R. Śmierzchalski and M. Kapczyński, "Autonomous Control of the Underwater Remotely Operated Vehicle in Collision Situation with Stationary Obstacle," Polish Maritime Research, vol. 29, no. 4, pp. 45–55, Dec. 2022. DOI: https://doi.org/10.2478/pomr-2022-0043
X.-R. Huang and L.-B. Chen, "An underwater explorer remotely operated vehicle: Unraveling the secrets of the ocean," IEEE Potentials, vol. 42, no. 3, pp. 31–36, Feb. 2023. DOI: https://doi.org/10.1109/MPOT.2022.3233713
Y. Cao, B. Li, Q. Li, A. A. Stokes, D. M. Ingram, and A. Kiprakis, "A nonlinear model predictive controller for remotely operated underwater vehicles with disturbance rejection," IEEE Access, vol. 8, pp. 158622–158634, Jan. 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3020530
K. L. Walker et al., "Experimental Validation of Wave Induced Disturbances for Predictive Station Keeping of a Remotely Operated Vehicle," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5421–5428, Jul. 2021. DOI: https://doi.org/10.1109/LRA.2021.3075662
M. I. Fadhok, B. Pramujati, and H. Nurhadi, "Design of sliding mode control for maneuver autonomous surface vehicle using genetic algorithm," AIP Conference Proceedings, vol. 2927, no. 1, Mar. 2024, Art. no. 040009. DOI: https://doi.org/10.1063/5.0196208
K. Hasan et al., "Oceanic Challenges to Technological Solutions: A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing," IEEE Access, vol. 12, pp. 46202–46231, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3380458
J. Neira, C. Sequeiros, R. Huamani, E. Machaca, P. Fonseca, and W. Nina, "Review on unmanned underwater robotics, structure designs, materials, sensors, actuators, and navigation control," Journal of Robotics, vol. 2021, pp. 1–26, Jul. 2021. DOI: https://doi.org/10.1155/2021/5542920
A. Topini and A. Ridolfi, "Probabilistic Particle Filter Anchoring (PPFA): A Novel Perspective in Semantic World Modeling for Autonomous Underwater Vehicles With Acoustic and Optical Exteroceptive Sensors," IEEE Journal of Oceanic Engineering, vol. 50, no. 2, pp. 1065–1086, Apr. 2025. DOI: https://doi.org/10.1109/JOE.2024.3492537
M. E. Hanbaly, S. Iacoponi, A. Infanti, C. Stefanini, G. de Masi, and F. Renda, "Inverse Dynamics Modeling as a State Estimation Aid for Underwater Robots Navigation," IEEE Access, vol. 12, pp. 159214–159225, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3487495
H. Nurhadi, T. Herlambang, and D. Adzkiya, "Trajectory Estimation of Autonomous Surface Vehicle using Square Root Ensemble Kalman Filter," in 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), Batu, Indonesia, Jul. 2019, pp. 325–328. DOI: https://doi.org/10.1109/ICAMIMIA47173.2019.9223354
T. Herlambang, D. Rahmalia, A. Suryowinoto, F. Yudianto, F. A. Susanto, and M. Y. Anshori, "H-infinity for autonomous surface vehicle position estimation," Journal of Physics Conference Series, vol. 2157, no. 1, Jan. 2022, Art. no. 012022. DOI: https://doi.org/10.1088/1742-6596/2157/1/012022
P. Li et al., "An efficient PSO-based AUV global path planning method incorporating with the positioning uncertainty-driven cost functions of both collision and terrain complexity," Ocean Engineering, vol. 336, Sep. 2025, Art. no. 121695. DOI: https://doi.org/10.1016/j.oceaneng.2025.121695
W. Liu et al., "Underwater remotely operated vehicle control system with optimized PID based on improved particle swarm optimization," Desalination and Water Treatment, vol. 314, pp. 322–329, Dec. 2023. DOI: https://doi.org/10.5004/dwt.2023.30037
F. F. Sørensen, M. Von Benzon, S. Pedersen, J. Liniger, K. Schmidt, and S. Klemmensen, "Experimental filter comparison of an acoustic positioning system for unmanned underwater navigation," IFAC-PapersOnLine, vol. 55, no. 36, pp. 25–30, Jan. 2022. DOI: https://doi.org/10.1016/j.ifacol.2022.11.328
Y. Muratoglu and A. Alkaya, "Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles," Engineering Technology & Applied Science Research, vol. 9, no. 6, pp. 4876–4882, Dec. 2019. DOI: https://doi.org/10.48084/etasr.3111
T. A. Eldamaty and M. M. Helal, "Optimizing GPS kinematic accuracy by employing the Kalman Filter technique, case study: Mecca - Medina Highway," Engineering Technology & Applied Science Research, vol. 15, no. 3, pp. 22301–22305, Jun. 2025. DOI: https://doi.org/10.48084/etasr.10528
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Copyright (c) 2025 Teguh Herlambang, Rachman Sinatriya Marjianto; Puguh Triwinanto; Zuraini Othman, Mohd. Sanusi Azmi, Nuzulha Khilwani Ibrahim, Nor Mas Aina Md. Bohari; Mohammad Soleh
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