Comparison of AUV Position Estimation Using Kalman Filter, Ensemble Kalman Filter and Fuzzy Kalman Filter Algorithm in the Specified Trajectories

Ngatini Ngatini, Erna Apriliani, Hendro Nurhadi


This research explains a comparison estimation for AUV position using Kalman Filter (KF), Ensemble Kalman Filter (EnKF), and Fuzzy Kalman Filter (FKF) algorithm in some specified trajectories. Estimation is developed for AUV Segorogeni ITS which was built by the Institute Technology of Sepuluh Nopember (ITS), Indonesia. The specified trajectories are the diving, straight, and turning path which is the real trajectories. We compare the result estimation for each of the trajectories from the simulation and the RMSE (Root Mean Square Error). In this case, the best estimation is given by the difference estimation method. Fuzzy Kalman Filter gives the best result for the diving trajectory (Y-position and angle) and the straight trajectory. Ensemble Kalman Filter (EnKF) gives the best result for the X-position in the diving trajectory. While Kalman Filter gives the best result for the straight trajectory.

Keywords: AUV; Kalman Filter (KF); Ensemble Kalman Filter (EnKF); Fuzzy Kalman Filter (FKF); AUV Segorogeni ITS.



Penelitian ini menjelaskan tentang perbandingan estimasi untuk posisi AUV antara algoritma Kalman Filter (KF)Ensemble Kalman Filter (EnKF) dan Fuzzy Kalman Filter (FKF) untuk trayektori tertentu. Estimasi dilakukan terhadap AUV Segorogeni ITS yang dibuat oleh ITS (Institut Teknologi Sepuluh Nopember), Indonesia. Trayektori yang diberikan adalah menyelam, lurus dan lintasan membelok yang merupakan lintasan real. Peneliti melakukan perbandingan untuk setiap lintasan berdasarkan hasil simulasi dan Root Mean Square Error (RMSE). Pada kasus ini estimasi terbaik diberikan oleh metode yang berbeda. Fuzzy Kalman Filter memberikan hasil terbaik untuk lintasan berbelok pada posisi-Y dan pada garis lurus. Ensemble Kalman Filter memberikan estimasi terbaik untuk posisi-X pada lintasan menyelam. Sedangkan Kalman Filter memberikan hasil terbaik untuk lintasan lurus.

Kata kunci: AUV; Kalman Filter (KF); Ensemble Kalman Filter (EnKF); Fuzzy Kalman Filter (FKF); AUV Segorogeni ITS.


AUV; Kalman Filter (KF); Ensemble Kalman Filter (EnKF); Fuzzy Kalman Filter (FKF); AUV Segorogeni ITS


J. M. Lewis, S. Laksmivarahan, and S. Dhall, Dynamic Data Assimilation, A Least Square Approach. New York: Press Taylor and Francis Group, 2006.

E. Apriliani, D. K. Arif, and B. A. Sanjoyo, “The Square Root Ensemble Kalman Filter to Estimate the Concentration of Air Pollution,” IEEE, International Conference on Mathematical Application in Engineering (ICMAE'10), Kuala Lumpur, Malaysia 2010.

Ngatini and H. Nurhadi, “Estimasi Lintasan AUV 3 Dimensi (3D) Dengan Ensemble Kalman Filter,” vol. 4, no. 1, 2019.

Ngatini, E. Apriliani, and H. Nurhadi, “Ensemble and Fuzzy Kalman Filter for Position Estimation of an Autonomous Underwater Vehicle Based on Dynamical System of AUV Motion,” Expert Systems With Applications, vol. 68, no. 7, pp. 29–35, 2017.

J. Yuh, “Learning Control for Underwater Robotic Vehicles,” IEEE Control Systems Magazine, vol. 14, no. 2, pp. 39–46, 1994.

C. von Alt, Autonomous Underwater Vehicles. The Autonomous Underwater Lagrangian Platforms and Sensors Workshop. United States: Woods Hole Oceanographic Institution, 2003.

B. Allota, R. Costanzi, F. Fanelli, N. Monni, L. Paolucci, and A. Ridolfi, “Sea Currents Estimating During AUV Navigation Using Unscented Kalman Filter,” IFAC (International Federation of Automatic Control), vol. 50, no. 1, pp. 13668–13673, 2017.

C. Yang, “Modular Modeling and Control for Autonomous Underwater Vehicle (AUV),” Singapore, 2007.

T. I. Fossen, Guidance and Control of Ocean Vehicles. Chichester, England: John Wiley and Sons Ltd., 1994.

M. Ataei and A. Y. Koma, “Three-Dimensional Optimal Path Planning for Waypoint Guidance of an Autonomous Underwater Vehicle,” Robotics and Autonomous Systems Vol. 67, 2014.

T. Herlambang, E. B. Djatmiko, and H. Nurhadi, “Ensemble Kalman Filter with a Square Root Scheme (EnKF-SR) for Trajectory Estimation of AUV SEGOROGENI ITS,” Journal of International Review of Mechanical Engineering, vol. 9, no. 6, pp. 553–560, 2015.

Z. Ermayanti, E. Apriliani, H. Nurhadi, and T. Herlambang, “Estimate and Control Position on The Autonomous Underwater Vehicle Based on Determined Trajectory using fuzzy Kalman filter method,” IEEE-International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), Surabaya, Indonesia, 2015.

F. L. Lewis, L. Xie, and D. Popa, Optimal and Robust EstimationWith an Introduction to Stochastic Control Theory, 2nd Edition. CRC Press, 2017.

A. Hommels, “Comparison of the Ensemble Kalman Filter with the Unscented Kalman Filter: Application to the Construction of a Road Embankment,” 2008.

Subiono, Sistem Linear dan Kontrol Optimal 2.1.1. Surabaya, Indonesia: Department of Mathematics, Institut Teknologi Sepuluh Nopember, 2013.

L. B. Burden and J. D. Faires, Numerical analysis, Ninth edition. Brooks/Cole Cengage Learning. Youngstown State University, United State of America, 2010.

G. Evensen, “The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation,” Ocean Dynamics, vol. 53, no. 4, pp. 343–367, 2003.

G. Chen, Q. Xie, and L. S. Shieh, “Fuzzy Kalman Filtering,” Information Sciences, vol. 109, pp. 197–209, 1998.

T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature,” Geoscientific Model Development, vol. 7, pp. 1247–1250, 2014.

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DOI: 10.15408/inprime.v4i1.22912


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