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

Ngatini1∗, Zunif Ermayanti2, Erna Apriliani2, and Hendro Nurhadi3
1Department of Informatics, Universitas Internasional Semen Indonesia, Indonesia
2Department of Mathematics, Institut Teknologi Sepuluh Nopember, Indonesia
4Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Email: * ngatini@uisi.ac.id, april@matematika.its.ac.id, hdnurhadi@me.its.ac.id

Abstract

This research compares AUV position estimations using Kalman Filter (KF), Ensemble Kalman Filter (EnKF), and Fuzzy Kalman Filter (FKF) algorithm for some specified trajectories. Assessment is performed on AUV Segorogeni ITS developed by the Institute Technology of Sepuluh Nopember (ITS), Indonesia. The specified trajectories are actual trajectories, i.e., diving, straight, and turning paths. The comparisons for each trajectory are made according to the simulation results and the RMSE (Root Mean Square Error) values. The best estimation is given by different methods depending on the trajectories. Fuzzy Kalman Filter gives the best result on the diving trajectory (Y-position and angle) and the straight trajectory. Ensemble Kalman Filter (EnKF) provides the best result on the X-position in the diving trajectory. At the same time, Kalman Filter gives the best result on a straight trajectory.

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

Full Paper: Download Full Paper

Plagiarism Check: Download Check Plagiarism

Peer Review: Download Peer Review