Comparison of AUV Position Estimation Using Kalman Filter, Ensemble Kalman Filter and Fuzzy Kalman Filter Algorithm in the Specified Trajectories
Abstract
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.
Abstrak
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.
Keywords
References
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DOI: 10.15408/inprime.v4i1.22912
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