Ensemble and Fuzzy Kalman Filter for position estimation of an autonomous underwater vehicle based on dynamical system of AUV motion

Ngatini,; Apriliani, Erna; Nurhadi, Hendro

doi:10.1016/j.eswa.2016.10.003

Cited by 44 publications

(7 citation statements)

References 10 publications

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“…is division method is more in line with the cognitive structure of the general public. Due to the diverse backgrounds and purposes of users who use digital music resources, the establishment of a complete classification system can lay a solid foundation for the further promotion of digital music [15,16].…”

Section: Music Digitization Appreciationmentioning

confidence: 99%

Digital Development Path of Music Appreciation Based on the Kalman Filter

2022

Mathematical Problems in Engineering

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For a long time, due to the influence of curriculum orientation and examination-oriented education, the learning of music appreciation courses has not been paid much attention. This makes the teaching method in music appreciation teaching single, the classroom effect cannot reach the expected learning goal, and the classroom teaching efficiency becomes low, which urgently needs a new learning method to deal with these problems. Focusing on the digital education background of the new curriculum reform, this paper investigates the teaching nature, teaching methods, teaching design and evaluation, classroom construction, and student ability development of music appreciation. In this paper, the Kalman filter algorithm is used, and the optimal transformation order determined in advance is used to suppress the noise in music and improve the sound quality of music appreciation. If the order of the full-band signal model is selected as 10, and the number of sub-bands is selected as 4, then the order of the sub-band signal model should be at least greater than or equal to 2. In the music appreciation teaching test based on the Kalman filter, it was found that 35.6% of the students believed that the music appreciation course based on the Kalman filter could completely improve the learning efficiency, and 49% of students believe that the teaching of music appreciation class based on the Kalman filter can improve a large part of the learning efficiency. Overall, 85.5% of the students believed that the music appreciation class based on the Kalman filter is of great help in improving the learning efficiency, and the Kalman filter denoising method proposed in this paper has an obvious effect, which is a new attempt to promote the digital development of music appreciation.

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Section: Music Digitization Appreciationmentioning

confidence: 99%

Digital Development Path of Music Appreciation Based on the Kalman Filter

2022

Mathematical Problems in Engineering

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“…Salah satu pengembangannya berupa estimasi lintasan AUV dengan trayektori lintasan yang ditentukan. Salah satu estimasi posisi AUV adalah dengan menggunakan Kalman Filter [3] dan juga estimasi AUV dengan menggunakan perbandingan antara Fuzzy Kalman Filter dan Ensemble Kalman Filter pada lintasan tertentu [4]. Pada dasarnya metode estimasi Kalman Filter memiliki beberapa modifikasi yang menyesuaikan dengan jenis model matematika pada permasalahan yang akan diselesaikan.…”

Section: Pendahuluanunclassified

“…Salah satu metode yang dapat digunakan untuk estimasi posisi AUV adalah metode Asimilasi Data. Beberapa metode telah banyak digunakan untuk melakukan estimasi posisi AUV antara lain yaitu Kalman Filter [3], Ensemble Kalman Filter pada model sistem non-linear [6], EnKF-SR pada AUV Segorogeni ITS [7], perbandingan Fuzzy Kalman Filter dan EnKF [4]. Metode estimasi tersebut mampu memberikan hasil estimasi posisi dari AUV.…”

Section: A Auv (Autonomous Underwater Vehicle)unclassified

Estimasi Lintasan AUV 3 Dimensi (3D) Dengan Ensemble Kalman Filter

Ngatini

Nurhadi

2019

ISI

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AUV is an unmanned underwater vehicle that is driven (controlled) by a computer device. The motion system of AUV requires a navigation and guidance control to direct the motion of the AUV, so an estimated position of the AUV is needed in accordance with the given path. This study develops the position estimation of AUV Segorogeni ITS using the Ensemble Kalman Filter (EnKF) method because EnKF can estimate the problem in the form of a non-linear system model where the equations of AUV are non-linear. Position estimation is carried out on a 3-dimensional (3D) trajectory which is built by using the Octave program. The simulation displays the results of AUV position estimation using the EnKF algorithm with several different ensembles which are 50, 100, 200 and 300 ensembles. The accuracy of the estimation is measured by the error value of the estimated result, namely the value of RMSE (Root Mean Square Error). The simulation results show an estimated error average of 0.4 m for x-position, 0.46 m for y-position, 0.08 m for z-position and 0.1 m for angle error.Keywords -AUV, EnKF, 3D Trajectory.Intisari -AUV (Autonomous Underwater Vehicle) merupakan kapal selam tanpa awak yang sistem geraknya dikemudikan (dikendalikan) oleh perangkat komputer. Sistem gerak dari AUV membutuhkan sebuah navigasi dan guidance control yang mampu mengarahkan gerak AUV, sehingga dibutuhkan sebuah estimasi posisi AUV sesuai dengan lintasan yang diberikan. Penelitian ini mengembangkan estimasi posisi dari AUV Segorogeni ITS menggunakan metode atau algoritma Ensemble Kalman Filter (EnKF) karena EnKF mampu mengestimasi persoalan berbentuk model sistem non linier dimana persamaan gerak dari AUV berbentuk non linear. Estimasi posisi dilakukan pada lintasan atau trayektori 3 dimensi (3D) yang dibangun dengan bantuan program Octave. Simulasi menampilkan hasil estimasi posisi AUV menggunakan algoritma EnKF dengan beberapa jumlah ensemble yang berbeda yaitu 50, 100, 200 dan 300 ensemble. Akurasi dari estimasi tersebut diukur dari nilai error hasil estimasi yaitu nilai RMSE (Root Mean Square Error). Hasil simulasi menunjukan rata-rata error estimasi yaitu 0.4 m posisi-x, 0.46 m posisi-y, 0.08 m posisi-z dan 0.1 m error sudut.Kata Kunci -AUV, EnKF, Lintasan 3 dimensi.

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“…Different variations of Kalman Filter (KF) algorithms are Fuzzy Kalman Filter (FKF), Unscented Kalman Filter (UKF), Ensemble Kalman Filter (EnKF), and other modifications. EnKF is an algorithm to estimate nonlinear models [3], while FKF is an algorithm to estimate linear models with a Fuzzy state variable [4]. This paper investigates the comparison of AUV (Autonomous Underwater Vehicle) position estimation between KF, FKF, and EnKF.…”

Section: Introductionmentioning

confidence: 99%

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

Ngatini

Apriliani

Nurhadi

2022

InPrime:Ind.Jour.Pure.Applied.Math

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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. AbstrakPenelitian 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.

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Ensemble and Fuzzy Kalman Filter for position estimation of an autonomous underwater vehicle based on dynamical system of AUV motion

Cited by 44 publications

References 10 publications

Digital Development Path of Music Appreciation Based on the Kalman Filter

Digital Development Path of Music Appreciation Based on the Kalman Filter

Estimasi Lintasan AUV 3 Dimensi (3D) Dengan Ensemble Kalman Filter

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

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