Perbandingan Metode Unscented Kalman Filter dan Ensemble Kalman Filter untuk Estimasi Lintasan Autonomous Surface Vehicle (ASV) di bawah Pengaruh Faktor Lingkungan

Puspitasari, Novita (2019) Perbandingan Metode Unscented Kalman Filter dan Ensemble Kalman Filter untuk Estimasi Lintasan Autonomous Surface Vehicle (ASV) di bawah Pengaruh Faktor Lingkungan. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Autonomous Surface Vehicle (ASV) merupakan sebuah wahana berbentuk kapal dipermukaan air yang dapat bergerak tanpa awak di dalamnya secara otomatis. Karena ASV bergerak tanpa awak, dan pada Tugas Akhir ini gerak ASV dipengaruhi oleh kecepatan angin dan tinggi gelombang maka dibutuhkan estimator untuk memprediksi lintasan ASV. Metode estimasi yang digunakan adalah metode Unscented Kalman Filter dan Ensemble Kalman Filter. Pada penelitian ini hasil estimasi lintasan ASV menggunakan metode Unscented Kalman Filter dibandingkan dengan hasil estimasi lintasan ASV menggunakan metode Ensemble Kalman Filter untuk mengetahui hasil estimasi yang optimal. Hasil estimasi menunjukkan bahwa Unscented Kalman Filter memiliki hasil yang optimal dalam memprediksi lintasan ASV di bawah pengaruh faktor lingkungan. Hal ini ditunjukkan dengan tingkat keakurasian sebesar 99,97% pada posisi x dan 99,89% pada posisi y.
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Autonomous Surface Vehicle (ASV) is a boat-shaped-vehicle moving automatically without a skipper inside. Since this movement (ASV) is influenced by the wind speeds and the high waves; therefore, an estimator is needed to ensure the boat moves along the navigation of ASV. The estimator methods used are Unscented Kalman Filter and Ensemble Kalman Filter. In this investigation, the navigation estimation result of Unscented Kalman Filter method is compared to the navigation estimation result of Ensemble Kalman Filter method to know the optimal estimation result. The estimation result of Unscented Kalman Filter shows an optimality to predict the navigation of ASV under the influence of environmental factor. This result is proven by the accuracy of 99,97% at the x position and 99,89% at the y position.

Item Type:	Thesis (Undergraduate)
Additional Information:	RSMa 519.2 Pus p-1 2019
Uncontrolled Keywords:	Autonomous Surface Vehicle (ASV), Ensemble Kalman Filter, Usnscented Kalman Filter
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA402.3 Kalman filtering. T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Novita Puspitasari
Date Deposited:	30 Dec 2021 06:37
Last Modified:	30 Dec 2021 06:37
URI:	http://repository.its.ac.id/id/eprint/61837

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