Estimasi Posisi Kapal Menggunakan Fusi GNSS-IMU dengan Metode Ensemble Kalman Filter

Anandifa, Aisyah Rania (2026) Estimasi Posisi Kapal Menggunakan Fusi GNSS-IMU dengan Metode Ensemble Kalman Filter. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Estimasi posisi yang akurat dan andal merupakan aspek penting dalam sistem navigasi kapal, terutama pada kondisi lingkungan yang menyebabkan gangguan atau kehilangan sinyal Global Navigation Satellite System (GNSS). GNSS mampu memberikan informasi posisi absolut dengan akurasi yang baik, namun kinerjanya sangat bergantung pada ketersediaan sinyal satelit. Di sisi lain, Inertial Measurement Unit (IMU) mampu menyediakan informasi gerak secara kontinu, tetapi memiliki kelemahan berupa akumulasi kesalahan (drift) seiring waktu. Oleh karena itu, diperlukan metode fusi sensor untuk menggabungkan keunggulan kedua sensor tersebut. Penelitian ini membahas estimasi posisi kapal menggunakan fusi sensor GNSS dan IMU dengan metode Ensemble Kalman Filter (EnKF). EnKF dipilih karena kemampuannya dalam menangani sistem nonlinier dan ketidakpastian model melalui pendekatan berbasis ensemble. Fusi sensor dilakukan secara loosely coupled dengan memanfaatkan data GNSS dan IMU dalam proses estimasi keadaan sistem navigasi kapal. Model matematika sistem digunakan untuk mempropagasikan keadaan berdasarkan data IMU, sedangkan data GNSS dimanfaatkan untuk melakukan koreksi estimasi. Hasil penelitian menunjukkan bahwa penerapan EnKF pada fusi GNSS–IMU mampu menghasilkan estimasi posisi kapal yang lebih stabil dan andal dibandingkan penggunaan sensor tunggal. Dengan demikian, pendekatan yang diusulkan dapat menjadi alternatif metode estimasi keadaan pada sistem navigasi kapal serta memberikan kontribusi dalam pengembangan sistem navigasi berbasis fusi sensor.
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Accurate and reliable position estimation is a crucial aspect of ship navigation systems, particularly under environmental conditions that cause disturbances or temporary loss of Global Navigation Satellite System (GNSS) signals. GNSS is capable of providing absolute position information with good accuracy; however, its performance strongly depends on the availability of satellite signals. On the other hand, the Inertial Measurement Unit (IMU) is able to provide continuous motion information, but it suffers from error accumulation (drift) over time. Therefore, a sensor fusion method is required to combine the advantages of both sensors. This study addresses ship position estimation using GNSS–IMU sensor fusion based on the Ensemble Kalman Filter (EnKF) method. EnKF is selected due to its capability to handle nonlinear systems and model uncertainties through an ensemble-based approach. The sensor fusion is implemented in a loosely coupled manner by incorporating GNSS and IMU data in the state estimation process of the ship navigation system. The system mathematical model is used to propagate the state based on IMU data, while GNSS measurements are employed to correct the estimation. The results indicate that the application of EnKF to GNSS–IMU fusion is able to produce more stable and reliable ship position estimates compared to the use of a single sensor. Therefore, the proposed approach can serve as an alternative state estimation method for ship navigation systems and contribute to the development of sensor fusion based navigation technologies.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	GNSS, IMU, fusi sensor, Ensemble Kalman Filter, navigasi kapal, GNSS, IMU, sensor fusion, Ensemble Kalman Filter, ship navigation
Subjects:	Q Science > QA Mathematics > QA402.3 Kalman filtering. T Technology > TA Engineering (General). Civil engineering (General) > TA1573 Detectors. Sensors T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles. T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL589.2.N3 Navigation computer T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL798.N3 Global Positioning System.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Aisyah Rania Anandifa
Date Deposited:	04 Aug 2026 01:54
Last Modified:	04 Aug 2026 01:54
URI:	http://repository.its.ac.id/id/eprint/140212

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