Implementasi Unscented Kalman Filter Pada Fusi GNSS Dan IMU Untuk Peningkatan Akurasi Estimasi Posisi Kapal

Oktavia, Selly Rahma (2026) Implementasi Unscented Kalman Filter Pada Fusi GNSS Dan IMU Untuk Peningkatan Akurasi Estimasi Posisi Kapal. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Akurasi posisi merupakan hal penting dalam sistem navigasi kapal. Global Navigation Satellite System (GNSS) dapat memberikan informasi posisi, tetapi akurasinya dapat menurun ketika kualitas sinyal kurang baik. Sementara itu, Inertial Measurement Unit (IMU) mampu memberikan informasi gerak secara kontinu, tetapi dapat mengalami drift yang menyebabkan estimasi posisi semakin menyimpang seiring waktu. Oleh karena itu, penelitian ini membahas fusi sensor Global Navigation Satellite System (GNSS) dan Inertial Measurement Unit (IMU) menggunakan metode Unscented Kalman Filter (UKF) berbasis model eror untuk meningkatkan akurasi estimasi posisi kapal. Data penelitian diperoleh dari uji Free Running Model kapal pada lintasan turning dan zigzag. Data GNSS diolah menjadi posisi east dan north, sedangkan data IMU digunakan untuk memperoleh informasi gerak kapal. Selain itu, estimasi IMU berbasis ridge regression digunakan
sebagai pembanding terhadap hasil fusi GNSS-IMU menggunakan UKF. Akurasi hasil estimasi dievaluasi menggunakan Root Mean Square Error (RMSE) terhadap referensi GNSS. Hasil simulasi menunjukkan bahwa metode UKF menghasilkan estimasi posisi yang lebih mendekati referensi GNSS dibandingkan IMU. Pada lintasan turning, hasil fusi GNSS-IMU menggunakan UKF menghasilkan RMSE sebesar 0.0108 m pada posisi east dan 0.0095 m pada posisi north, sedangkan IMU menghasilkan RMSE sebesar 0.0772 m pada posisi east dan 0.1019 m pada posisi north. Pada lintasan zigzag, hasil fusi GNSS-IMU menggunakan UKF menghasilkan RMSE sebesar 0.0082 m pada posisi east dan 0.0068 m pada posisi north, sedangkan IMU menghasilkan RMSE sebesar 1.8822 m pada posisi east dan 0.4762 m pada posisi north. Dengan demikian, fusi GNSS dan IMU menggunakan UKF mampu meningkatkan akurasi estimasi posisi kapal.
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Position accuracy is an important aspect of a ship navigation system. Global Navigation Satellite System (GNSS) can provide position information, but its accuracy may decrease when the signal quality is poor. Meanwhile, Inertial Measurement Unit (IMU) can provide continuous motion information, but it may experience drift, which causes the position estimation to increasingly deviate over time. Therefore, this study discusses the fusion of Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors using the Unscented Kalman Filter (UKF) method based on an error model to improve the accuracy of ship position estimation. The data used in this study were obtained from a ship Free Running Model test on turning and zigzag trajectories. The GNSS data were processed into east and north positions, while the IMU data were used to obtain ship motion information. In addition, IMU estimation based on ridge regression was used as a comparison to the GNSS-IMU fusion result using UKF. The estimation accuracy was evaluated using Root Mean Square Error (RMSE) against the GNSS reference. The simulation results show that the UKF method produces position estimates that are closer to the GNSS reference than the IMU result. For the turning trajectory, the GNSS-IMU fusion using UKF produces an RMSE of 0.0108 m in the east direction and 0.0095 m in the north direction, while the IMU produces an RMSE of 0.0772 m in the east direction and 0.1019 m in the north direction. For the zigzag trajectory, the GNSS-IMU fusion using UKF produces an RMSE of 0.0082 m in the east direction and 0.0068 m in the north direction, while the IMU produces an RMSE of 1.8822 m in the east direction and 0.4762 m in the north direction. Thus, GNSS and IMU fusion using UKF can improve the accuracy of ship position estimation.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Estimasi Posisi Kapal, Fusi Sensor, GNSS, IMU, Unscented Kalman Filter, GNSS, IMU, Sensor Fusion, Ship Position Estimation, Unscented Kalman Filter
Subjects:	Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression 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:	Selly Rahma Oktavia
Date Deposited:	28 Jul 2026 02:49
Last Modified:	28 Jul 2026 02:49
URI:	http://repository.its.ac.id/id/eprint/138421

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