Analisis Fusi Data Sensor Gnss Dan Imu Menggunakan Metode Unscented Kalman Filter Pada Medical Drone Di Ruang Udara Terbuka.

Navisa, Shilvy Choiriyatun (2022) Analisis Fusi Data Sensor Gnss Dan Imu Menggunakan Metode Unscented Kalman Filter Pada Medical Drone Di Ruang Udara Terbuka. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Estimasi posisi secara akurat dan efisien merupakan elemen penting dalam misi penerbangan wahana udara secara autonomous seperti halnya Unmanned Aerial Vehicle (UAV). UAV bergantung pada sensor yang menyediakan informasi terkait posisi, kecepatan, dan orientasi. GNSS dan IMU merupakan sensor yang mampu memberikan informasi navigasi, dimana setiap sensor memiliki karakteristik yang mampu melengkapi kekurangan masing-masing. Penelitian terkait integrasi sensor GNSS dan IMU untuk penentuan posisi dengan akurasi yang semakin tinggi telah banyak diterapkan, salah satunya adalah metode Kalman Filter. Kalman filter tidak hanya melakukan filtering sinyal digital, tetapi juga mampu melakukan smoothing dan prediksi secara rekursi hingga memperoleh hasil estimasi yang paling akurat. Seiring berkembangnya modifikasi Kalman Filter, metode Unscented Kalman Filter (UKF) menjadi solusi dalam mengatasi sistem nonlinier dengan hasil estimasi mendekati nilai sebenarnya, sehingga akurasi posisi lebih tinggi dan konvergen. Penelitian ini bermaksud melakukan analisis peforma fusi data sensor GNSS dan IMU menggunakan metode UKF pada Medical Drone di ruang terbuka sehingga diperoleh posisi dengan akurasi tinggi. Simulasi UKF pada Medical Drone dilakukan menggunakan model matematis pada 6 DOF (Degree of Freedom) UAV. Peforma fusi data sensor diuji dengan membandingkan hasil metode UKF dengan EKF pada software Geopointer. Hasil penelitian menunjukkan bahwa simulasi UKF memberikan akurasi posisi sebesar 0,022 m terhadap data pengukuran. Simulasi UKF tersebut menghasilkan akurasi posisi yang lebih baik jika dibandingkan pengolahan EKF pada software Geopointer yang hanya mencapai akurasi posisi sebesar 8,467 m. Rendahnya akurasi hasil pengolahan EKF dikarenakan ketidaksesuaian parameter IMU yang diperoleh terhadap parameter Medical Drone yang sebenarnya.
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Estimating position accurately and efficiently is an important element in flight missions for autonomous aerial vehicles such as Unmanned Aerial Vehicles (UAV). UAV rely on sensors that provide information regarding position, speed, and orientation. GNSS and IMU are sensors that can provide navigation information, which each sensor has characteristics that can complement each other's shortcomings. Research related to the integration of GNSS and IMU sensors for positioning with increasingly higher accuracy has been widely applied, one of which is the Kalman Filter method. Kalman filter not only performs digital signal filtering, but is also capable of smoothing and predicting recursion to obtain the most accurate estimation results. Along with the development of modifications to the Kalman Filter, the Unscented Kalman Filter (UKF) method is a solution in overcoming nonlinear systems with estimation results that are close to the actual value, so that the positioning accuracy is higher and convergent. This study intends to analyze the fusion performance of GNSS and IMU sensor data using the UKF method on a Medical Drone in an open space in order to obtain a position with high accuracy. UKF simulation on Medical Drone was carried out using a mathematical model on a 6 DOF (Degree of Freedom) UAV. The sensor data fusion performance was tested by comparing the results of the UKF method with the EKF on the Geopointer software. The results showed that the UKF simulation gave a positional accuracy of 0.022 m to the measurement data. The UKF simulation produces a better positional accuracy when compared to EKF processing on the Geopointer software which only achieves a position accuracy of 8.467 m. The low accuracy of the EKF processing results is due to the mismatch of the IMU parameters obtained with the actual Medical Drone parameters.

Item Type:	Thesis (Other)
Additional Information:	RSG 621.367 8 Nav a-1 2022
Uncontrolled Keywords:	Fusi Sensor, GNSS, IMU, UAV, Kalman Filter, UKF. Sensor Fusion, GNSS, IMU, UAV, Kalman Filter, UKF.
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing
Divisions:	Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	20 May 2026 04:25
Last Modified:	20 May 2026 04:25
URI:	http://repository.its.ac.id/id/eprint/133273

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