Implementasi Extended Kalman Filter Untuk Sensor Fusion Pada Localization System Berbasis Sensor GPS/IMU

Arkham, Dikri (2022) Implementasi Extended Kalman Filter Untuk Sensor Fusion Pada Localization System Berbasis Sensor GPS/IMU. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Berkembangnya kendaraan otonom menyebabkan kebutuhan akan system localization yang akurat semakin meningkat. Dalam hal ini banyak digunakan penggabungan dua sensor GPS dan IMU. Penelitian ini bertujuan untuk merancang dan mengetahui performansi sensor fusion antara sensor GPS dan sensor IMU menggunakan algoritma extended kalman filter. Sensor GPS menghasilkan informasi berupa posisi dan kecepatan sedangkan sensor IMU menghasilkan informasi sudut dan kecepatan sudut. Extended kalman filter dipilih dikarenakan dapat mengestimasi keluaran posisi, kecepatan, arah heading (sudut), dan kecepatan sudut dengan mengakomodasi persamaan nonlinear serta dapat menggabungkan informasi pembacaan dari sensor GPS dan IMU. Hasil performansi sensor fusion menggunakan algoritma extended kalman filter berbasis GPS/IMU pada localization system memberikan estimasi yang lebih baik dengan hasil yang mendekati nilai referensi dimana pada uji statis posisi longitude sebesar 0,634 m, uji statis kecepatan sebesar 1,72%, uji statis arah heading (sudut) sebesar 0,735%, uji statis kecepatan sudut sebesar 2,660%, uji dinamis posisi longitude sebesar 1,599 m, uji dinamis kecepatan sebesar 0,637 km/j, uji dinamis arah heading (sudut) 2,691 derajat, dan uji dinamis kecepatan sudut sebesar 14,144 derajat/sekon.
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The development of autonomous vehicles causes the need for an accurate localization system to increase. In many cases it is used to combine the two GPS sensors and the IMU. This study aims to design and determine the performance of sensor fusion between GPS sensors and IMU sensors using the extended kalman filter algorithm. The GPS sensor produces information in the form of position and speed while the IMU sensor produces angular and angular velocity information. The Extended Kalman filter was chosen because it can estimate the output position, speed, heading direction (angle), and angular velocity by accommodating nonlinear equations and can combine reading information from GPS and IMU sensors. The results of sensor fusion performance using the extended kalman filter algorithm based on GPS/IMU on the localization system provide a better estimate with results that are close to the reference value where the longitude position static test is 0.634 m, velocity static test is 1.72%, heading direction static test (angle) is 0.735%, static test of angular velocity is 2.660%, dynamic test of longitude position is 1.599 m, dynamic test of speed is 0.637 km/h, dynamic test of heading direction (angle) is 2.691 degrees, and dynamic test of angular velocity is 14.144 degrees / sec.

Item Type:	Thesis (Other)
Additional Information:	RSF 621.367 Ark i-1
Uncontrolled Keywords:	Localization System, Sensor GPS, Sensor IMU, Extended Kalman Filter,
Subjects:	Q Science > QA Mathematics > QA402.3 Kalman filtering.
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	27 Apr 2026 06:02
Last Modified:	27 Apr 2026 06:02
URI:	http://repository.its.ac.id/id/eprint/132916

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