Estimasi Hasil Fusi Sensor GNSS/IMU pada Sistem Navigasi Mobil Menggunakan Metode Improved Innovation Adaptive Kalman Filter (IAKF)

Nabilah, Dwitiya Khansa (2023) Estimasi Hasil Fusi Sensor GNSS/IMU pada Sistem Navigasi Mobil Menggunakan Metode Improved Innovation Adaptive Kalman Filter (IAKF). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Sistem navigasi pada mobil otonom umumnya menggunakan sensor berbiaya tinggi, sehingga diperlukan pengembangan sensor berbiaya rendah dan memiliki akurasi tinggi. Sensor yang umum digunakan yaitu GNSS (Global Navigation Satelite System). GNSS dapat menentukan posisi absolut (pasti) dengan akurasi yang cukup tinggi, namun akurasi GNSS dapat menurun ketika terjadi hambatan penerimaan sinyal. Hambatan dapat disebabkan oleh bias atmosfer dan berbagai kondisi lingkungan seperti gedung tinggi, terowongan, pegunungan, dan sebagainya. Ketika GNSS tidak menerima sinyal satelit, GNSS akan kehilangan sinyal dan padam (signal outage). Sensor IMU (Inertial Measurement Unit), dapat digunakan sebagai pendukung GNSS dalam memperoleh informasi navigasi. IMU mempunyai frekuensi pengukuran yang tinggi dan tidak tergantung pada sinyal satelit. Namun gangguan (noise) pada sensor IMU yang cenderung menumpuk menyebabkan hasil navigasi yang kurang akurat. Berdasarkan kelebihan dan kekurangan kedua sensor, dilakukan integrasi sensor yang disebut fusi sensor. Fusi sensor GNSS dan IMU pada kondisi free outage akan dilakukan dengan menggunakan model pergerakan mobil pada 3 Degree of Freedom (DOF) dengan metode Improved Innovation Adaptive Kalman Filter (IAKF). Sementara pada kondisi GNSS outage, tidak bisa dilakukan fusi GNSS dan IMU karena hanya IMU yang memiliki pengukuran. Oleh karena itu, digunakan metode integrasi numerik Euler untuk memperoleh informasi posisi dari data IMU. Pada kondisi free outage, hasil fusi GNSS dan IMU mampu meningkatkan akurasi posisi Easting dan Northing hingga 93, 01% dan 93, 05% dibanding penggunaan stand-alone GNSS. Namun, pada kondisi GNSS outage akurasi yang dihasilkan IMU masih tergolong rendah yaitu sebesar 14.7081m pada Easting dan 5.6436m pada Northing.
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Navigation systems in autonomous cars generally use high-cost sensors, so it is necessary to develop sensors that are low-cost and have high accuracy. The sensor that is commonly used is GNSS (Global Navigation Satellite System). GNSS can determine absolute positions with fairly high accuracy, but GNSS accuracy can decrease when there are obstacles to signal reception. Resistance can be caused by atmospheric bias and various environmental conditions such as tall buildings, tunnels, mountains, and so on. When GNSS does not receive satellite signals, GNSS will lose signal and go out (signal outage). IMU (Inertial Measurement Unit) sensor, can be used as a support for GNSS in obtaining navigational information. The IMU has a high measurement frequency and is independent of satellite signals. However, noise on the IMU sensor tends to accumulate causing inaccurate navigation results. Based on the advantages and disadvantages of the two sensors, sensor integration is carried out which is called sensor fusion. The GNSS and IMU sensor fusion in free outage conditions will be carried out using the car movement model at 3 Degrees of Freedom (DOF) with the Improved Innovation Adaptive Kalman Filter (IAKF) method. Meanwhile, in the GNSS outage condition, GNSS and IMU fusion cannot be carried out because only the IMU has measurements. Therefore, the Euler numerical integration method is used to obtain position information from IMU data. In free outage conditions, the results of the fusion of GNSS and IMU can increase the position accuracy of Easting and Northing up to 93.01% and 93.05% compared to the use of stand-alone GNSS. However, under GNSS outage conditions, the accuracy produced by the IMU is still relatively low, namely 14.7081m in Easting and 5.6436m in Northing.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Fusi sensor, Outage, GNSS, IMU, Improved Innovation Adaptive Kalman Filter, Euler, Sensor fusion, Outage
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL221.5 Hybrid Vehicles. Hybrid cars
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Dwitiya Khansa Nabilah
Date Deposited:	31 Jul 2023 08:15
Last Modified:	31 Jul 2023 08:15
URI:	http://repository.its.ac.id/id/eprint/100070

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