The orientation angle of a quadrotor UAV can be estimated from gyroscope and accelerometer data. Orientation can be predicted from gyroscope data under static or dynamic conditions, but the predicted value has accumulated errors. Meanwhile, orientation can also be calculated from accelerometer data, but only correct if the sensor is in a static state. To get a more precise orientation angle, the orientation predicted from the gyroscope data and the orientation calculated from the accelerometer data were fused using a Kalman filter. Determination of the condition of the sensor using a threshold value that is applied to the covariance of the acceleration data. in this study, the zero-acceleration compensation algorithm is used so that when the sensor is static, the orientation angle is calculated from the accelerometer. The use of this algorithm can increase the accuracy of the quadrotor orientation for roll angle to 96.84% and pitch angle to 98.91%. Keywords Kalman filter; orientation; attitude; quadrotor; zero-acceleration compensation Full Text: PDF References Sandi, B. Y., Kurniawan, F., & Lasmadi, L. (2020, December). Estimasi Sudut Orientasi Rigid Body dengan Menggunakan Sensor IMU (Inertial Measurement Unit) dan Magnetometer. In Conference SENATIK STT Adisutjipto Yogyakarta (Vol. 6, pp. 283-294). Ardiantara, P. S., Sumiharto, R., & Wibowo, S. B. (2014). Purwarupa Kontrol Kestabilan Posisi dan Sikap pada Pesawat Tanpa Awak Menggunakan IMU dan Algoritma Fusion Sensor Kalman Filter. IJEIS, 4(1), 25-34. Kurniawan, F., Nasution, M. R. E., Dinaryanto, O., & Lasmadi, L. (2021). Penentuan Orientasi dan Translasi Gerakan UAV Menggunakan Data Fusion Berbasis Kalman Filter. AVITEC, 3(2), DOI: 10.28989/avitec.v3i2.890 Rhudy, M. B., Salguero, R. A., & Holappa, K. (2017). A Kalman filtering tutorial for undergraduate students. International Journal of Computer Science & Engineering Survey, 8(1), 1-9. Lasmadi, Cahyadi, A., & Hidayat, R. (2016). Implementasi Kalman Filter untuk Navigasi Quadrotor Berbasis Sensor Accelerometer. Prosiding SENIATI, 242-B. Jonathan, N., & Rippun, F. (2016). Implementasi Filter Kalman Pada Sistem Sensor Inertial Measurement Unit (IMU) Quadcopter. Jurnal Elektro Unika Atma Jaya, 9(2), 99-110. Alma’i, V. R., Wahyudi, W., & Setiawan, I. (2011). Aplikasi Sensor Accelerometer Pada Deteksi Posisi (Doctoral dissertation, Jurusan Teknik Elektro Fakultas Teknik). Kimberly Tuck (2007). Accelerometer Systems and Applications Engineering,Accelerometers, T. S. U. L. Tempe, AZ. Suryanti, D. I. (2017). Inertial Measurement Unit (IMU) Pada Sistem Pengendali Satelit. Media Dirgantara, 12(2). Ojeda, L. V., Zaferiou, A. M., Cain, S. M., Vitali, R. V., Davidson, S. P., Stirling, L. A., & Perkins, N. C. (2017). Estimating Stair Running Performance Using Inertial Sensors.Sensors, 17(11), 2647. Wicaksono, M. A. R., Kurniawan, F., & Lasmadi, L. (2020). Kalman Filter Untuk Mengurangi Derau Sensor Accelerometer pada IMU Guna Estimasi Jarak. AVITEC, 2(2), 145-160, DOI: 10.28989/avitec.v2i2.752 Ermawati, E., Rahayu, P., & Zuhairoh, F. (2017). Perbandingan Solusi Numerik Integral Lipat Dua pada Fungsi Aljabar dengan Metode Romberg dan Simulasi Monte Carlo. Jurnal MSA. http://dx.doi.org/10.28989/avitec.v4i1.1109 Refbacks There are currently no refbacks.