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Estimation of UAVS’ attitude by using multi-sensor integration
Thien H.P.a, Mulyanto T.a, Muhammad H.a, Suzuki S.b
a Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Indonesia
b Department of Aeronautics and Astronautics, University of Tokyo, Japan
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]As a major step toward the establishment of mathematical model as well as control development for an Unmanned Aerial Vehicle (UAV), sensing of vehicle state should be done in priority. Acceleration, velocity, attitude, and position of the vehicle should be determined in a direct or indirect way. In fact, these states could not be provided by an individual sensor. Hence, integration of multi-sensor is required. And, merging of sensors’ output is necessary to achieve accurate information. In this paper, the estimation of attitude for an UAV by fusing of multi sensor is dealt with. Two Kalman filters are used to merge the angular rate, acceleration from Inertial Measurement Unit and attitude data from Digital Magnetic Compass to obtain the effective information of attitude. Testing on developed sensory system shows that the fusion algorithm works rather well, and can be used to determine the attitude based on data come from several sensors at different update rate.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Angular rate,Digital magnetic compass,Filtering,Fusion algorithms,Inertial measurement unit,Multi sensor,Sensory system,Vehicle state[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Data fusion,Filtering,Kalman filter,State estimation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.2316/P.2010.702-043[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]