[vc_empty_space][vc_empty_space]
Design and simulation of navigation system for hybrid autonomous underwater glider
Azhima S.A.T.A.a, Hadi M.I.a, Priandiri V.P.a, Trilaksono B.R.a, Hidayat E.M.a
a Institut Teknologi Bandung, Control and Computer Research Group, School of Electrical Engineering and Informatics, Bandung, Jawa Barat, Indonesia
[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]© 2019 IEEE.In this paper, the position, velocity and attitude of a Hybrid Autonomous Underwater Glider (HAUG) have estimated by fusion of inertial navigation system (INS) and Doppler velocity log (DVL) to optimize the variables estimation. The filters have been used for estimate the position, velocity and attitude of HAUG, namely Extended Kalman Filter. In the EKF is implemented using nonlinear kinematic model of state error vector. The Allan variance method is used to calculate matrix Q and R parameters. Output of the system was estimated position, attitude and velocity of the vehicle. The result in open loop simulation of navigation process will compared to the output of model vehicle. The estimates have accurate results shown with graphs and average of error.[/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]Allan variance,Autonomous underwater gliders,Design and simulation,Doppler velocity logs,Inertial navigation systems (INS),Nonlinear kinematics,Open-loop simulations,Underwater[/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]Allan variance,DVL,Extended Kalman filter,IMU,Underwater,Vehicle[/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/10.1109/ICSEngT.2019.8906500[/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]