[vc_empty_space][vc_empty_space]
On Using Unscented Kalman Filter Based Multi Sensors Fusion for Train Localization
Nazaruddin Y.Y.a,c, Tamba T.A.b,c, Faruqi I.a, Waluya M.B.a, Widyotriatmo A.a
a Instrumentation Control Research Group, Institut Teknologi Bandung, West-Java, 40132, Indonesia
b Dept. of Electrical Engineering (Mechatronics), Parahyangan Catholic University, West-Java, 40141, Indonesia
c National Center for Sustainable Transportation Technology, 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 JSME.To guarantee high precision control and assure the reliability and safety of the railway systems operation, highly accurate measurement data from different onboard and wayside sensors are needed. This paper proposes a solution approach to obtain more accurate measurement data which are collected during train movement using an Unscented Kalman Filter (UKF)-based multi sensors data fusion. Several sensors that are used in the proposed approach include indoor positioning systems, radio frequency identification, and rotary encoder. Experimental results that were conducted on a train model show how the proposed method effectively able to provide more accurate measurement data about the train position.[/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]Accurate measurement,High precision control,Highly accurate,Multi sensors data fusion,Reliability and safeties,Solution approach,Train movement,Unscented Kalman Filter[/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][/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]ACKNOWLEDGMENT The authors gratefully acknowledge financial research support for this work from the Ministry of Research, Technology and Higher Education of the Republic of Indonesia (Kemenristekdikti) under the fundamental research scheme (PDUPT) at Institut Teknologi Bandung, 2019. This work was supported in part by USAID through the sustainable higher education research alliances (SHERA) program under grant number IIE00000078-ITB-1.[/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][/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]