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A Cooperative Localization Method Using AOA Measurements Based on Deep Neural Network
Multi-station cooperative localization system using the intersection of angle of arrival (AOA) has gained widespread application due to its low cost and strong concealment. However, traditional methods based on linear least squares (LLS) are prone to low localization accuracy due to the influence of...
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| creator | Han, Kuoye Xie, Wupeng Pei, Ruilin Xu, Tianlin Li, Zhuang Pei, Xiaoshuai |
| description | Multi-station cooperative localization system using the intersection of angle of arrival (AOA) has gained widespread application due to its low cost and strong concealment. However, traditional methods based on linear least squares (LLS) are prone to low localization accuracy due to the influence of measurement errors. To address this issue, this paper proposes an AOA localization method based on deep neural network (DNN). This method is applicable to both mobile and fixed base stations. The DNN model takes measurement values and their corresponding measurement errors as network inputs and the target coordinates as network outputs. The network is trained using a large number of samples generated through simulation. Simulation results demonstrate that the proposed method outperforms traditional approaches in terms of localization accuracy and robustness. |
| doi_str_mv | 10.1109/ICET61945.2024.10672630 |
| format | conference_proceeding |
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However, traditional methods based on linear least squares (LLS) are prone to low localization accuracy due to the influence of measurement errors. To address this issue, this paper proposes an AOA localization method based on deep neural network (DNN). This method is applicable to both mobile and fixed base stations. The DNN model takes measurement values and their corresponding measurement errors as network inputs and the target coordinates as network outputs. The network is trained using a large number of samples generated through simulation. 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Simulation results demonstrate that the proposed method outperforms traditional approaches in terms of localization accuracy and robustness.</description><subject>Accuracy</subject><subject>AOA-based localization</subject><subject>Base stations</subject><subject>cooperative localization</subject><subject>Coordinate measuring machines</subject><subject>deep neural network</subject><subject>localization accuracy</subject><subject>Location awareness</subject><subject>Measurement errors</subject><subject>Simulation</subject><subject>Training</subject><issn>2768-6515</issn><isbn>9798350363951</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNqFjstuwjAURE0lJBDkD5DwDxCu7diJl2mgKhKPDayRBRcwDXFkByr4-mbRrrs6mjmzGELGDGLGQE8XxXyrmE5kzIEnMQOVciWgQyKd6kxIEEpoyd5In6cqmyjJZI9EIVwBQHBIIBN9sstp4VyN3jT2gXTpDqa0rza4iq6wubgj3QVbnWm-ydvChLvHG1ZNoO8m4JG2sxliTdd496Zs0Xw7_zUk3ZMpA0a_HJDRx3xbfE4sIu5rb2_GP_d_j8U_-ge64EO1</recordid><startdate>20240517</startdate><enddate>20240517</enddate><creator>Han, Kuoye</creator><creator>Xie, Wupeng</creator><creator>Pei, Ruilin</creator><creator>Xu, Tianlin</creator><creator>Li, Zhuang</creator><creator>Pei, Xiaoshuai</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20240517</creationdate><title>A Cooperative Localization Method Using AOA Measurements Based on Deep Neural Network</title><author>Han, Kuoye ; Xie, Wupeng ; Pei, Ruilin ; Xu, Tianlin ; Li, Zhuang ; Pei, Xiaoshuai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_106726303</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>AOA-based localization</topic><topic>Base stations</topic><topic>cooperative localization</topic><topic>Coordinate measuring machines</topic><topic>deep neural network</topic><topic>localization accuracy</topic><topic>Location awareness</topic><topic>Measurement errors</topic><topic>Simulation</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Han, Kuoye</creatorcontrib><creatorcontrib>Xie, Wupeng</creatorcontrib><creatorcontrib>Pei, Ruilin</creatorcontrib><creatorcontrib>Xu, Tianlin</creatorcontrib><creatorcontrib>Li, Zhuang</creatorcontrib><creatorcontrib>Pei, Xiaoshuai</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore (Online service)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Han, Kuoye</au><au>Xie, Wupeng</au><au>Pei, Ruilin</au><au>Xu, Tianlin</au><au>Li, Zhuang</au><au>Pei, Xiaoshuai</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Cooperative Localization Method Using AOA Measurements Based on Deep Neural Network</atitle><btitle>2024 7th International Conference on Electronics Technology (ICET)</btitle><stitle>ICET</stitle><date>2024-05-17</date><risdate>2024</risdate><spage>844</spage><epage>848</epage><pages>844-848</pages><eissn>2768-6515</eissn><eisbn>9798350363951</eisbn><abstract>Multi-station cooperative localization system using the intersection of angle of arrival (AOA) has gained widespread application due to its low cost and strong concealment. However, traditional methods based on linear least squares (LLS) are prone to low localization accuracy due to the influence of measurement errors. To address this issue, this paper proposes an AOA localization method based on deep neural network (DNN). This method is applicable to both mobile and fixed base stations. The DNN model takes measurement values and their corresponding measurement errors as network inputs and the target coordinates as network outputs. The network is trained using a large number of samples generated through simulation. Simulation results demonstrate that the proposed method outperforms traditional approaches in terms of localization accuracy and robustness.</abstract><pub>IEEE</pub><doi>10.1109/ICET61945.2024.10672630</doi></addata></record> |
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| identifier | EISSN: 2768-6515 |
| ispartof | 2024 7th International Conference on Electronics Technology (ICET), 2024, p.844-848 |
| issn | 2768-6515 |
| language | eng |
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| source | IEEE Xplore All Conference Series |
| subjects | Accuracy AOA-based localization Base stations cooperative localization Coordinate measuring machines deep neural network localization accuracy Location awareness Measurement errors Simulation Training |
| title | A Cooperative Localization Method Using AOA Measurements Based on Deep Neural Network |
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