{"title":"基于AOA测距测量的无线传感器网络协同定位","authors":"Xianbo Jiang, Shengchu Wang","doi":"10.1109/WCNC45663.2020.9120806","DOIUrl":null,"url":null,"abstract":"This paper researches the cooperative localization in wireless sensor networks (WSNs) with $2\\pi/\\pi$-periodic angle-of-arrival (AOA) ranging measurements. When the orientation angles of the antenna arrays at WSN nodes are known, a ranging link loss is defined based on the tan-relationships between AOA observations and xy-minus coordinates of two neighboring nodes. Subsequently, the positioning problem under $2\\pi$-periodic AOAs is converted as a convex optimization problem about minimizing total ranging-link loss through optimizing agent positions, which is resolved by the gradient-descent (GD) method. Under $\\pi$-periodic AOAs, additional 0/1 integers are introduced to indicate the front-or-back impinging directions. By relaxing 0/1 integers as continuous variables within $[0,1]$, the positioning problem is relaxed as a nonconvex optimization one about minimizing total link loss over the agent positions and indicating variables, which is solved by the projected GD (PGD) method. Finally, Type-I least-square (LS) localizer is developed for WSNs with both $2\\pi$ and $\\pi$-periodic AOAs. When the orientation angles are unknown, Type-II LS localizer is developed by combining Type-I LS localizer with a maximum-likelihood (ML) orientation estimator, which alternatively updates agent positions and orientation angles. Simulation results validate that the proposed LS-type localizers outperform existing localizers.","PeriodicalId":415064,"journal":{"name":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Cooperative Localization in Wireless Sensor Networks with AOA Ranging Measurements\",\"authors\":\"Xianbo Jiang, Shengchu Wang\",\"doi\":\"10.1109/WCNC45663.2020.9120806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper researches the cooperative localization in wireless sensor networks (WSNs) with $2\\\\pi/\\\\pi$-periodic angle-of-arrival (AOA) ranging measurements. When the orientation angles of the antenna arrays at WSN nodes are known, a ranging link loss is defined based on the tan-relationships between AOA observations and xy-minus coordinates of two neighboring nodes. Subsequently, the positioning problem under $2\\\\pi$-periodic AOAs is converted as a convex optimization problem about minimizing total ranging-link loss through optimizing agent positions, which is resolved by the gradient-descent (GD) method. Under $\\\\pi$-periodic AOAs, additional 0/1 integers are introduced to indicate the front-or-back impinging directions. By relaxing 0/1 integers as continuous variables within $[0,1]$, the positioning problem is relaxed as a nonconvex optimization one about minimizing total link loss over the agent positions and indicating variables, which is solved by the projected GD (PGD) method. Finally, Type-I least-square (LS) localizer is developed for WSNs with both $2\\\\pi$ and $\\\\pi$-periodic AOAs. When the orientation angles are unknown, Type-II LS localizer is developed by combining Type-I LS localizer with a maximum-likelihood (ML) orientation estimator, which alternatively updates agent positions and orientation angles. Simulation results validate that the proposed LS-type localizers outperform existing localizers.\",\"PeriodicalId\":415064,\"journal\":{\"name\":\"2020 IEEE Wireless Communications and Networking Conference (WCNC)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Wireless Communications and Networking Conference (WCNC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCNC45663.2020.9120806\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Wireless Communications and Networking Conference (WCNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCNC45663.2020.9120806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cooperative Localization in Wireless Sensor Networks with AOA Ranging Measurements
This paper researches the cooperative localization in wireless sensor networks (WSNs) with $2\pi/\pi$-periodic angle-of-arrival (AOA) ranging measurements. When the orientation angles of the antenna arrays at WSN nodes are known, a ranging link loss is defined based on the tan-relationships between AOA observations and xy-minus coordinates of two neighboring nodes. Subsequently, the positioning problem under $2\pi$-periodic AOAs is converted as a convex optimization problem about minimizing total ranging-link loss through optimizing agent positions, which is resolved by the gradient-descent (GD) method. Under $\pi$-periodic AOAs, additional 0/1 integers are introduced to indicate the front-or-back impinging directions. By relaxing 0/1 integers as continuous variables within $[0,1]$, the positioning problem is relaxed as a nonconvex optimization one about minimizing total link loss over the agent positions and indicating variables, which is solved by the projected GD (PGD) method. Finally, Type-I least-square (LS) localizer is developed for WSNs with both $2\pi$ and $\pi$-periodic AOAs. When the orientation angles are unknown, Type-II LS localizer is developed by combining Type-I LS localizer with a maximum-likelihood (ML) orientation estimator, which alternatively updates agent positions and orientation angles. Simulation results validate that the proposed LS-type localizers outperform existing localizers.