{"title":"基于到达角约束的线性规划传感器定位","authors":"C. Gentile, John Shiu","doi":"10.1109/CISS.2007.4298378","DOIUrl":null,"url":null,"abstract":"In previous work, we established a linear programming framework to determine sensor location from measured link distances between neighboring nodes in a network. Besides providing greater accuracy compared to other techniques, linear programs in particular suit large networks since they can be solved efficiently through distributed computing over the nodes without compromising the optimality of the objective function. This work extends our framework to determine sensor location from measured arrival angles instead. An extensive simulation suite substantiates the performance of the algorithm according to several network parameters, including noise up to 15% the maximum error; the proposed algorithm reduces the error up to 84% depending on the noise level.","PeriodicalId":151241,"journal":{"name":"2007 41st Annual Conference on Information Sciences and Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Sensor Location through Linear Programming with Arrival Angle Constraints\",\"authors\":\"C. Gentile, John Shiu\",\"doi\":\"10.1109/CISS.2007.4298378\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In previous work, we established a linear programming framework to determine sensor location from measured link distances between neighboring nodes in a network. Besides providing greater accuracy compared to other techniques, linear programs in particular suit large networks since they can be solved efficiently through distributed computing over the nodes without compromising the optimality of the objective function. This work extends our framework to determine sensor location from measured arrival angles instead. An extensive simulation suite substantiates the performance of the algorithm according to several network parameters, including noise up to 15% the maximum error; the proposed algorithm reduces the error up to 84% depending on the noise level.\",\"PeriodicalId\":151241,\"journal\":{\"name\":\"2007 41st Annual Conference on Information Sciences and Systems\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 41st Annual Conference on Information Sciences and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2007.4298378\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 41st Annual Conference on Information Sciences and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2007.4298378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sensor Location through Linear Programming with Arrival Angle Constraints
In previous work, we established a linear programming framework to determine sensor location from measured link distances between neighboring nodes in a network. Besides providing greater accuracy compared to other techniques, linear programs in particular suit large networks since they can be solved efficiently through distributed computing over the nodes without compromising the optimality of the objective function. This work extends our framework to determine sensor location from measured arrival angles instead. An extensive simulation suite substantiates the performance of the algorithm according to several network parameters, including noise up to 15% the maximum error; the proposed algorithm reduces the error up to 84% depending on the noise level.