{"title":"利用定向天线和递归估计进行定位","authors":"M. Nilsson","doi":"10.1109/WPNC.2008.4510377","DOIUrl":null,"url":null,"abstract":"In wireless sensor networks, there is often a need for nodes to find their position. This process is referred to as localization. Many methods have been proposed for this purpose, but they typically suffer from one of two major problems: Either, they are inaccurate for noisy measurement data, or they require a considerable amount of computation. In this paper, we present a method based on recursive estimation of position from angle-of-arrival measurements by directional antennas. The method computes a new position estimate for every new measurement, using a Kalman filter. Computation is fast and is performed entirely locally. No complex data structure needs to be maintained. A prominent feature of the proposed method is that it applies only a linear Kalman filter.","PeriodicalId":277539,"journal":{"name":"2008 5th Workshop on Positioning, Navigation and Communication","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Localization using directional antennas and recursive estimation\",\"authors\":\"M. Nilsson\",\"doi\":\"10.1109/WPNC.2008.4510377\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In wireless sensor networks, there is often a need for nodes to find their position. This process is referred to as localization. Many methods have been proposed for this purpose, but they typically suffer from one of two major problems: Either, they are inaccurate for noisy measurement data, or they require a considerable amount of computation. In this paper, we present a method based on recursive estimation of position from angle-of-arrival measurements by directional antennas. The method computes a new position estimate for every new measurement, using a Kalman filter. Computation is fast and is performed entirely locally. No complex data structure needs to be maintained. A prominent feature of the proposed method is that it applies only a linear Kalman filter.\",\"PeriodicalId\":277539,\"journal\":{\"name\":\"2008 5th Workshop on Positioning, Navigation and Communication\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 5th Workshop on Positioning, Navigation and Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WPNC.2008.4510377\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th Workshop on Positioning, Navigation and Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WPNC.2008.4510377","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Localization using directional antennas and recursive estimation
In wireless sensor networks, there is often a need for nodes to find their position. This process is referred to as localization. Many methods have been proposed for this purpose, but they typically suffer from one of two major problems: Either, they are inaccurate for noisy measurement data, or they require a considerable amount of computation. In this paper, we present a method based on recursive estimation of position from angle-of-arrival measurements by directional antennas. The method computes a new position estimate for every new measurement, using a Kalman filter. Computation is fast and is performed entirely locally. No complex data structure needs to be maintained. A prominent feature of the proposed method is that it applies only a linear Kalman filter.