{"title":"一种基于GA-BFO的室内智能定位算法","authors":"Z. Lan, Ma Hongmei, S. Changyin, Wu Xinqiao","doi":"10.1109/ICISCE.2016.261","DOIUrl":null,"url":null,"abstract":"The error caused by nonline-of-sight (NLOS) is main factor affecting the indoor wireless positioning accuracy. In order to eliminate the NLOS error and improve the positioning accuracy, genetic algorithm, genetic algorithm-Hill Climbing algorithm and genetic algorithm-Bacteria Foraging Optimization algorithm are applied to time difference of arrival (TDOA) positioning optimization in this paper. Research results show genetic algorithm-Bacteria Foraging Optimization algorithm, combined global search with local search, has the best performance in terms of positioning accuracy and convergence speed. This method is better in eliminating the NLOS error and improving the performance of real-time positioning.","PeriodicalId":6882,"journal":{"name":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","volume":"182 ","pages":"1222-1225"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Novel Indoor Intelligent Location Algorithm Based on GA-BFO\",\"authors\":\"Z. Lan, Ma Hongmei, S. Changyin, Wu Xinqiao\",\"doi\":\"10.1109/ICISCE.2016.261\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The error caused by nonline-of-sight (NLOS) is main factor affecting the indoor wireless positioning accuracy. In order to eliminate the NLOS error and improve the positioning accuracy, genetic algorithm, genetic algorithm-Hill Climbing algorithm and genetic algorithm-Bacteria Foraging Optimization algorithm are applied to time difference of arrival (TDOA) positioning optimization in this paper. Research results show genetic algorithm-Bacteria Foraging Optimization algorithm, combined global search with local search, has the best performance in terms of positioning accuracy and convergence speed. This method is better in eliminating the NLOS error and improving the performance of real-time positioning.\",\"PeriodicalId\":6882,\"journal\":{\"name\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"volume\":\"182 \",\"pages\":\"1222-1225\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCE.2016.261\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCE.2016.261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Indoor Intelligent Location Algorithm Based on GA-BFO
The error caused by nonline-of-sight (NLOS) is main factor affecting the indoor wireless positioning accuracy. In order to eliminate the NLOS error and improve the positioning accuracy, genetic algorithm, genetic algorithm-Hill Climbing algorithm and genetic algorithm-Bacteria Foraging Optimization algorithm are applied to time difference of arrival (TDOA) positioning optimization in this paper. Research results show genetic algorithm-Bacteria Foraging Optimization algorithm, combined global search with local search, has the best performance in terms of positioning accuracy and convergence speed. This method is better in eliminating the NLOS error and improving the performance of real-time positioning.
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