一种基于GA-BFO的室内智能定位算法

2016 3rd International Conference on Information Science and Control Engineering (ICISCE) Pub Date : 2016-07-08 DOI:10.1109/ICISCE.2016.261

Z. Lan, Ma Hongmei, S. Changyin, Wu Xinqiao

{"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":null,"pages":null},"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\":null,\"pages\":null},\"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}

引用次数: 3

摘要

非线性视距引起的误差是影响室内无线定位精度的主要因素。为了消除NLOS误差，提高定位精度，本文将遗传算法、遗传算法-爬坡算法和遗传算法-细菌觅食优化算法应用于到达时间差(TDOA)定位优化。研究结果表明，将全局搜索与局部搜索相结合的遗传算法-细菌觅食优化算法在定位精度和收敛速度方面具有最佳性能。该方法较好地消除了NLOS误差，提高了实时定位性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2016 3rd International Conference on Information Science and Control Engineering (ICISCE)

自引率

0.00%

发文量