| 基于GAWK-means的地铁车站指纹定位方法 |
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| 引用本文： | 金霄,吴飞,鄢松,陆雯霞,张忠艺. 基于GAWK-means的地铁车站指纹定位方法[J]. 电子科技, 2022, 35(2): 34-39. DOI: 10.16180/j.cnki.issn1007-7820.2022.02.006 |
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| 作者姓名： | 金霄 吴飞 鄢松 陆雯霞 张忠艺 |
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| 作者单位： | 上海工程技术大学 电子电气工程学院,上海 201620 |
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| 基金项目： | 上海工程技术大学研究生科研创新项目;上海市科技学术委员会重点项目;上海市自然科学基金;国家自然科学基金;上海市科委青年科技英才杨帆计划项目 |
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| 摘 要： | 针对在城市轨道交通车站内,利用iBeacon技术进行指纹定位时存在匹配效率较低、定位精度不理想的问题,文中提出了一种基于GAWK-means的地铁车站指纹定位方法.离线阶段,根据指纹数据本身的离散程度进行K-means欧式距离权重优化以便更好地体现类内相似度,再将改进的K-means结合遗传算法,优化聚类结果以减少陷入...
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| 关 键 词： | 地铁车站 iBeacon技术 指纹定位 遗传算法 K-means聚类 欧式距离 K近邻法 GAWK-means |
| 收稿时间： | 2020-10-30 |
Fingerprint Location Method of Metro Station Based on GAWK-means |
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| JIN Xiao,WU Fei,YAN Song,LU Wenxia,ZHANG Zhongyi. Fingerprint Location Method of Metro Station Based on GAWK-means[J]. Electronic Science and Technology, 2022, 35(2): 34-39. DOI: 10.16180/j.cnki.issn1007-7820.2022.02.006 |
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| Authors: | JIN Xiao WU Fei YAN Song LU Wenxia ZHANG Zhongyi |
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| Affiliation: | School of Electronic and Electrical Engineering,Shanghai University of Engineering Science, Shanghai 201620,China |
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| Abstract: | In order to solve the problem of low matching efficiency and poor positioning accuracy when using iBeacon technology for fingerprint location in urban rail transit stations, a metro station fingerprint location method based on GAWK-means is proposed in this study. In the offline stage, the K-means Euclidean distance weight is optimized according to the discreteness of the fingerprint data to better reflect the intra-class similarity. Then, the improved K-means is combined with the genetic algorithm to optimize the clustering results to reduce the clustering results from falling into the local optimum. In the online stage, the K-nearest neighbor method is used to match the signal vector with the nearest sub-fingerprint database to get the location result, and the overall performance of the method is evaluated by the average positioning error. The experimental results show that the average positioning error of the GAWK-means algorithm is 1.52 m in the offline phase of the subway station. Compared with the un-clustered and traditional K-means clustering, the positioning error of the proposed method is reduced by more than 0.41 m. |
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| Keywords: | metro station iBeacon technology fingerprint location genetic algorithm K-means clustering Euclidean distance K-nearest neighbor method GAWK-means |
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