| 基于主成分分析的室内指纹定位模型 |
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| 引用本文： | 陈祠,牟楠,张晨,陈永乐,朱红松,刘燕.基于主成分分析的室内指纹定位模型[J].软件学报,2013,24(S1):98-107. |
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| 作者姓名： | 陈祠 牟楠 张晨 陈永乐 朱红松 刘燕 |
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| 作者单位： | 北京大学 软件与微电子学院, 北京 102600;中国科学院 信息工程研究所 信息安全国家重点实验室, 北京 100093;北京大学 软件与微电子学院, 北京 102600;中央民族大学 信息工程学院, 北京 100081;中国科学院 信息工程研究所 信息安全国家重点实验室, 北京 100093;中国科学院 信息工程研究所 信息安全国家重点实验室, 北京 100093;北京大学 软件与微电子学院, 北京 102600 |
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| 基金项目： | 国家自然科学基金(61073180); 国家高技术研究发展计划(863)(2012AA013104); 国家科技重大专项(2011ZX03005-002-02); 中国科学院信息工程研究所前瞻部署项目(Y3Z0071E02) |
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| 摘 要： | 指纹定位是目前最有前途的室内定位方法之一,基于无线信号强度的指纹模型因其无需额外硬件成本、易于推广等特点被广泛采用.指纹模型的选择是影响指纹定位精度的关键因素.传统的通过选择指纹采集点的指纹方法尽管可以减少计算量,但对定位精度贡献不大.提出一种基于主成分分析的指纹模型,通过选择对精度影响最大的一组“成分”作为指导定位的指纹,在减少指纹计算量的同时,提高定位精度.实验结果表明,与基于欧式距离指纹算法和最近邻指纹算法相比,基于主成分分析的指纹算法可以将平均定位精度由5.3m 和3.9m 降低到2.7m.
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| 关 键 词： | 室内定位技术 指纹定位算法 主成分分析法 误差分析 指纹模型 |
| 收稿时间： | 5/2/2013 12:00:00 AM |
| 修稿时间： | 2013/8/22 0:00:00 |
Indoor Fingerprint Positioning Model Based on Principal Component Analysis |
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| CHEN Ci,MU Nan,ZHANG Chen,CHEN Yong-Le,ZHU Hong-Song and LIU Yan.Indoor Fingerprint Positioning Model Based on Principal Component Analysis[J].Journal of Software,2013,24(S1):98-107. |
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| Authors: | CHEN Ci MU Nan ZHANG Chen CHEN Yong-Le ZHU Hong-Song and LIU Yan |
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| Affiliation: | School of Software and Microelectronics, Peking University, Beijing 102600, China;State Key Laboratory of Information Security, Institute of Information Engineering, The Chinese Academy of Sciences, Beijing 100093, China;School of Software and Microelectronics, Peking University, Beijing 102600, China;School of Information Engineering, Minzu University of China, Beijing 100081, China;State Key Laboratory of Information Security, Institute of Information Engineering, The Chinese Academy of Sciences, Beijing 100093, China;State Key Laboratory of Information Security, Institute of Information Engineering, The Chinese Academy of Sciences, Beijing 100093, China;School of Software and Microelectronics, Peking University, Beijing 102600, China |
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| Abstract: | Fingerprint localization is one of the most promising indoor positioning methods, and the fingerprint model based on the wireless signal strength is widely used due to its no-additional hardware cost and easy-to-spread characteristics. The selection of the fingerprint model is the key factor to the fingerprint positioning accuracy. Although the traditional fingerprint method by selecting the fingerprint collection points can reduce the computation, it contributes little to the accuracy of the positioning. In this paper, a fingerprint model based on principal component analysis is proposed. The new model accomplishes improvement in positioning accuracy as well as reduction in fingerprint calculation by selecting a set of "ingredients" with the largest impact on the accuracy to guide the positioning of fingerprint. Experimental results show that compared with the fingerprint algorithms based on Euclidean distance and nearest neighbor, the fingerprint algorithm based on principal component analysis improves average positioning accuracy to 2.7 m from 5.3 m and 3.9 m. |
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| Keywords: | indoor positioning technology fingerprint positioning algorithm principal component analysis error analysis fingerprint model |
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