| WiFi-PDR室内组合定位的无迹卡尔曼滤波算法 |
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| 引用本文： | 陈国良,张言哲,汪云甲,孟晓林.WiFi-PDR室内组合定位的无迹卡尔曼滤波算法[J].测绘学报,2015,44(12):1314-1321. |
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| 作者姓名： | 陈国良 张言哲 汪云甲 孟晓林 |
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| 作者单位： | 1. 中国矿业大学环境与测绘学院, 江苏 徐州 221116;2. 中国矿业大学国土环境与灾害监测国家测绘地理信息局重点实验室, 江苏 徐州 221116;3. 诺丁汉大学, 英国诺丁汉 NG7 2TU |
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| 基金项目： | 国家863计划(2013AA12A201)，国家自然科学基金(41371423)，江苏高校优势学科建设工程(SZBF2011-6-B35)，The National High-tech Research and Development Program of China (863 Program)(2013AA12A201)，The National Natural Science Foundation of China(41371423)，Engineering Construction of Jiangsu Universities(SZBF2011-6-B35) |
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| 摘 要： | 针对当前室内定位的应用需求和亟待解决的关键问题,结合城市室内环境下广泛存在的WiFi无线信号以及智能手机传感器信息,提出了一种WiFi无线信号联合行人航迹推算(PDR)的室内定位方法。该方法采用无迹卡尔曼滤波(UKF)算法对WiFi和PDR定位信息进行融合处理,有效克服了WiFi单点定位精度低和PDR存在累计误差的问题。针对融合算法中WiFi指纹匹配计算量大的问题,用k-means聚类算法对WiFi指纹库进行聚类处理,降低了指纹匹配算法的计算量,提高了算法的实时性。通过在华为P6-U06智能手机平台上实际测试,在时间效率上经过聚类处理后系统定位耗时有很大程度的改善,平均降幅为51%,其中最大降幅达到64%,最小的也达到了36%;在定位精度上,当室内人员为行走状态时WiFi定位平均误差为7.76m,PDR定位平均误差为4.57m,UKF滤波融合后平均定位误差下降到1.24m。
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| 关 键 词： | 室内定位 手机传感器 WiFi 行人航迹推算 k-means 无迹卡尔曼滤波 |
| 收稿时间： | 2015-01-01 |
| 修稿时间： | 2015-05-11 |
Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning |
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| CHEN GuoLiang,ZHANG Yanzhe,WANG Yunjia,MENG Xiaolin.Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning[J].Acta Geodaetica et Cartographica Sinica,2015,44(12):1314-1321. |
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| Authors: | CHEN GuoLiang ZHANG Yanzhe WANG Yunjia MENG Xiaolin |
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| Institution: | 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;2. Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou 221116, China;3. The University of Nottingham, Nottingham NG7 2TU, UK |
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| Abstract: | Indoor positioning still faces lots of fundamental technical problems although it has been widely applied. A novel indoor positioning technology by using the smart phone with the assisting of the widely available and economically signals of WiFi is proposed. It also includes the principles and characteristics in indoor positioning. Firstly, improve the system's accuracy by fusing the WiFi fingerprinting positioning and PDR (ped estrian dead reckoning) positioning with UKF (unscented Kalman filter). Secondly, improve the real-time performance by clustering the WiFi fingerprinting with k-means clustering algorithm. An investigation test was conducted at the indoor environment to learn about its performance on a HUAWEI P6-U06 smart phone. The result shows that compared to the pattern-matching system without clustering, an average reduction of 51% in the time cost can be obtained without degrading the positioning accuracy. When the state of personnel is walking, the average positioning error of WiFi is 7.76 m, the average positioning error of PDR is 4.57 m. After UKF fusing, the system's average positioning error is down to 1.24 m. It shows that the algorithm greatly improves the system's real-time and positioning accuracy. |
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| Keywords: | Indoor positioning smart phone sensors WiFi ped estrian dead reckoning k-means UKF |
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