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| IMU/GPS组合导航系统自适应Kalman滤波算法 | |
| 引用本文： | 胡安冬, 王坚, 汪云甲, 刘春燕, 谭兴龙, 李增科. 利用渐消自适应EKF算法进行PDR-WiFi融合定位[J]. 武汉大学学报 ( 信息科学版), 2016, 41(11): 1556-1562. DOI: 10.13203/j.whugis20140432 |
| 作者姓名： | 胡安冬 王坚 汪云甲 刘春燕 谭兴龙 李增科 |
| 作者单位： | 1.中国矿业大学国土环境与灾害监测国家测绘局重点实验室, 江苏 徐州, 221116;2.中国矿业大学环境与测绘学院, 江苏 徐州, 221116;3.墨尔本皇家理工大学数学与地理信息系, 澳大利亚 |
| 基金项目： | 国土资源部公益性行业科研专项（201411007-1） |
| 摘 要： |  针对基于指纹库的WiFi定位存在的点位重积、回跳，行人航位推算算法中误差积累的问题，提出了并实现了通过一种自适应加权扩展卡尔曼滤波对两种定位算法进行松耦合。首先给出了WiFi无线定位和行人航位推算进行位置解算的原理，采用渐消因子的自适应加权EKF算法实现了两者的融合，最后通过实测数据验证算法的有效性。  试验表明，该方法在保持了WiFi定位单次定位高精度的特性的同时，继承了航位推算的连贯性，不仅减少了WiFi定位所存在的重复堆积点以及回跳点，并在一定程度上削弱了行人航位推算所存在的积累误差，提高了融合算法的效率，大大提高了室内定位的精度与稳定性。  |
| 关 键 词： | 室内定位 行人航位推算 扩展卡尔曼滤波 WiFi 渐消因子 自适应加权 |
| 收稿时间： | 2015-11-04 |
Application of Adaptive Kalman Algorithm in IMU/GPS Integrated Navigation System |
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| HU Andong, WANG Jian, WANG Yunjia, LIU Chunyan, TAN Xinglong, LI Zengke. An Fusion Positioning for PDR and WiFi Based on Fading Adaptive Weighted EKF[J]. Geomatics and Information Science of Wuhan University, 2016, 41(11): 1556-1562. DOI: 10.13203/j.whugis20140432 | |
| Authors: | HU Andong WANG Jian WANG Yunjia LIU Chunyan TAN Xinglong LI Zengke |
| Affiliation: | 1.Key Laboratory of Land Environment and Disaster Monitoring, National Administration of Surveying, Mapping and Geoinformation, China University of Mining and Technology, Xuzhou 221116, China;2.School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;3.School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, VIC, Austrlia |
| Abstract: | According to the accumulation of points within WiFi locations based on the fingerprint map database, and error accumulation calculated by Pedestrian Dead Reckoning, a loose fusion coupling algorithm by Adaptive Weighted Extended Kalman Filter is presented. This method maintains the high-precision of WiFi locations. In the meanwhile, the algorithm inherited the coherence from PDR(Pedestrian Dead Reckoning), which not only decreased the accumulated rebound points, but weakened the error accumulation, enhanced the efficiency of the fusion algorithm, and finally improved the precision and stability of indoor localization. The result denotes that this method works in the indoor environment quite well, which improves almost 22.9% according to WiFi results. |
| Keywords: | indoor localization pedestrian dead reckoning extended Kalman filter WiFi fading factor adaptive weighted |
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