基于GPS和RFID组合定位算法的研究

发布时间：2018-03-14 23:03

本文选题：智慧城市　切入点：GPS定位　出处：《北京建筑大学》2017年硕士论文　论文类型：学位论文

【摘要】：智慧城市和物联网技术的不断发展,对实时定位服务提出了更高的需求。城管业务中面临缺乏动态智能感知城市部件位置的方法,也受困于城市部件的流动性大导致监察执法难度大等问题。一种能够实时动态感知,具有较高精度,可以适应城市复杂环境的定位算法应运而生。本文在我校自主设计的“移动式单射频基站与车载GNSS协同的标签定位算法”的基础上,针对现有协同定位算法中GPS定位数据精度不高、稳定性差,缺乏定位数据精度提高的方法,整体组合定位精度受到限制的现状,开展了基于卡尔曼滤波器的GPS定位数据优化方法研究,推导了组合定位基站的运动方程,并构建了基站的运动状态模型,实现了对GPS定位数据的最优化估计;设计并完成了组合定位算法的精度评定实验,提出了实验数据的精度评估方法,将均值、均方差等统计量应用于GPS定位精度以及组合定位精度的评价,对卡尔曼滤波器在GPS定位数据优化方面的效果进行验证分析。针对文中实测数据进行分析评估,对比使用卡尔曼滤波器对GPS定位数据进行优化估计的前后,组合定位误差平均值降低了25.8%,方差降低了31.2%;GPS数据处理前后,组合定位误差减少了0.719米,降低了28.1%,提高了标签的定位精度。本文扩充了组合定位算法的数据优化方法,提高了组合定位算法在实际中的实用性与适用性。
[Abstract]:With the development of intelligent city and Internet of things technology, there is a higher demand for real-time location service. They are also trapped by the mobility of urban components, which makes it difficult to supervise and enforce the law. One is capable of real-time and dynamic perception and has a high accuracy. The localization algorithm which can adapt to the complex urban environment arises at the historic moment. Based on the "tag localization algorithm of mobile single radio frequency base station and vehicle GNSS", which is designed by our school, In view of the low accuracy and poor stability of GPS positioning data and the lack of methods to improve the accuracy of positioning data, the integrated positioning accuracy is limited. The optimization method of GPS positioning data based on Kalman filter is studied, the motion equation of the combined positioning base station is deduced, the motion state model of the base station is constructed, and the optimal estimation of the GPS positioning data is realized. The precision evaluation experiment of the combined positioning algorithm is designed and completed, and the precision evaluation method of the experimental data is put forward. The mean value, mean square deviation and other statistics are applied to the evaluation of the GPS positioning accuracy and the combined positioning accuracy. The effect of Kalman filter on the optimization of GPS positioning data is verified and analyzed. The actual data in this paper are analyzed and evaluated, and the GPS positioning data are optimized and estimated before and after the Kalman filter is used. The average value of the combined positioning error is reduced by 25.8, the variance is reduced by 31.2um before and after GPS data processing, the combined positioning error is reduced by 0.719 meters, and the positioning accuracy of the tag is reduced by 28.1m. the data optimization method of the combined positioning algorithm is extended in this paper. The practicability and applicability of the combined location algorithm in practice are improved.
【学位授予单位】：北京建筑大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.44;P228.4

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