基于RSSI-LQI的ZigBee定位算法的研究与设计

发布时间：2018-05-16 07:47

本文选题：ZigBee定位 + RSSI　；参考：《南京邮电大学》2017年硕士论文

【摘要】：随着智能家居的兴起,越来越多的人将目光转向了短距离无线定位网络技术,特别是Zig Bee定位技术,在未来较长一段时间内,定位实时性、定位误差和定位算法将越来越来被人们广泛关注。研究有关Zig Bee定位相关的课题,对Zig Bee的发展前景具有深远意义。本文将从定位的两个阶段分别进行改进:(1)测距阶段,本文首先引入LQI,利用LQI和RSSI共同来求解距离,在RSSI和LQI的数据处理上,使用高斯滤波的方法,解决了RSSI和LQI出现较大偏差的问题,在学者们用的比较多的理论模型的基础上进行改进,提出多项式模型,此模型解决了理想模型不能够贴合真实环境的问题,并减小了测距误差。(2)定位阶段,本文将采用极大似然估计的定位方法,但是在实际运用过程中,极大似然估计存在一些问题,所以本文运用两次极大似然估计法进行定位,通过仿真,证明了二次极大似然估计定位法能够提高定位精度。通过对测距和定位阶段的改进,本文所提出的新型Zig Bee定位法具有低功耗、低复杂度以及高精度等特点。
[Abstract]:With the rise of smart home, more and more people turn their attention to the short-range wireless positioning network technology, especially Zig Bee positioning technology, in the future for a long period of time, positioning real-time, More and more attention will be paid to the localization error and location algorithm. It is of great significance for the future development of Zig Bee to study the related topics of Zig Bee positioning. In this paper, we will improve the two stages of location by using the LQI, and use LQI and RSSI to solve the distance. In the data processing of RSSI and LQI, the method of Gao Si filter is used. The problem of large deviation between RSSI and LQI is solved, and the polynomial model is proposed on the basis of more theoretical models used by scholars. This model solves the problem that the ideal model can not fit the real environment. In this paper, the location method of maximum likelihood estimation is used, but in the practical application process, there are some problems in maximum likelihood estimation, so this paper uses the method of maximum likelihood estimation twice to locate. By simulation, it is proved that the quadratic maximum likelihood estimation method can improve the positioning accuracy. Through the improvement of ranging and localization stage, the new Zig Bee localization method proposed in this paper has the characteristics of low power consumption, low complexity and high precision.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN92;TP212.9

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