WiFi网络中面向移动目标的定位追踪算法研究

发布时间：2018-03-30 06:41

本文选题：WiFi　切入点：定位　出处：《东北大学》2014年硕士论文

【摘要】：随着数据业务和多媒体业务的快速增加,人们对位置服务的需求日益增大。用户无论在室外还是室内环境,快速准确地获得移动终端的位置信息和提供位置服务的需求变得日益迫切,位置服务已经成为我们生活中不可或缺的一部分。而基于IEEE 802.11a/b/g/n通信协议的WiFi网络的提出,它具有灵活拓扑架构,改善了现有无线网络通信条件,快速成为移动通信系统的重要补充,现已成为国内外研究的热点。本文对传统的均值聚类和卡尔曼滤波进行改进,提出了WiFi网络中面向移动目标的精确定位技术。该技术主要用于在WiFi无线网络覆盖的环境下,它能根据接收WiFi信号强度对移动终端进行定位和追踪。本文在分析研究现有的WIFI定位技术基础上,结合传统K-均值聚类和卡尔曼滤波中出现的问题,从定位响应速度、目标定位精确度等方面,对定位技术中的算法进行了以下改进：1、通过对位置坐标序列指纹特征的分析,将聚类算法引入指纹库的处理。在对各种聚类算法的研究后,结合室内WiFi定位的具体情况,将K-Means聚类技术进行改进,从而减少因初始聚类中心选择带来的聚类结果不稳定和不准确的现象。另一方面,我们还对聚类比对算法进行改进,针对异构类型终端定位效果差的问题,引入了序列化对比算法。2、分析定位系统得到的目标位置序列,我们先提出了一种基于卡尔曼自适应算法。它通过对观测数据的误差进行自动更新,减少传统卡尔曼滤波中对场景环境的依赖性,提高了工程使用价值。另一方面,为了针对滤波发散问题,我们提出自适应的衰减因子,在保证滤波收敛的同时也减少旧历史数据对滤波结果的影响。3、本课题最后结合以上技术实现了快速定位跟踪系统,并对系统性能进行了测试。实验结果证实本文设计实现的系统在WiFi网络下基本满足项目需求。
[Abstract]:With the rapid increase of data services and multimedia services, the demand for location services is increasing. It is increasingly urgent to obtain location information of mobile terminal and provide location service quickly and accurately. Location service has become an indispensable part of our life. WiFi network based on IEEE 802.11a/b/g/n communication protocol is proposed. It has a flexible topology, improves the existing wireless network communication conditions, and quickly becomes an important supplement to mobile communication systems. It has become a hot research topic at home and abroad. In this paper, the traditional mean clustering and Kalman filter are improved. This paper presents a precise location technology for mobile targets in WiFi networks, which is mainly used in the environment of WiFi wireless network coverage. It can locate and track the mobile terminal according to the received WiFi signal strength. Based on the analysis and research of the existing WIFI positioning technology, this paper combines the problems in the traditional K-means clustering and Kalman filter, from the localization response speed. In the aspect of target location accuracy, the algorithm of location technology is improved as follows: 1. By analyzing the fingerprint features of position coordinate sequence, the clustering algorithm is introduced into the fingerprint database. After the research of various clustering algorithms, Combined with the specific situation of indoor WiFi location, the K-Means clustering technology is improved to reduce the instability and inaccuracy of the clustering results caused by the selection of the initial clustering center. On the other hand, we also improve the clustering comparison algorithm. Aiming at the problem of poor positioning effect of heterogeneous terminals, a serialization contrast algorithm. 2 is introduced to analyze the target position sequence obtained by the location system. We first propose an adaptive algorithm based on Kalman, which can automatically update the error of observation data, reduce the dependence on scene environment in traditional Kalman filter, and improve the engineering value, on the other hand, In order to solve the problem of filtering divergence, we propose an adaptive attenuation factor, which not only guarantees the convergence of filtering, but also reduces the influence of old historical data on filtering results. The performance of the system is tested and the experimental results show that the system designed and implemented in this paper basically meets the requirements of the project under the WiFi network.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN92

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