基于多传感器与iBeacon室内定位的研究与实现

发布时间：2018-03-27 23:27

本文选题：iBeacon室内定位　切入点：多传感器定位　出处：《重庆理工大学》2017年硕士论文

【摘要】：随着智能终端的普及以及科技的快速发展,室内位置服务的需求与日俱增。从1992年红外线定位技术到近年来的iBeacon定位技术,室内定位技术得到了快速发展,多种多样的定位技术被提了出来,其中基于无线传感器网络的定位技术应用最为广泛,比如WiFi定位、蓝牙定位、Zigbee定位等。2013年采用低功耗蓝牙(Bluetooth Low Energy,BLE)技术的iBeacon被提出后,基于iBeacon位置指纹库的定位技术被广泛追捧,而基于智能手机惯性传感器的定位技术具有自主、短时精度高等特点。本文通过对国内外室内定位技术的分析,提出了基于多传感器与iBeacon的融合定位技术,主要采用了位置指纹库定位方法与行人航迹推算方法来进行实现。主要研究工作和创新点如下:(1)针对多径效应以及人员扰动等因素造成的iBeacon信号噪声问题,本文引入卡尔曼滤波对采集的iBeacon信号进行处理。(2)为解决加权K近邻算法(WKNN)定位结果跳变问题,采用卡尔曼滤波对WKNN定位结果进行处理,实验结果表明在办公室环境下采用卡尔曼滤波进行处理后可将定位结果误差在1米以内的比例提高到80%以上,使定位精度得到了提升。(3)对多传感器定位中的行人步数统计方法进行了改进,主要提出了基于阈值分级的方法实现行人运行步态的检测,同时依据行人步伐频率来判断有效步伐。通过实验验证本文的步数统计方法准确率在97%以上。(4)针对位置指纹库匹配过程中运算量较大以及匹配结果中存在较大偏差数据的问题,本文提出了多传感器定位与iBeacon定位的融合策略。首先通过多传感器定位来预测定位结果的范围,实现对位置指纹库的约减,最后采用基于WKNN+卡尔曼滤波的组合方法得到定位结果。按照实验设计进行测试,实验结果表明采用本文提出的融合定位方法可将定位结果误差在1米以内的比例提高到85%以上。(5)定位系统的设计和实现。根据本文室内定位系统的要求,开发了一套集成本文融合策略和算法的室内定位系统。后台服务器采用J2EE架构,数据访问层采用了Hibernate框架,数据表现层和业务逻辑层采用Java Servlet组件,主要实现了位置指纹库管理模块、定位算法模块以及Socket通信模块等。移动客户端在Android系统平台下实现,主要完成了用户界面交互模块、服务器通信模块、iBeacon信号采集和处理模块、传感器信号采集和处理模块等。经过实际测试,本系统达到了预期效果。
[Abstract]:With the popularization of intelligent terminals and the rapid development of science and technology, the demand for indoor location services is increasing day by day. From infrared positioning technology in 1992 to iBeacon positioning technology in recent years, indoor positioning technology has been rapidly developed. A variety of localization techniques have been proposed, among which wireless sensor network-based localization technologies are most widely used, such as WiFi positioning, Bluetooth positioning and Zigbee positioning. After the iBeacon, which uses low power Bluetooth Low energy BLEtechnology, was proposed in 2013, The location technology based on iBeacon position fingerprint database is widely sought after, while the positioning technology based on the inertial sensor of smart phone has the characteristics of independence, high precision in short time and so on. A fusion localization technology based on multi-sensor and iBeacon is proposed. The main research work and innovation are as follows: (1) aiming at the iBeacon signal noise problem caused by multipath effect and personnel disturbance, the paper mainly adopts the location fingerprint database location method and the pedestrian track calculation method to carry on the realization, the main research work and the innovation point are as follows:. In this paper, Kalman filter is introduced to process the collected iBeacon signal. In order to solve the jump problem of the location result of weighted K nearest neighbor algorithm, Kalman filter is used to process the WKNN localization result. The experimental results show that the proportion of the error of positioning results within 1 meter can be increased to more than 80% by using Kalman filter in the office environment. So that the positioning accuracy is improved. (3) the statistical method of pedestrian walking number in multi-sensor location is improved, and the method based on threshold classification is put forward to detect pedestrian walking gait. At the same time, according to the pedestrian step frequency to judge the effective step. The experimental results show that the accuracy of the statistical method is more than 97%, aiming at the problem of large computation and large deviation data in the matching process of position fingerprint database. In this paper, the fusion strategy of multi-sensor location and iBeacon location is proposed. Firstly, the range of location results is predicted by multi-sensor positioning, and the reduction of position fingerprint database is realized. Finally, the combined method based on WKNN Kalman filter is used to get the localization results. The experimental results show that the fusion localization method proposed in this paper can improve the proportion of the error of the positioning results to more than 85%. According to the requirements of the indoor positioning system in this paper, the design and implementation of the positioning system can be achieved. A set of indoor positioning system integrating the strategy and algorithm of this paper is developed. The background server adopts J2EE architecture, the data access layer adopts Hibernate framework, the data presentation layer and business logic layer adopt Java Servlet component. The mobile client is implemented on the platform of Android system. The user interface interaction module and the server communication module are implemented, and the server communication module is designed to collect and process the signals of iBeacon, and the mobile client is implemented on the platform of Android system, which includes the location fingerprint database management module, the location algorithm module and the Socket communication module. Sensor signal acquisition and processing module. After the actual test, the system achieved the desired results.
【学位授予单位】：重庆理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212;TN925

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