本文選題：iBeacon室內(nèi)定位　切入點(diǎn)：多傳感器定位　出處：《重慶理工大學(xué)》2017年碩士論文

【摘要】：隨著智能終端的普及以及科技的快速發(fā)展,室內(nèi)位置服務(wù)的需求與日俱增。從1992年紅外線定位技術(shù)到近年來的iBeacon定位技術(shù),室內(nèi)定位技術(shù)得到了快速發(fā)展,多種多樣的定位技術(shù)被提了出來,其中基于無線傳感器網(wǎng)絡(luò)的定位技術(shù)應(yīng)用最為廣泛,比如WiFi定位、藍(lán)牙定位、Zigbee定位等。2013年采用低功耗藍(lán)牙(Bluetooth Low Energy,BLE)技術(shù)的iBeacon被提出后,基于iBeacon位置指紋庫的定位技術(shù)被廣泛追捧,而基于智能手機(jī)慣性傳感器的定位技術(shù)具有自主、短時(shí)精度高等特點(diǎn)。本文通過對(duì)國(guó)內(nèi)外室內(nèi)定位技術(shù)的分析,提出了基于多傳感器與iBeacon的融合定位技術(shù),主要采用了位置指紋庫定位方法與行人航跡推算方法來進(jìn)行實(shí)現(xiàn)。主要研究工作和創(chuàng)新點(diǎn)如下:(1)針對(duì)多徑效應(yīng)以及人員擾動(dòng)等因素造成的iBeacon信號(hào)噪聲問題,本文引入卡爾曼濾波對(duì)采集的iBeacon信號(hào)進(jìn)行處理。(2)為解決加權(quán)K近鄰算法(WKNN)定位結(jié)果跳變問題,采用卡爾曼濾波對(duì)WKNN定位結(jié)果進(jìn)行處理,實(shí)驗(yàn)結(jié)果表明在辦公室環(huán)境下采用卡爾曼濾波進(jìn)行處理后可將定位結(jié)果誤差在1米以內(nèi)的比例提高到80%以上,使定位精度得到了提升。(3)對(duì)多傳感器定位中的行人步數(shù)統(tǒng)計(jì)方法進(jìn)行了改進(jìn),主要提出了基于閾值分級(jí)的方法實(shí)現(xiàn)行人運(yùn)行步態(tài)的檢測(cè),同時(shí)依據(jù)行人步伐頻率來判斷有效步伐。通過實(shí)驗(yàn)驗(yàn)證本文的步數(shù)統(tǒng)計(jì)方法準(zhǔn)確率在97%以上。(4)針對(duì)位置指紋庫匹配過程中運(yùn)算量較大以及匹配結(jié)果中存在較大偏差數(shù)據(jù)的問題,本文提出了多傳感器定位與iBeacon定位的融合策略。首先通過多傳感器定位來預(yù)測(cè)定位結(jié)果的范圍,實(shí)現(xiàn)對(duì)位置指紋庫的約減,最后采用基于WKNN+卡爾曼濾波的組合方法得到定位結(jié)果。按照實(shí)驗(yàn)設(shè)計(jì)進(jìn)行測(cè)試,實(shí)驗(yàn)結(jié)果表明采用本文提出的融合定位方法可將定位結(jié)果誤差在1米以內(nèi)的比例提高到85%以上。(5)定位系統(tǒng)的設(shè)計(jì)和實(shí)現(xiàn)。根據(jù)本文室內(nèi)定位系統(tǒng)的要求,開發(fā)了一套集成本文融合策略和算法的室內(nèi)定位系統(tǒng)。后臺(tái)服務(wù)器采用J2EE架構(gòu),數(shù)據(jù)訪問層采用了Hibernate框架,數(shù)據(jù)表現(xiàn)層和業(yè)務(wù)邏輯層采用Java Servlet組件,主要實(shí)現(xiàn)了位置指紋庫管理模塊、定位算法模塊以及Socket通信模塊等。移動(dòng)客戶端在Android系統(tǒng)平臺(tái)下實(shí)現(xiàn),主要完成了用戶界面交互模塊、服務(wù)器通信模塊、iBeacon信號(hào)采集和處理模塊、傳感器信號(hào)采集和處理模塊等。經(jīng)過實(shí)際測(cè)試,本系統(tǒng)達(dá)到了預(yù)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.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212;TN925

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 袁曉峰;陳頎;;一種基于RFID的定位方法[J];無線電通信技術(shù);2016年06期

2 張旭;張?jiān)?鄭正奇;陳雯;;基于核函數(shù)與卡爾曼濾波的室內(nèi)定位方法[J];上海理工大學(xué)學(xué)報(bào);2016年03期

3 周瑞;袁興中;黃一鳴;;基于卡爾曼濾波的WiFi-PDR融合室內(nèi)定位[J];電子科技大學(xué)學(xué)報(bào);2016年03期

4 劉天yN;;基于RFID的室內(nèi)定位系統(tǒng)研究[J];智能計(jì)算機(jī)與應(yīng)用;2016年02期

5 宋鏢;程磊;周明達(dá);吳懷宇;陳洋;;基于慣導(dǎo)輔助地磁的手機(jī)室內(nèi)定位系統(tǒng)設(shè)計(jì)[J];傳感技術(shù)學(xué)報(bào);2015年08期

6 魏秀蓉;;無線傳感器網(wǎng)絡(luò)數(shù)據(jù)融合研究綜述[J];無線互聯(lián)科技;2015年14期

7 邢倩;郝鵬;劉維亭;李俊英;周揚(yáng);李俊;;基于WLSE-KF的傳感器融合定位算法研究[J];電子器件;2014年06期

8 劉春燕;王堅(jiān);;基于幾何聚類指紋庫的約束KNN室內(nèi)定位模型[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2014年11期

9 余揚(yáng);趙凱飛;沈嘉;;室內(nèi)定位技術(shù)應(yīng)用、研究現(xiàn)狀及展望[J];電信網(wǎng)技術(shù);2014年05期

10 周亮;付永濤;李廣軍;;無線定位與慣性導(dǎo)航結(jié)合的室內(nèi)定位系統(tǒng)設(shè)計(jì)[J];電子技術(shù)應(yīng)用;2014年04期

相關(guān)博士學(xué)位論文 前1條

1 柴艷妹;基于步態(tài)特征的身份識(shí)別技術(shù)研究[D];西北工業(yè)大學(xué);2007年

相關(guān)碩士學(xué)位論文 前10條

1 田波;基于濾噪位置指紋的低功耗藍(lán)牙室內(nèi)定位技術(shù)研究[D];太原理工大學(xué);2016年

2 張旭;基于WLAN位置指紋與慣性傳感器的室內(nèi)定位技術(shù)研究[D];華東師范大學(xué);2016年

3 李翔;基于無線局域網(wǎng)的位置指紋室內(nèi)定位技術(shù)研究[D];湖南大學(xué);2015年

4 宋昌寧;基于WiFi-Sensor技術(shù)的室內(nèi)定位系統(tǒng)研究與實(shí)現(xiàn)[D];北京郵電大學(xué);2015年

5 黃承愷;多傳感器信息融合算法在室內(nèi)定位中的應(yīng)用[D];北京郵電大學(xué);2015年

6 卞合善;基于藍(lán)牙4.0低功耗室內(nèi)定位研究[D];北京郵電大學(xué);2015年

7 徐偉;基于Android手機(jī)的室內(nèi)定位技術(shù)研究與實(shí)現(xiàn)[D];華中師范大學(xué);2014年

8 彭宇倩;基于Zigbee室內(nèi)定位的智能導(dǎo)游系統(tǒng)研究[D];廣東工業(yè)大學(xué);2014年

9 代琪怡;距離無關(guān)的室內(nèi)路徑損耗模型的建立[D];重慶理工大學(xué);2014年

10 王克己;室內(nèi)定位系統(tǒng)中的行人航跡推算研究[D];北京郵電大學(xué);2015年

，

本文編號(hào)：1673747