【摘要】：位置信息是連接物理世界和網(wǎng)絡(luò)空間的重要結(jié)合點(diǎn),是物聯(lián)網(wǎng)時(shí)代中極其重要的因素,與人類的社會(huì)生活息息相關(guān)�，F(xiàn)在人們的生活方式已發(fā)生改變,每天80%的時(shí)間都活動(dòng)在室內(nèi)環(huán)境下,然而在室內(nèi)環(huán)境下GPS技術(shù)無(wú)法取得令人滿意的定位精度。隨著WiFi網(wǎng)絡(luò)大面積部署于各種室內(nèi)場(chǎng)所,智能手機(jī)的使用已成“燎原”之勢(shì),基于智能手機(jī)的WiFi室內(nèi)定位研究受到越來(lái)越多的關(guān)注。目前,基于智能手機(jī)的WiFi室內(nèi)定位算法主要分為兩種:基于無(wú)線測(cè)距的室內(nèi)定位算法和基于接收信號(hào)強(qiáng)度指示(RSSI)指紋的室內(nèi)定位算法。這兩種定位算法在實(shí)際定位中遇到很多挑戰(zhàn),基于測(cè)距的室內(nèi)定位算法是根據(jù)無(wú)線測(cè)距原理進(jìn)行幾何約束定位,由于室內(nèi)環(huán)境的多徑效應(yīng)導(dǎo)致信號(hào)強(qiáng)度值波動(dòng)很大,從而使得定位精度很低。基于RSSI指紋定位算法的前提是構(gòu)建精確的指紋數(shù)據(jù)庫(kù),然而構(gòu)建RSSI指紋數(shù)據(jù)需要花費(fèi)大量的人工代價(jià)進(jìn)行現(xiàn)場(chǎng)勘測(cè)采集。在針對(duì)基于測(cè)距和RSSI指紋室內(nèi)定位中遇到的挑戰(zhàn),本文分別進(jìn)行如下兩個(gè)方面研究:(1)在針對(duì)無(wú)線測(cè)距誤差大導(dǎo)致定位誤差較大的問(wèn)題,本文提出基于智能手機(jī)的信號(hào)自適應(yīng)修正算法,采用修正因子來(lái)提高無(wú)線測(cè)距的精度,從而降低定位的誤差。(2)在針對(duì)基于RSSI指紋定位中人工采集指紋數(shù)據(jù)代價(jià)的問(wèn)題,本文利用無(wú)線傳感器網(wǎng)絡(luò)進(jìn)行指紋數(shù)據(jù)的采集,并提出面向稀疏采樣的無(wú)線指紋構(gòu)建算法,利用稀疏表示技術(shù)有效降低了無(wú)線傳感器網(wǎng)絡(luò)中數(shù)據(jù)傳輸代價(jià)。本文的研究取得非常不錯(cuò)的效果。在針對(duì)測(cè)距定位中,基于智能手機(jī)的信號(hào)自適應(yīng)修正算法將定位精度提高35.9%。在針對(duì)基于指紋數(shù)據(jù)定位中人工采集指紋成本的問(wèn)題上,本文的面向稀疏采樣的無(wú)線指紋構(gòu)建算法在保證指紋數(shù)據(jù)精度的條件下,有效地降低了采集成本。
[Abstract]:Location information is an important link between the physical world and cyberspace, and is an extremely important factor in the age of the Internet of things, which is closely related to the social life of human beings. Nowadays, people's life style has changed, 80% of the time is in indoor environment. However, GPS technology can not achieve satisfactory positioning accuracy in indoor environment. With the WiFi network deployed in a wide range of indoor locations, the use of smart phones has become a "prairie fire" trend. More and more attention has been paid to the research of WiFi indoor positioning based on smart phones. At present, WiFi indoor location algorithm based on smart phone is mainly divided into two kinds: indoor location algorithm based on wireless ranging and indoor location algorithm based on received signal intensity indicating (RSSI) fingerprint. These two localization algorithms meet a lot of challenges in the actual localization. The indoor localization algorithm based on ranging is based on the principle of wireless ranging for geometric constraint localization. Because of the multipath effect of indoor environment, the signal intensity fluctuates greatly. Thus, the positioning accuracy is very low. The premise of fingerprint location algorithm based on RSSI is to build an accurate fingerprint database. However, the construction of RSSI fingerprint data requires a great deal of manual cost to carry out field survey and collection. In view of the challenges encountered in indoor location based on ranging and RSSI fingerprint, the following two aspects are studied in this paper: (1) aiming at the problem that the large error of wireless ranging leads to the large positioning error, In this paper, an adaptive signal correction algorithm based on smart phone is proposed. The correction factor is used to improve the accuracy of wireless ranging and reduce the positioning error. (2) aiming at the cost of manually collecting fingerprint data in fingerprint location based on RSSI. In this paper, we use wireless sensor networks to collect fingerprint data, and propose a sparse sampling oriented fingerprint construction algorithm. The sparse representation technology can effectively reduce the cost of data transmission in wireless sensor networks. The research in this paper has achieved very good results. In the localization of ranging, the signal adaptive correction algorithm based on smart phone can improve the precision of location by 35.9. Aiming at the cost of fingerprint acquisition based on fingerprint data location, the wireless fingerprint construction algorithm for sparse sampling can effectively reduce the cost of fingerprint acquisition under the condition of ensuring the precision of fingerprint data.
【學(xué)位授予單位】：安徽工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN92

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蔡文學(xué);邱珠成;黃曉宇;蕭超武;陳康;;基于WiFi指紋的室內(nèi)軌跡定位模型[J];計(jì)算機(jī)工程;2015年06期

2 王振朝;張琦;張峰;;基于RSSI測(cè)距的改進(jìn)加權(quán)質(zhì)心定位算法[J];電測(cè)與儀表;2014年21期

3 陳順明;李平;;基于RSSI權(quán)值的環(huán)境適應(yīng)型室內(nèi)定位算法研究[J];計(jì)算機(jī)工程與應(yīng)用;2015年22期

4 張蒼松;郭軍;崔嬌;尚軍;;基于RSSI的室內(nèi)定位算法優(yōu)化技術(shù)[J];計(jì)算機(jī)工程與應(yīng)用;2015年03期

5 王梓有;周憲英;;無(wú)線傳感器網(wǎng)絡(luò)基于信號(hào)到達(dá)角度的節(jié)點(diǎn)定位算法研究[J];艦船電子工程;2012年07期

6 王沁;何杰;張前雄;劉冰峰;于彥偉;;測(cè)距誤差分級(jí)的室內(nèi)TOA定位算法[J];儀器儀表學(xué)報(bào);2011年12期

7 劉云浩;楊錚;王小平;簡(jiǎn)麗榮;;Location,Localization,and Localizability[J];Journal of Computer Science & Technology;2010年02期

8 吳彥鴻;王聰;徐燦;;無(wú)線通信系統(tǒng)中電波傳播路徑損耗模型研究[J];國(guó)外電子測(cè)量技術(shù);2009年08期

9 任春林;文武;;超寬帶無(wú)線通信技術(shù)友應(yīng)用研究[J];電信快報(bào);2007年02期

10 陳維克;李文鋒;首珩;袁兵;;基于RSSI的無(wú)線傳感器網(wǎng)絡(luò)加權(quán)質(zhì)心定位算法[J];武漢理工大學(xué)學(xué)報(bào)(交通科學(xué)與工程版);2006年02期

，

本文編號(hào)：2278121