【摘要】：隨著位置服務(wù)的興起和發(fā)展,作為位置服務(wù)前提的定位技術(shù)越來(lái)越受人們的重視。全球定位系統(tǒng)GPS作為一種廣泛應(yīng)用的定位技術(shù),在開(kāi)闊的室外環(huán)境中,能夠提供精確的定位信息。然而,由于GPS信號(hào)無(wú)法良好地穿透建筑物,GPS在室內(nèi)環(huán)境中無(wú)法實(shí)現(xiàn)精確定位。 隨著智能移動(dòng)終端的普及,基于移動(dòng)終端的室內(nèi)定位技術(shù)具有廣泛的應(yīng)用前景。目前室內(nèi)定位研究領(lǐng)域最常見(jiàn)的定位技術(shù)主要包括位置指紋定位技術(shù)和航位推測(cè)技術(shù)。這兩種技術(shù)分別在不同的應(yīng)用背景下使用： (1)定位區(qū)域無(wú)線信號(hào)分布情況已知�；诮邮招盘�(hào)強(qiáng)度(Received Signal Strength, RSS)的位置指紋定位技術(shù)因成本低、調(diào)度簡(jiǎn)單而成為研究熱點(diǎn)。位置指紋定位技術(shù)要求在定位之前首先依據(jù)無(wú)線信號(hào)分布情況建立位置指紋數(shù)據(jù)庫(kù),定位時(shí)通過(guò)匹配當(dāng)前無(wú)線信號(hào)測(cè)量值和位置指紋數(shù)據(jù)庫(kù)中的記錄來(lái)進(jìn)行定位,因而位置指紋定位技術(shù)不能實(shí)現(xiàn)未知環(huán)境中的定位。為了獲得精確的定位結(jié)果,位置指紋定位技術(shù)前期需要大量的數(shù)據(jù)采集和校準(zhǔn)工作來(lái)建立指紋庫(kù),實(shí)際應(yīng)用效率較低。 (2)定位區(qū)域無(wú)線信號(hào)分布情況未知。航位推測(cè)定位技術(shù)可在無(wú)線信號(hào)分布情況未知的情況下,利用移動(dòng)終端的傳感器測(cè)量單元進(jìn)行慣性數(shù)據(jù)采集,通過(guò)慣性數(shù)據(jù)進(jìn)行慣性自定位。但若慣性測(cè)量單元存在較大測(cè)量誤差,定位誤差會(huì)不斷累積,定位精度隨著時(shí)間迅速下降。 基于上述兩種室內(nèi)定位應(yīng)用背景,本文在現(xiàn)有的室內(nèi)關(guān)鍵定位技術(shù)基礎(chǔ)上,研究基于普通移動(dòng)終端的室內(nèi)定位增強(qiáng)改進(jìn)技術(shù),以提高定位技術(shù)的精度和應(yīng)用效率。本文依據(jù)空間無(wú)線信號(hào)的變化趨勢(shì)規(guī)律,將高斯過(guò)程回歸模型運(yùn)用到位置指紋定位技術(shù)中,研究高效的位置指紋數(shù)據(jù)庫(kù)構(gòu)建方法,以減少指紋樣本采集密度,提高位置指紋定位技術(shù)的應(yīng)用效率。本文在航位推測(cè)定位技術(shù)中,引入了粒子濾波技術(shù)和位置指紋定位技術(shù),以此來(lái)對(duì)慣性定位結(jié)果進(jìn)行校正,從而解決慣性定位誤差隨慣性測(cè)量誤差不斷累積的問(wèn)題。 本文對(duì)上述的定位技術(shù)方案進(jìn)行了驗(yàn)證實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明：高斯過(guò)程回歸模型能夠很好地預(yù)測(cè)空間位置RSS,可利用高斯過(guò)程回歸模型來(lái)對(duì)定位區(qū)域進(jìn)行RSS預(yù)測(cè),建立指紋庫(kù),減少信號(hào)樣本采集密度；粒子濾波技術(shù)結(jié)合位置指紋技術(shù)應(yīng)用到慣性定位系統(tǒng)中,能夠顯著抑制慣性導(dǎo)航定位的誤差累積效應(yīng)。
[Abstract]:With the rise and development of location service, people pay more and more attention to location technology as the premise of location service. Global Positioning system (GPS), as a widely used positioning technology, can provide accurate location information in open outdoor environment. However, because the GPS signal can not penetrate the building well, GPS can not locate accurately in the indoor environment. With the popularity of intelligent mobile terminals, indoor positioning technology based on mobile terminals has a wide range of application prospects. At present, the most common localization technology in indoor positioning research field mainly includes position fingerprint location technology and position estimation technology. The two techniques are used in different applications: (1) the distribution of wireless signals in the location area is known. Location fingerprint location technology based on received signal strength (Received Signal Strength, RSS) has become a research hotspot because of its low cost and simple scheduling. The location fingerprint location technology requires that the location fingerprint database be established according to the distribution of wireless signals before location, and the location is located by matching the current wireless signal measurement values and the records in the location fingerprint database. Therefore, the location fingerprint location technology can not realize the location in unknown environment. In order to obtain accurate location results, a large amount of data acquisition and calibration work is needed to establish fingerprint database in the early stage of location fingerprint location technology, and the practical application efficiency is relatively low. (2) the distribution of wireless signal in the location area is unknown. Under the condition that the wireless signal distribution is unknown, the mobile terminal sensor measurement unit can be used to collect the inertial data, and the inertial self-positioning can be carried out by inertial data. However, if there is a large measurement error in the inertial measurement unit, the positioning error will accumulate continuously, and the positioning accuracy will decrease rapidly with time. Based on the above two indoor positioning application background, this paper studies the indoor positioning enhancement and improvement technology based on the common mobile terminal based on the existing indoor key positioning technology, in order to improve the accuracy and application efficiency of the positioning technology. According to the changing trend of space wireless signal, Gao Si process regression model is applied to location fingerprint location technology, and an efficient location fingerprint database construction method is studied in order to reduce the collection density of fingerprint samples. Improve the application efficiency of position fingerprint location technology. In this paper, particle filter technique and position fingerprint technique are introduced to correct the result of inertial positioning, so as to solve the problem that the error of inertial positioning is accumulated with the error of inertial measurement. The experimental results show that Gao Si's process regression model can well predict the spatial location RSS, can make RSS prediction of the location area and establish the fingerprint database by Gao Si process regression model. Reducing the sampling density of signal samples; Particle filter technology combined with position fingerprint technology is applied to inertial positioning system, which can significantly suppress the error accumulation effect of inertial navigation positioning.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN92

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 邵婷婷;馬建倉(cāng);胡士峰;王超;;電子羅盤的傾斜及羅差補(bǔ)償算法研究[J];傳感技術(shù)學(xué)報(bào);2007年06期

2 郭紅成;羅海勇;尹浩;韓霜;;基于線性插值和動(dòng)態(tài)指紋補(bǔ)償?shù)姆植际蕉ㄎ凰惴╗J];傳感技術(shù)學(xué)報(bào);2009年12期

3 夏克寒;許化龍;張樸睿;;粒子濾波的關(guān)鍵技術(shù)及應(yīng)用[J];電光與控制;2005年06期

4 王俊;;淺析測(cè)繪技術(shù)的新發(fā)展——室內(nèi)定位技術(shù)[J];科教文匯(下旬刊);2012年10期

5 李曉陽(yáng);;WiFi技術(shù)及其應(yīng)用與發(fā)展[J];信息技術(shù);2012年02期

6 李昊;;位置指紋定位技術(shù)[J];山西電子技術(shù);2007年05期

7 邊平勇;;粒子濾波算法在貝葉斯模型中的應(yīng)用[J];統(tǒng)計(jì)與決策;2009年14期

8 盧志忠;楊先飛;;零速校正在慣性導(dǎo)航系統(tǒng)中的仿真研究[J];應(yīng)用科技;2009年06期

9 陳嶺;許曉龍;楊清;陳根才;;基于三次樣條插值的無(wú)線信號(hào)強(qiáng)度衰減模型[J];浙江大學(xué)學(xué)報(bào)(工學(xué)版);2011年09期

10 付強(qiáng)文;秦永元;李四海;;速度約束輔助車載捷聯(lián)慣導(dǎo)系統(tǒng)零速校正算法[J];系統(tǒng)工程與電子技術(shù);2013年08期

相關(guān)博士學(xué)位論文 前1條

1 賀建軍;基于高斯過(guò)程模型的機(jī)器學(xué)習(xí)算法研究及應(yīng)用[D];大連理工大學(xué);2012年

，

本文編號(hào)：2334885