本文關(guān)鍵詞： 室內(nèi)定位 數(shù)據(jù)挖掘 指紋法 WKNN K均值聚類 嵌入式系統(tǒng)　出處：《北京交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：位置信息是重要的信息資源,位置信息的應(yīng)用關(guān)系到國(guó)防、醫(yī)藥、工業(yè)和我們生活的方方面面。定位系統(tǒng)的研究曾經(jīng)是國(guó)防科技的重要技術(shù)研究方向,自從GPS等衛(wèi)星定位系統(tǒng)普及,定位芯片逐漸植入各式各樣的電子產(chǎn)品,其中包括人們目前大量使用智能手機(jī),這樣,定位功能開始走入尋常百姓的生活之中。智能手持設(shè)備的普及催生了新的生活需求,基于位置信息的服務(wù)(Location Based Services, LBS)應(yīng)運(yùn)而生,LBS逐漸成為學(xué)術(shù)和工業(yè)界的研究熱點(diǎn)。衛(wèi)星定位系統(tǒng)使得室外定位技術(shù)已經(jīng)非常成熟,但是微波信號(hào)不能穿透墻壁進(jìn)入室內(nèi),不能滿足更大的室內(nèi)定位需求。本文討論的主要問(wèn)題是定位技術(shù)的這一個(gè)新的方向——室內(nèi)定位技術(shù)。本文對(duì)室內(nèi)環(huán)境下的無(wú)線局域網(wǎng)(Wireless Local Area Network, WLAN)信號(hào)傳播特點(diǎn)進(jìn)行了調(diào)研,對(duì)采集到的信號(hào)強(qiáng)度進(jìn)行統(tǒng)計(jì),將信號(hào)強(qiáng)度的分布特點(diǎn)和時(shí)變特性進(jìn)行了簡(jiǎn)單總結(jié)。并且詳細(xì)闡述了基于WLAN環(huán)境的接收信號(hào)強(qiáng)度(Received Signal Strength, RSS)的指紋法定位理論,包括定位系統(tǒng)的主要邏輯模型和主要的定位算法。將室內(nèi)定位系統(tǒng)與信息檢索系統(tǒng)進(jìn)行類比,同樣可以分為在線階段和離線階段。同時(shí)引入了數(shù)據(jù)挖掘方面的一些觀點(diǎn),根據(jù)數(shù)據(jù)挖掘應(yīng)用于知識(shí)發(fā)現(xiàn)的功能特點(diǎn),將數(shù)據(jù)挖掘理論體系融入指紋法室內(nèi)定位研究當(dāng)中,指導(dǎo)定位系統(tǒng)改進(jìn)。本文重點(diǎn)研究了室內(nèi)定位系統(tǒng)在線階段以加權(quán)的K鄰近(Weighed K-Nearest Neighbors, WKNN)算法作為定位算法的定位性能,根據(jù)訓(xùn)練數(shù)據(jù)的統(tǒng)計(jì)分析給出定位算法參數(shù)的選定數(shù)值,并且分析了各個(gè)參數(shù)對(duì)定位性能的影響情況。在離線階段優(yōu)化了指紋數(shù)據(jù)的組織形式,將指紋數(shù)據(jù)進(jìn)行聚類管理,以求減小在線定位時(shí)查找信息的計(jì)算量,通過(guò)改進(jìn)后的適用于錨點(diǎn)指紋數(shù)據(jù)結(jié)構(gòu)的K均值聚類算法對(duì)離線指紋庫(kù)聚類分析,得到聚類結(jié)果。文章給出了特定環(huán)境下的最佳聚類參數(shù)選定辦法,分析了室內(nèi)定位中和離線指紋庫(kù)聚類相關(guān)的性能分析公式,以求更加深刻地認(rèn)識(shí)室內(nèi)定位問(wèn)題。文章最后描述了室內(nèi)定位系統(tǒng)的開發(fā)成果,本系統(tǒng)是搭載在嵌入式平臺(tái)的手持硬件系統(tǒng),利用USB無(wú)線網(wǎng)卡采集數(shù)據(jù),定位算法通過(guò)純軟件實(shí)現(xiàn),定位效果和理論值比較接近,從而佐證了指紋法室內(nèi)定位系統(tǒng)在工程上的可實(shí)現(xiàn)性。
[Abstract]:Location information is an important information resource, the application of location information is related to national defense, medicine, industry and all aspects of our lives. Since the popularity of satellite positioning systems such as GPS, positioning chips have been implanted in a wide variety of electronic products, including the current mass use of smartphones. Positioning function began to enter the lives of ordinary people. The popularity of smart handheld devices gave birth to new needs of life. Location Based Services (LBSs) emerge as the times require. LBS has gradually become a research hotspot in academia and industry. The outdoor positioning technology has been very mature due to the satellite positioning system, but microwave signal can not penetrate the wall into the room. The main problem discussed in this paper is this new orientation of localization technology-indoor positioning technology. In this paper, the wireless local area network (WLAN) in indoor environment (. Wireless Local Area Network. The characteristics of WLAN) signal propagation are investigated and the signal intensity collected is analyzed. The distribution and time-varying characteristics of signal intensity are briefly summarized, and the received signal intensity based on WLAN environment is described in detail. Received Signal Strength. RSS-based fingerprint location theory, including the main logical model of the location system and the main location algorithm. The indoor positioning system and information retrieval system are compared. It can also be divided into online stage and off-line stage. At the same time, some viewpoints of data mining are introduced, according to the functional characteristics of data mining application in knowledge discovery. The theoretical system of data mining is integrated into the research of fingerprint indoor location. This paper focuses on the study of weighted K-nearest Neighbors in the on-line phase of the indoor positioning system. The WKNN) algorithm is used as the location performance of the localization algorithm. According to the statistical analysis of the training data, the selected values of the location algorithm parameters are given. At the off-line stage, the organizational form of fingerprint data is optimized, and the fingerprint data is clustered and managed in order to reduce the computation of searching information in online location. Through the improved K-means clustering algorithm suitable for anchor fingerprint data structure, the clustering results of off-line fingerprint database are obtained. The method of selecting the best clustering parameters in a specific environment is given in this paper. The performance analysis formulas related to off-line fingerprint database clustering in indoor positioning are analyzed in order to better understand the indoor positioning problem. Finally, the paper describes the development results of indoor positioning system. This system is a handheld hardware system based on embedded platform, using USB wireless network card to collect data, the localization algorithm is realized by pure software, and the positioning effect is close to the theoretical value. Therefore, it proves the engineering realizability of the fingerprint indoor positioning system.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.13;TN925.93

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