本文選題：位置指紋定位 + LTE網(wǎng)絡(luò)��； 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：隨著移動(dòng)設(shè)備的數(shù)量不斷增加,手機(jī)制造商和服務(wù)提供商正在努力向用戶推出新的功能和服務(wù),其中基于位置的服務(wù)(LBS)獲得了移動(dòng)用戶和服務(wù)提供商的更多關(guān)注。LTE網(wǎng)絡(luò)的快速發(fā)展使得人們對(duì)智能終端的定位需求也越來越高。位置指紋定位技術(shù)僅僅采用無線信號(hào)特征就可以提供定位服務(wù),方法簡便而且能夠克服建筑物的干擾,因此成為近年來研究的熱點(diǎn)之一。本文結(jié)構(gòu)安排如下:首先,分析國內(nèi)外關(guān)于位置指紋定位技術(shù)的研究成果,對(duì)LTE網(wǎng)絡(luò)中的定位技術(shù)進(jìn)行總結(jié)分析。其次,結(jié)合位置指紋定位技術(shù)的特點(diǎn)對(duì)LTE網(wǎng)絡(luò)定位架構(gòu)進(jìn)行完善,設(shè)計(jì)了一種在網(wǎng)絡(luò)中嵌入位置指紋定位功能的LTE網(wǎng)絡(luò)定位架構(gòu),并詳細(xì)設(shè)計(jì)了 LTE網(wǎng)絡(luò)下位置指紋定位系統(tǒng)功能模塊結(jié)構(gòu)、位置指紋定位邏輯功能、位置指紋定位類型的LPP消息結(jié)構(gòu)以及位置指紋定位流程。再次,分別利用四種不同濾波方式對(duì)指紋數(shù)據(jù)的隨機(jī)性進(jìn)行了預(yù)處理。針對(duì)指紋數(shù)據(jù)庫數(shù)據(jù)量龐大的問題,本文提出了一種指紋數(shù)據(jù)動(dòng)態(tài)二次搜索算法,大大降低了定位響應(yīng)時(shí)間。然后分別從噪聲、指紋間隔、K值等影響定位性能的主要因素對(duì)最近鄰法(NN)、K最近鄰法(KNN)、K加權(quán)最近鄰法(WKNN)以及貝葉斯概率法進(jìn)行了仿真分析,針對(duì)WKNN算法提出了一種基于皮爾遜相關(guān)系數(shù)的改進(jìn)匹配定位算法(ImWKNN),結(jié)果表明改進(jìn)算法在定位精度、穩(wěn)定性方面均有提升。然后,在算法研究的基礎(chǔ)上本文設(shè)計(jì)了一種室外位置指紋定位演示系統(tǒng),該系統(tǒng)采用客戶端/服務(wù)器模式,Android客戶端進(jìn)行位置指紋采集與定位結(jié)果顯示,定位服務(wù)器進(jìn)行指紋存儲(chǔ)與定位計(jì)算。最后,搭建系統(tǒng)的測試環(huán)境,建立離線階段的指紋數(shù)據(jù)庫,驗(yàn)證系統(tǒng)的定位功能及性能,測試結(jié)果表明在指紋間隔約為20米的情況下,定位精度基本滿足E911的定位要求。
[Abstract]:As the number of mobile devices continues to grow, mobile phone manufacturers and service providers are working to introduce new features and services to users. Among them, location-based services (LBS) have attracted more attention from mobile users and service providers. With the rapid development of LTE network, people need more and more intelligent terminals. Location fingerprint location technology can provide location services only by using wireless signal features. The method is simple and can overcome the interference of buildings, so it has become one of the hot research topics in recent years. The structure of this paper is as follows: firstly, this paper analyzes the research results of location fingerprint location technology at home and abroad, and summarizes and analyzes the location technology in LTE network. Secondly, according to the characteristics of location fingerprint location technology, the LTE network location architecture is improved, and a LTE network location architecture with location fingerprint location function embedded in the network is designed. The function module structure of the location fingerprint location system, the location fingerprint location logic function, the LPP message structure of the location fingerprint location type and the location fingerprint location flow are designed in detail. Thirdly, four different filtering methods are used to preprocess the randomness of fingerprint data. In order to solve the problem of large amount of data in fingerprint database, a dynamic quadratic search algorithm for fingerprint data is proposed in this paper, which greatly reduces the time of location response. Then, the paper simulates the nearest neighbor method (NN) and K-weighted nearest neighbor method (WKNN) and Bayesian probability method, respectively, from the main factors of noise, fingerprint interval K value and so on, which affect the location performance of the nearest neighbor method (NN) and the K-nearest neighbor method (KNN). An improved matching location algorithm based on Pearson correlation coefficient (ImWKNN) is proposed for WKNN algorithm. The results show that the improved algorithm can improve the accuracy and stability of the algorithm. Then, based on the research of the algorithm, an outdoor location fingerprint location demonstration system is designed in this paper. The system adopts the client / server mode and Android client to collect the location fingerprint and display the location result. The location server carries on the fingerprint storage and the localization computation. Finally, the testing environment of the system is set up, and the fingerprint database in off-line stage is established to verify the location function and performance of the system. The test results show that the positioning accuracy can basically meet the requirements of E911 when the fingerprint interval is about 20 meters.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 張明華;張申生;曹健;;無線局域網(wǎng)中基于信號(hào)強(qiáng)度的室內(nèi)定位[J];計(jì)算機(jī)科學(xué);2007年06期

2 王蕾;廖鑫;姚銳;黃幫明;;LTE復(fù)雜場景下的無線傳播模型校正研究[J];電視技術(shù);2014年23期

，

本文編號(hào)：2062962