本文關(guān)鍵詞：基于RSS指紋的室內(nèi)定位方法　出處：《湘潭大學(xué)》2016年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 位置指紋 室內(nèi)定位 解耦 定位精度 RSS 無(wú)線局域網(wǎng)

【摘要】：近年來(lái),基于無(wú)線接入點(diǎn)(Access Point,AP)接收信號(hào)強(qiáng)度(Received Signal Strength,RSS)的位置指紋(Location Fingerprinting,LF)室內(nèi)定位技術(shù)已成為國(guó)內(nèi)外位置感知研究的熱點(diǎn)�；赗SS指紋的室內(nèi)定位技術(shù)通過(guò)利用在感興趣區(qū)域(Range of Interest,ROI)采集到的來(lái)自各個(gè)AP的RSS值來(lái)推斷觀察者或場(chǎng)景內(nèi)物體的坐標(biāo),且事先不需知道AP的位置。此外基于位置指紋的定位技術(shù)不需要添加額外的硬件支持,可以方便的應(yīng)用到移動(dòng)設(shè)備中,展現(xiàn)出明顯的優(yōu)勢(shì)。論文深入研究了基于RSS的室內(nèi)定位技術(shù),并分析了提高定位精度的方法。本文提出基于位置指紋的解耦室內(nèi)定位,通過(guò)對(duì)X軸和Y軸獨(dú)立地進(jìn)行定位決策,以期在減少?zèng)Q策代價(jià)的同時(shí)提高定位精度。論文主要工作如下:首先,給出了基于RSS的無(wú)線局域網(wǎng)(Wireless Local Area Network,WLAN)室內(nèi)定位系統(tǒng)的研究依據(jù)。并分析了幾種基于WLAN的室內(nèi)定位系統(tǒng)的優(yōu)缺點(diǎn),對(duì)比得出基于LF的定位技術(shù)是目前應(yīng)用最多、發(fā)展前景最好的方法。其次,分析概括了定位技術(shù)的理論基礎(chǔ)。本文先從WLAN網(wǎng)絡(luò)的組成、拓?fù)浣Y(jié)構(gòu)及工作模式簡(jiǎn)要描述了無(wú)線局域網(wǎng)。在此基礎(chǔ)上,給出當(dāng)前應(yīng)用較為廣泛的幾種在WLAN環(huán)境下的室內(nèi)定位技術(shù),并重點(diǎn)闡述了LF定位原理。然后介紹了三種應(yīng)用最多的定位算法,即K近鄰(K-nearest Neighbor,K-NN)法、樸素貝葉斯法和支持向量機(jī)(Support Vector Machine,SVM)法。接下來(lái),實(shí)現(xiàn)了基于最小二乘支持向量機(jī)(Least Squares Support Vector Machine,LS-SVM)的LF室內(nèi)定位。本文首先提出基于LS-SVM的指紋定位模型,再給出了LS-SVM指紋樣本訓(xùn)練的具體實(shí)現(xiàn)過(guò)程。接下來(lái)詳細(xì)闡述了如何利用一對(duì)一和一對(duì)多方法將多分類(lèi)問(wèn)題轉(zhuǎn)化為多個(gè)二值分類(lèi)問(wèn)題。仿真結(jié)果表明,較傳統(tǒng)SVM、K-NN方法的分類(lèi)準(zhǔn)確率高且計(jì)算代價(jià)小。最后,實(shí)現(xiàn)了基于RSS指紋的解耦室內(nèi)定位。文章首先提出了軸向解耦位置指紋定位框架,然后給出了解耦定位的訓(xùn)練與預(yù)測(cè)的具體實(shí)現(xiàn)過(guò)程;最后將LS-SVM、SVM、K-NN等分類(lèi)器應(yīng)用到解耦定位框架中,實(shí)驗(yàn)結(jié)果驗(yàn)證了解耦定位方法的有效性。
[Abstract]:In recent years, location Fingerprinting (Location Fingerprinting, LF) indoor location technology based on Access Point (AP) Received Signal Strength has become a hot research topic in location awareness at home and abroad. The indoor location technology based on RSS fingerprint is used to infer the coordinates of objects in observers or scenes by using RSS values collected from Range of Interest (ROI) collected in different regions, and do not need to know the location of AP in advance. In addition, location technology based on location fingerprint does not require additional hardware support, which can be easily applied to mobile devices, showing obvious advantages. This paper studies the indoor positioning technology based on RSS, and analyzes the methods to improve the positioning accuracy. In this paper, a location based fingerprint decoupling indoor location is proposed. Location decision is made independently by X axis and Y axis, in order to reduce the cost of decision-making and improve positioning accuracy. The main work of this paper is as follows: first, the research basis of the Wireless Local Area Network (WLAN) indoor location system based on RSS is given. The advantages and disadvantages of several indoor location systems based on WLAN are analyzed. It is concluded that the location technology based on LF is the most widely applied and the best way of development. Secondly, the theoretical basis of positioning technology is analyzed and summarized. This paper briefly describes the wireless LAN from the composition, topology and working mode of the WLAN network. On this basis, several kinds of indoor positioning technology which are widely used in WLAN environment are given, and the principle of LF positioning is emphasized. Then we introduce three most widely applied location algorithms, namely K K-nearest (Neighbor), K-NN, naive Bayes and support vector machine (Support Vector Machine). Next, the LF indoor location based on the Least Squares Support Vector Machine (LS-SVM) is implemented. This paper first proposes a LS-SVM based fingerprint location model, and then gives the specific implementation process of LS-SVM fingerprint sample training. Then we elaborate on how to transform the multi classification problem into multiple two value classification problems by one to one and one to many methods. The simulation results show that the classification accuracy of the traditional SVM and K-NN methods is high and the calculation cost is small. Finally, the decoupling indoor location based on RSS fingerprint is realized. This paper first puts forward the axial decoupling of fingerprint positioning frame, and then gives the concrete realization of the process of training and prediction of the decoupling location; LS-SVM, SVM, K-NN classifier is applied to decouple positioning frame, the experiment results validate the effectiveness of decoupling positioning method.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TN925.93
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本文編號(hào)：1347261