【摘要】：無(wú)線局域網(wǎng)作為寬帶有線接入網(wǎng)的補(bǔ)充應(yīng)用越來(lái)越廣泛，同時(shí)也催生了以無(wú)線局域網(wǎng)為基礎(chǔ)的各類(lèi)服務(wù)如WLAN室內(nèi)定位服務(wù)等。而基于位置指紋的WLAN室內(nèi)定位系統(tǒng)以其操作及設(shè)備簡(jiǎn)單等特點(diǎn)而成為研究熱點(diǎn)。因而本文將基于位置指紋的WLAN室內(nèi)定位方法作為主要研究?jī)?nèi)容，并通過(guò)改進(jìn)該方法提高定位準(zhǔn)確度和定位所需時(shí)間。 基于位置指紋的WLAN室內(nèi)定位一般分兩個(gè)階段：離線階段Radio Map的建立和在線定位階段。在離線階段，通過(guò)實(shí)測(cè)得到參考點(diǎn)的位置信息及相應(yīng)的RSS值形成Radio Map；在線階段使用特征匹配算法計(jì)算出在線測(cè)得數(shù)據(jù)的物理位置�；谖恢弥讣y的定位算法需要解決兩個(gè)問(wèn)題：定位的準(zhǔn)確性和時(shí)效性。因而本文研究了聚類(lèi)算法、AP選擇算法及Radio Map更新算法。 首先，本文分析了現(xiàn)有的基于Radio Map的WLAN室內(nèi)定位的特點(diǎn)，根據(jù)其關(guān)鍵的兩個(gè)環(huán)節(jié)即Radio Map的建立及特征匹配算法進(jìn)行分析。位置指紋的創(chuàng)建方法有兩種，即自由空間傳播模型法和接收到RSS值的特征值法，本文選用RSS特征值法。RSS值隨著時(shí)間，天線朝向，參考點(diǎn)位置變化而變化，因而需要選用合理的方法建立Radio Map。特征匹配算法中，包括最簡(jiǎn)單的最近鄰算法、經(jīng)典的K近鄰算法和加權(quán)K近鄰算法。 其次，本文通過(guò)分析Radio Map，研究如何對(duì)Radio Map進(jìn)行化簡(jiǎn)及更新操作。為了定位的時(shí)效性，本文首先對(duì)Radio Map進(jìn)行聚類(lèi)處理，將RadioMap劃分為幾個(gè)小類(lèi)，然后在每一個(gè)小類(lèi)中使用AP選擇算法選擇出合適的AP組合用于定位。在聚類(lèi)算法中，研究了最簡(jiǎn)單的K均值聚類(lèi)算法、引入隸屬度概念的模糊K均值聚類(lèi)算法和無(wú)需指定初始聚類(lèi)數(shù)的仿射傳播聚類(lèi)算法；在AP選擇算法中，研究了隨機(jī)選擇及均值最大選擇AP方法、信息熵增益方法和互信息熵方法。最后，為定位的準(zhǔn)確性，研究了基于隱馬爾科夫模型的Radio Map更新方法，，并使用EM算法對(duì)隱馬爾科夫模型進(jìn)行求解。 最后，通過(guò)在真實(shí)環(huán)境下的實(shí)驗(yàn)仿真，利用特征匹配算法進(jìn)行定位。對(duì)聚類(lèi)算法、AP選擇算法及Radio Map更新算法進(jìn)行了性能分析，并基于實(shí)驗(yàn)環(huán)境選擇了合適的參數(shù)以期達(dá)到定位準(zhǔn)確度高及定位時(shí)間短的特點(diǎn)。
[Abstract]:WLAN is more and more widely used as a supplement to broadband wired access network. At the same time, WLAN services such as WLAN indoor positioning services are given birth to. The WLAN indoor positioning system based on position fingerprint has become a research hotspot because of its simple operation and equipment. Therefore, the WLAN indoor location method based on location fingerprint is taken as the main research content in this paper, and the accuracy and time of location are improved by improving the method. WLAN indoor location based on position fingerprint is generally divided into two stages: the establishment of Radio Map and the online location. In the off-line phase, the position information of the reference point and the corresponding RSS value are measured to form the Radio Map; online phase. The physical position of the on-line measured data is calculated by using the feature matching algorithm. The localization algorithm based on location fingerprint needs to solve two problems: accuracy and timeliness. Therefore, clustering algorithm, AP selection algorithm and Radio Map update algorithm are studied in this paper. Firstly, this paper analyzes the characteristics of existing WLAN indoor location based on Radio Map, and analyzes its two key links, namely, the establishment of Radio Map and the feature matching algorithm. There are two methods to create position fingerprint, that is, free space propagation model method and eigenvalue method that receives RSS value. In this paper, RSS eigenvalue method is used. The RSS value changes with time, antenna orientation and reference point position. Therefore, it is necessary to select a reasonable method to establish Radio Map.. The feature matching algorithms include the simplest nearest neighbor algorithm, the classical K nearest neighbor algorithm and the weighted K nearest neighbor algorithm. Secondly, this paper studies how to simplify and update Radio Map by analyzing Radio Map,. In order to get the timeliness of the localization, the Radio Map is first clustered, the RadioMap is divided into several subclasses, and then the appropriate AP combination is selected by using the AP selection algorithm in each subclass. In the clustering algorithm, the simplest K-means clustering algorithm is studied, the fuzzy K-means clustering algorithm based on membership degree and the affine propagation clustering algorithm without specifying the initial clustering number are introduced. In the AP selection algorithm, the random selection and the mean maximum selection AP method, the information entropy gain method and the mutual information entropy method are studied. Finally, for the accuracy of location, the Radio Map updating method based on Hidden Markov Model is studied, and the EM algorithm is used to solve the Hidden Markov Model. Finally, through the real-time simulation, the feature matching algorithm is used to locate the location. The performance of clustering algorithm, AP selection algorithm and Radio Map update algorithm are analyzed, and the suitable parameters are selected based on the experimental environment in order to achieve the characteristics of high localization accuracy and short localization time.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN925.93

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