本文選題：數(shù)據(jù)挖掘　切入點：指紋定位　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近些年來,隨著移動互聯(lián)網(wǎng)的迅猛發(fā)展以及智能移動終端的廣泛普及,基于位置的服務(wù)受到越來越多的關(guān)注。但受限于室內(nèi)障礙物較多,空間較為狹小等特點,傳統(tǒng)的室外定位算法無法應(yīng)用于室內(nèi)定位。目前,基于WIFI系統(tǒng)的指紋法室內(nèi)定位技術(shù)以其部署成本低、組網(wǎng)靈活、易于實現(xiàn)、便于擴展等特點逐漸成為研究的熱點。基于WIFI的指紋法室內(nèi)定位通常選擇在定位點上接收到的各個接入點(Access Point,AP)的接收信號強度指示(Received Signal Strength Indicator,RSSI)作為定位的特征指紋,利用RSSI與地理位置之間特殊的映射關(guān)系實現(xiàn)定位。然而RSSI容易受到多徑、衰減和環(huán)境變化的影響,從而導(dǎo)致信號強度測量數(shù)據(jù)難以構(gòu)建一個可信的模型,這是提高室內(nèi)定位精度所面臨的主要挑戰(zhàn),需要利用不斷發(fā)展的新技術(shù)加以改進。為此,本文通過對相關(guān)文獻的查閱,將數(shù)據(jù)挖掘理論引入到室內(nèi)定位中,結(jié)合實地的數(shù)據(jù)采集和分析,進行了如下的研究工作:(1)為分析RSSI作為定位特征指紋所表現(xiàn)出來的特性,在WIFI網(wǎng)絡(luò)環(huán)境中實地采集RSSI樣本,采用理論與實驗相結(jié)合的方法,對環(huán)境中各個AP的RSSI進行驗證。發(fā)現(xiàn)WIFI網(wǎng)絡(luò)中不同AP所發(fā)送信號的RSSI具有不確定性和重復(fù)性,為了準確描繪RSSI與地理位置之間的關(guān)系,需要盡可能多的收集不同AP的RSSI,這會導(dǎo)致定位算法計算量的增加。此外,在室內(nèi)環(huán)境中,為了保證WIFI網(wǎng)絡(luò)的覆蓋和數(shù)據(jù)傳輸質(zhì)量,往往會重復(fù)部署很多AP,這些AP之間的RSSI具有很高的重復(fù)性。為此,本文提出一種基于主成分分析的指紋降維算法,該算法利用特征空間基變換的原理,將原始高維指紋數(shù)據(jù)映射到低維,在降低數(shù)據(jù)維度的同時,去除了不同AP間的冗余信息。經(jīng)驗證,該算法降低了算法復(fù)雜度,提高了定位效率。(2)為了提高定位算法的精度,本文提出了一種基于k層網(wǎng)格參數(shù)尋優(yōu)的支持向量回歸定位模型。針對RSSI的非線性特征,目前的解決方法是使用基于核函數(shù)的支持向量機來構(gòu)建定位模型。但是支持向量機的定位效果受參數(shù)的影響較大,傳統(tǒng)的參數(shù)尋優(yōu)算法效率低,耗時大。為此,通過分析傳統(tǒng)算法效率不佳的原因,采用分層的思想,在不同參數(shù)區(qū)間選擇不同的搜索步長。經(jīng)驗證,本文提出的k層網(wǎng)格參數(shù)尋優(yōu)算法在計算效率上取得了顯著地提升。(3)最后,本文將指紋降維算法和最優(yōu)參數(shù)的支持向量回歸定位模型相結(jié)合。使用實地采集的數(shù)據(jù)進行算法仿真。實驗結(jié)果證明,相較于其他基于數(shù)據(jù)挖掘的定位算法如傳統(tǒng)支持向量機定位算法、KNN定位算法、神經(jīng)網(wǎng)絡(luò)算法,本文提出的算法在定位精度方面,表現(xiàn)出優(yōu)越的性能。
[Abstract]:In recent years, with the rapid development of mobile Internet and the widespread popularity of intelligent mobile terminals, location-based services have attracted more and more attention. The traditional outdoor location algorithm can not be applied to indoor localization. At present, the fingerprint indoor location technology based on WIFI system has the advantages of low deployment cost, flexible networking and easy implementation. The characteristics such as easy to expand and so on have gradually become the research hotspot. The fingerprint method based on WIFI usually selects the received signal strength indication of received Signal Strength indicator (RSSI) from each access point received at the location point as the fingerprint feature of the location. Using the special mapping relationship between RSSI and geographical location, RSSI is easy to be affected by multipath, attenuation and environmental changes, which makes it difficult to build a credible model of signal strength measurement data. This is the main challenge to improve the accuracy of indoor positioning, which needs to be improved by using the new technology. Therefore, this paper introduces the theory of data mining into indoor positioning by consulting related documents. Combined with data acquisition and analysis in the field, the following research work was carried out: 1) in order to analyze the characteristics of RSSI as a location feature fingerprint, RSSI samples were collected in the WIFI network environment, and the method of combining theory with experiment was adopted. The RSSI of each AP in the environment is verified. It is found that the RSSI of different AP signals in WIFI network is uncertain and repetitive. In order to accurately describe the relationship between RSSI and geographical location, The need to collect as many different AP RSSIs as possible, which can lead to an increase in the computation of the location algorithm. In addition, in an indoor environment, in order to ensure the coverage of WIFI networks and the quality of data transmission, Many APs are often repeatedly deployed, and the RSSI between these APs is highly repeatable. In this paper, a fingerprint dimensionality reduction algorithm based on principal component analysis (PCA) is proposed, which utilizes the principle of feature space basis transform. The original high-dimensional fingerprint data is mapped to low-dimensional fingerprint data, and the redundant information between different AP is removed while reducing the data dimension. It is proved that the algorithm reduces the complexity of the algorithm and improves the localization efficiency. In this paper, a support vector regression location model based on k-layer grid parameter optimization is proposed. The current solution is to use kernel function based support vector machine (SVM) to construct localization model. However, the localization effect of SVM is greatly affected by parameters, and the traditional parameter optimization algorithm is inefficient and time-consuming. By analyzing the reasons for the inefficiency of the traditional algorithm and adopting the idea of stratification, different search steps are selected in different parameter intervals. The k-layer mesh parameter optimization algorithm presented in this paper has achieved a significant increase in computational efficiency. In this paper, the dimensionality reduction algorithm of fingerprint is combined with the support vector regression localization model of optimal parameters. The algorithm is simulated using the data collected in the field. The experimental results show that, Compared with other localization algorithms based on data mining, such as traditional support vector machine (SVM) localization algorithm and neural network algorithm, the algorithm presented in this paper shows superior performance in terms of location accuracy.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN92

【引證文獻】

相關(guān)期刊論文 前1條

1 陳詩軍;林利成;徐小龍;陳大偉;王園園;;一種面向位置數(shù)據(jù)隱私保護的離線地磁定位模型[J];南京信息工程大學(xué)學(xué)報(自然科學(xué)版);2017年05期

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本文編號：1587726