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  本文關鍵詞： WLAN 指紋定位法 支持量向量回歸 半監(jiān)督學習 協(xié)同訓練　出處：《重慶郵電大學》2016年碩士論文　論文類型：學位論文

【摘要】：隨著移動智能終端設備的普及和通信技術的快速發(fā)展,基于位置的服務的市場需求越來越大�；谖恢玫姆⻊赵趯Ш�、緊急救援、個性化信息的傳遞等領域發(fā)揮著巨大的作用。對于室外定位技術,主要有以美國的GPS為代表的衛(wèi)星定位技術和利用通信基站的蜂窩網定位技術。然而由于室內環(huán)境復雜,室外定位技術在室內很難滿足人們對定位精度的要求。同時,隨著WLAN設備在室內各種環(huán)境中被廣泛部署,這為WLAN定位技術的發(fā)展和推廣奠定了很好的基礎�；赪LAN的室內定位技術因其較高的定位精度和不需要額外的設備等優(yōu)點成為研究的熱點�；谖恢弥讣y的WLAN定位技術是WLAN定位技術的主流,其分為離線階段和在線階段。本論文正是對基于位置指紋的WLAN定位算法進行研究。首先,由于室內環(huán)境復雜,以及無線信號的傳播特性,室內接收信號具有不確定性和非線性特性。這都對基于位置指紋的WLAN室內技術的定位性能產生了很大的影響。同時,支持向量機在解決小樣本和非線性問題上有很大的優(yōu)勢,且具有很好的泛化能力�；诖�,本文把支持向量回歸引入到室內WLAN指紋定位中,建立RSS信號與物理位置的映射預測模型,以提高定位精度。其次,由于指紋定位在離線階段需要花費大量人力物力采集大量的位置指紋,而獨立于位置的未標記RSS通過移動終端很容易獲得,半監(jiān)督學習能夠很好的利用獨立于位置的RSS信息,減少了對位置指紋的要求,同時能夠提高定位精度。因此,本文引入半監(jiān)督學習協(xié)同訓練算法與支持向量回歸相結合,提出基于半監(jiān)督學習的室內WLAN支持向量回歸定位算法,提高定位精度。最后,對基于半監(jiān)督學習的室內WLAN支持向量回歸定位算法進行改進,改善其性能。本文在仿真環(huán)境和普遍真實室內環(huán)境——辦公環(huán)境和走廊環(huán)境下,對本文提出的算法進行仿真及實驗驗證。通過與傳統(tǒng)算法在性能上的對比,驗證本文提出的定位算法在定位性能上的優(yōu)越性。
[Abstract]:With the popularization of mobile intelligent terminal devices and the rapid development of communication technology, the market demand for location-based services is increasing. The field of personalized information transmission plays a great role. For outdoor positioning technology, there are mainly satellite positioning technology represented by GPS of the United States and cellular network positioning technology using communication base stations. However, because of the complexity of indoor environment, Outdoor positioning technology is very difficult to meet the requirements of positioning accuracy in indoor. At the same time, with the wide deployment of WLAN equipment in various indoor environments, This has laid a good foundation for the development and popularization of WLAN positioning technology. The indoor positioning technology based on WLAN has become a hot spot for its high positioning accuracy and no need of additional equipment. WLAN location based on position fingerprint has become a hot topic. Bit technology is the mainstream of WLAN positioning technology, It is divided into offline phase and online stage. This thesis is to study the location fingerprint based WLAN localization algorithm. Firstly, because of the complexity of indoor environment and the propagation characteristics of wireless signal, Indoor received signals are uncertain and nonlinear, which have great influence on the localization performance of WLAN indoor technology based on position fingerprint. At the same time, support vector machine has great advantages in solving small samples and nonlinear problems. Based on this, support vector regression is introduced into indoor WLAN fingerprint location, and the mapping and prediction model of RSS signal and physical position is established to improve the location accuracy. Because fingerprint location requires a lot of manpower and material resources to collect a large number of location fingerprints, and the location independent RSS can be easily obtained through mobile terminals, semi-supervised learning can make good use of location-independent RSS information. The requirement of location fingerprint is reduced and the location accuracy is improved. Therefore, a semi-supervised learning cooperative training algorithm is combined with support vector regression, and an indoor WLAN support vector regression location algorithm based on semi-supervised learning is proposed in this paper. Finally, the indoor WLAN support vector regression algorithm based on semi-supervised learning is improved to improve its performance. The performance of the proposed algorithm is compared with that of the traditional algorithm, and the superiority of the proposed algorithm in location performance is verified.
【學位授予單位】：重慶郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN925.93

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本文編號：1513331