本文選題：WLAN室內(nèi)定位　切入點：位置指紋　出處：《江蘇科技大學》2017年碩士論文

【摘要】：隨著數(shù)據(jù)業(yè)務和多媒體業(yè)務在社會生活中應用的日益頻繁,基于位置的服務(Location-Based Services,LBS)在人們的需求中呈現(xiàn)出明顯的上升趨勢。尤其是在地下停車場、機場大廳、購物中心等場景,常常需要對室內(nèi)物體進行精確定位,傳統(tǒng)的衛(wèi)星定位技術(shù)采用微波傳輸,由于多徑干擾、非視距傳輸、信號衰減等原因,信號難以穿過復雜的建筑物外層,無法滿足室內(nèi)定位的要求。隨著室內(nèi)無線信號設施被廣泛部署,基于WLAN的室內(nèi)定位技術(shù)成為近年來定位領域中的重要研究方向。本文對WLAN室內(nèi)定位技術(shù)進行了研究,首先分析了WLAN室內(nèi)定位技術(shù)的研究現(xiàn)狀與發(fā)展趨勢,對來自接入點(Access Point,AP)的信號接收強度(Received Signal Strength,RSS)傳播特性進行了實地測量研究。在此基礎上,對位置指紋特征匹配算法和AP選擇算法進行分析,并分別提出改進算法,提高了室內(nèi)定位精度,具體工作如下:(1)研究RSS信號的傳播特性:在實際定位環(huán)境下,對RSS信號進行了實地測量與分析。分別以采樣時長、人員走動、AP選取、采樣點位置為變量,研究RSS概率分布情況。另外,通過設計實驗研究了采樣網(wǎng)格尺寸和AP數(shù)量對定位效果的影響,確定了適用于本文系統(tǒng)的指紋網(wǎng)格邊長與最佳AP選擇數(shù)量。(2)研究指紋特征匹配算法:提出了一種基于貝葉斯算法的改進特征匹配算法。引入?yún)^(qū)域劃分的概念,將定位空間內(nèi)所有參考點進行聚類分簇,解決了樸素貝葉斯算法僅靠參考點后驗概率估算位置的不足。通過仿真實驗,驗證了改進算法的有效性,2米精度范圍內(nèi)的累積概率,由改進前的60%提升至70%。(3)研究AP選擇算法:提出了一種基于信息增益的AP選擇算法。在InfoGain算法的基礎上,引入鄰近AP間的差異性判定,與信息增益值進行加權(quán),共同量化AP的綜合可辨識性,并以此為標準選擇最具位置辨識能力的AP參與定位計算。通過仿真實驗,所提的AP選擇算法能夠有效去除冗余AP信息;與特征匹配算法聯(lián)合運用,降低了定位復雜度,與InfoGain算法相比,2米精度范圍內(nèi)的累積概率,由78%提升至85%。
[Abstract]:With the increasing use of data services and multimedia services in social life, location-based services (LBSs) show an obvious upward trend in people's needs, especially in underground parking lots, airport halls, shopping centers, etc. It is often necessary to accurately locate indoor objects. The traditional satellite positioning technology uses microwave transmission. Because of multipath interference, non-line-of-sight transmission, signal attenuation and other reasons, the signal is difficult to pass through the outer layer of complex buildings. The indoor location technology based on WLAN has become an important research direction in the field of localization in recent years with the widespread deployment of indoor wireless signal facilities. In this paper, the indoor positioning technology of WLAN has been studied. Firstly, the research status and development trend of WLAN indoor positioning technology are analyzed, and the propagation characteristics of received Signal received signal from access Point WLAN are studied in the field. The location fingerprint feature matching algorithm and AP selection algorithm are analyzed, and the improved algorithms are proposed to improve the indoor positioning accuracy. The specific work is as follows: 1) study the propagation characteristics of RSS signal: in the actual location environment, The field measurement and analysis of RSS signal are carried out. The probability distribution of RSS is studied by taking the sampling time, the selection of personnel walking AP and the location of sampling points as variables, respectively. The effects of sampling mesh size and AP number on the location effect are studied through design experiments. The fingerprint feature matching algorithm is studied. An improved feature matching algorithm based on Bayesian algorithm is proposed, and the concept of region partition is introduced. By clustering all reference points in the location space, the deficiency of naive Bayes algorithm to estimate the location only by a posteriori probability of reference points is solved. The effectiveness of the improved algorithm is verified by simulation experiments, and the cumulative probability in the precision range of 2 meters is verified. This paper studies the AP selection algorithm from 60% to 70% before the improvement. An AP selection algorithm based on information gain is proposed. Based on the InfoGain algorithm, the difference between adjacent AP is determined and weighted with the information gain. The synthesis identifiability of AP is quantized, and the AP with the most ability of position identification is selected as the standard to participate in the location calculation. Through the simulation experiment, the proposed AP selection algorithm can effectively remove redundant AP information and be used in conjunction with the feature matching algorithm. Compared with the InfoGain algorithm, the cumulative probability in the precision range of 2 meters is reduced from 78% to 85%.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN925.93

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