本文選題：WLAN室內(nèi)定位 + 位置指紋��； 參考：《杭州電子科技大學》2017年碩士論文

【摘要】：隨著無線通訊技術(shù)和智能終端的廣泛應用,使基于位置服務(Localtion Based Service,LBS)的應用逐漸從室外延伸到室內(nèi)。由于室內(nèi)WLAN部署廣泛且移動終端可以容易獲取接收信號強度,使基于位置指紋的WLAN室內(nèi)定位方法受到了國內(nèi)外學者的重視。針對室內(nèi)環(huán)境中RSS信號波動性大以及不同終端信號接收能力差異性的問題,本文通過對RSS特性進行分析,提出一種新穎的位置指紋定位算法,并進一步對指紋算法進行了優(yōu)化研究,最后設(shè)計開發(fā)了室內(nèi)定位應用系統(tǒng)。本文主要工作和創(chuàng)新點如下:(1)針對RSS信號時變性以及不同終端信號接收能力差異性,導致WLAN位置指紋定位不穩(wěn)定的問題,提出基于RSS空間位置線性相關(guān)的定位算法。該算法以離線采集的多組RSS樣本形成的特征矩陣構(gòu)建離線指紋數(shù)據(jù)庫,定位時,通過計算實時RSS矩陣與指紋庫參考點相關(guān)性,得到最相關(guān)的k個參考點,并利用二次加權(quán)質(zhì)心算法計算用戶的最終位置。為了有效降低信號時變性的影響,RSS采樣時進行了濾波、排序等處理,構(gòu)建離線指紋數(shù)據(jù)庫時盡量增加采樣次數(shù),但需要對樣本進行聚合處理以適應定位相關(guān)性計算。(2)為了降低定位匹配計算過程中計算開銷,研究提出適用于RSS空間線性相關(guān)定位算法的位置指紋聚類方法。以RSS特征矩陣作為聚類樣本,利用相關(guān)系數(shù)作為相似性度量標準,通過K-means聚類方法將指紋數(shù)據(jù)庫分割成相對較小的指紋子庫,匹配計算時采用動態(tài)篩選策略對指紋子庫進行判斷,通過匹配被選中的指紋子庫即可估算最終位置,這樣可以縮小匹配計算過程中指紋搜索空間,提升定位系統(tǒng)的效率。(3)不同AP布局會對定位性能造成不同影響,研究以參考點位置指紋區(qū)分度最大化為目的,提出一種AP布局規(guī)劃參考方案。以參考點與相鄰參考點歐氏距離之和表示為參考點指紋區(qū)分度,將所有參考點指紋區(qū)分度之和定義為當前AP布局區(qū)分度SD,以SD最大化為條件來布置AP的位置,這樣可以有效改進系統(tǒng)的定位精度。
[Abstract]:With the wide application of wireless communication technology and intelligent terminal, the application of Localtion Based Service (LBS) is gradually extended from outdoor to indoor.Because the indoor WLAN is widely deployed and the mobile terminal can easily obtain the received signal strength, the WLAN indoor location method based on position fingerprint has been paid more attention by domestic and foreign scholars.In order to solve the problem of high volatility of RSS signal in indoor environment and the difference of receiving ability of different terminal signals, this paper proposes a novel location fingerprint location algorithm by analyzing the characteristics of RSS.Furthermore, the fingerprint algorithm is optimized and the indoor location application system is designed and developed.The main work and innovation of this paper are as follows: (1) aiming at the problem of RSS signal time-varying and different terminal signal receiving ability, which leads to the instability of WLAN location fingerprint location, a location algorithm based on RSS spatial position linear correlation is proposed.In this algorithm, the off-line fingerprint database is constructed from the characteristic matrix of multi-groups of RSS samples collected offline. When locating, the correlation between the real-time RSS matrix and the reference points of the fingerprint database is calculated, and the most relevant k reference points are obtained.The final position of the user is calculated by the quadratic weighted centroid algorithm.In order to effectively reduce the influence of time-varying signal on RSS sampling, filtering and sorting are carried out, and the sampling times are increased when constructing off-line fingerprint database.In order to reduce the computation cost of location matching, a location fingerprint clustering method suitable for RSS spatial linear correlation localization algorithm is proposed.RSS feature matrix is used as clustering sample, correlation coefficient is used as similarity measure, fingerprint database is divided into relatively small fingerprint subdatabase by K-means clustering method.The dynamic screening strategy is used to judge the fingerprint subdatabase, and the final position can be estimated by matching the selected fingerprint subdatabase, which can reduce the fingerprint search space in the course of matching calculation.To improve the efficiency of the positioning system, different AP layouts will have different effects on the location performance. In order to maximize the fingerprint differentiation of reference points, a reference scheme for AP layout planning is proposed in this paper.The sum of Euclidean distance between reference points and adjacent reference points is taken as the fingerprint differentiation degree of reference points. The sum of fingerprint differentiation degrees of all reference points is defined as the current AP layout differentiation degree SD.The position of AP is arranged under the condition of maximum SD.This can effectively improve the positioning accuracy of the system.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN925.93

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