【摘要】：傳統(tǒng)的GPS等定位技術(shù)在室外已經(jīng)能夠?qū)崿F(xiàn)米級(jí)的精確定位,但在室內(nèi)環(huán)境下難以精確定位。隨著移動(dòng)智能終端的普及和室內(nèi)廣泛覆蓋的WiFi為低成本、高精度的室內(nèi)定位技術(shù)提供了可能。其中基于位置指紋識(shí)別的WiFi室內(nèi)定位以其實(shí)現(xiàn)簡(jiǎn)單、成本低、定位精度高等優(yōu)勢(shì)成為室內(nèi)定位技術(shù)的研究熱點(diǎn)。本文深入的研究了基于位置指紋識(shí)別的WiFi室內(nèi)定位算法,為了進(jìn)一步提高該算法的定位精度和定位速度,從離線階段和在線階段提出算法的改進(jìn)。離線階段,隨著室內(nèi)定位環(huán)境的增大,采集的指紋點(diǎn)也相應(yīng)增多,在線階段的匹配計(jì)算量就會(huì)大大增加。考慮到定位的實(shí)時(shí)性,將數(shù)據(jù)挖掘中的聚類算法應(yīng)用到離線階段數(shù)據(jù)庫的處理。本文提出對(duì)二分Kmeans聚類算法進(jìn)行改進(jìn),改進(jìn)算法將聚類相似度定義為信號(hào)強(qiáng)度與坐標(biāo)兩者歐氏距離的乘積。通過該算法聚類后,有效改善了聚類后指紋對(duì)應(yīng)坐標(biāo)個(gè)別離散的情況,改善了聚類的效果,提高了定位精度。在線階段,本文提出了基于加權(quán)歐氏距離的自適應(yīng)K值的WKNN算法。該算法對(duì)檢測(cè)到待定位點(diǎn)的AP的信號(hào)強(qiáng)度加權(quán),然后計(jì)算待定位點(diǎn)的指紋與數(shù)據(jù)庫中指紋的加權(quán)歐氏距離,篩選出K個(gè)鄰近點(diǎn)后去除其中的離散點(diǎn),對(duì)剩下的點(diǎn)進(jìn)行加權(quán)求平均。本文還針對(duì)聚類邊界定位點(diǎn)提出改進(jìn)算法,有效改善了聚類邊界定位點(diǎn)的定位精度。實(shí)驗(yàn)結(jié)果表明,通過這兩個(gè)階段對(duì)算法的改進(jìn),有效的改善了定位精度,減小了定位時(shí)的計(jì)算量。改進(jìn)后算法平均定位誤差為1.24米,相比于傳統(tǒng)的WKNN,定位誤差減小了0.47米,可獲得27.2%以上的定位誤差的改善。匹配指紋數(shù)據(jù)量減小了(Q-1)/Q,定位時(shí)間相應(yīng)的縮短(Q-1)/Q,Q是聚類的數(shù)量。最后本文將提出的算法應(yīng)用到車庫導(dǎo)航系統(tǒng)中,其中定位系統(tǒng)模塊采用C/S架構(gòu),包括客戶端,服務(wù)器,數(shù)據(jù)庫三個(gè)模塊的設(shè)計(jì)與實(shí)現(xiàn)。本文的研究可以為基于位置指紋識(shí)別的WiFi定位算法的進(jìn)一步研究提供理論支持,同時(shí)也為各大定位及導(dǎo)航系統(tǒng)中確定當(dāng)前位置提供相應(yīng)的算法支持。
[Abstract]:Traditional GPS and other positioning technologies have been able to achieve accurate positioning of meters outside, but it is difficult to locate accurately in indoor environment. With the popularity of mobile intelligent terminals and the extensive indoor coverage of WiFi, it is possible for low cost and high precision indoor positioning technology. Among them, WiFi indoor location based on position fingerprint recognition has become the research focus of indoor positioning technology because of its simple implementation, low cost and high positioning accuracy. In this paper, the WiFi indoor location algorithm based on position fingerprint recognition is deeply studied. in order to further improve the positioning accuracy and speed of the algorithm, the improvement of the algorithm is proposed from the offline stage and the online stage. In the off-line stage, with the increase of indoor positioning environment, the number of fingerprint points collected increases correspondingly, and the amount of matching computation in the online stage will be greatly increased. Considering the real-time performance of location, the clustering algorithm in data mining is applied to the processing of offline database. In this paper, an improved binary Kmeans clustering algorithm is proposed. The clustering similarity is defined as the product of signal strength and Euclidean distance between coordinates. After clustering, the clustering algorithm effectively improves the individual discretization of the corresponding coordinates of fingerprint after clustering, improves the effect of clustering, and improves the positioning accuracy. In the online phase, an adaptive K-value WKNN algorithm based on weighted Euclidean distance is proposed. In this algorithm, the signal strength of AP detected is weighted, and then the weighted Euclidean distance between the fingerprint of the unlocated point and the fingerprint in the database is calculated. The K adjacent points are selected and the discrete points are removed, and the remaining points are weighted and averaged. This paper also proposes an improved algorithm for clustering boundary location, which effectively improves the positioning accuracy of clustering boundary location. The experimental results show that through the improvement of the algorithm in these two stages, the positioning accuracy is effectively improved and the computational complexity is reduced. The average positioning error of the improved algorithm is 1.24 meters, which is reduced by 0.47 meters compared with the traditional WKNN, positioning error, and more than 27.2% of the positioning error can be improved. The matching fingerprint data volume decreases (Q 鈮，

本文編號(hào)：2504465