基于DBSCAN子空間匹配的蜂窩網(wǎng)室內(nèi)指紋定位算法
doi: 10.11999/JEIT160768 cstr: 32379.14.JEIT160768
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61301126)，長(zhǎng)江學(xué)者和創(chuàng)新團(tuán)隊(duì)發(fā)展計(jì)劃(IRT1299)，重慶市基礎(chǔ)與前沿研究計(jì)劃(cstc2013jcyjA 40041, cstc2015jcyjBX0065)，重慶郵電大學(xué)青年科學(xué)研究項(xiàng)目(A2013-31)
DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm
Funds:
The National Natural Science Foundation of China (61301126), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Fundamental and Frontier Research Project of Chongqing (cstc2013jcyjA40041, cstc2015jcyjBX0065), The Young Science Research Program of Chongging University of Posts and Telecommunications (A2013-31)
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摘要: 針對(duì)無(wú)線(xiàn)信道動(dòng)態(tài)衰落特性引起的蜂窩網(wǎng)室內(nèi)定位誤差較大的問(wèn)題，該文提出基于密度的空間聚類(lèi)(Density Based Spatial Clustering of Applications with Noise, DBSCAN)子空間匹配算法，有效剔除大誤差點(diǎn)，提高定位精度。首先通過(guò)劃分信號(hào)空間，構(gòu)建多個(gè)子空間，在子空間中利用加權(quán)K近鄰匹配算法(Weighted K Nearest Neighbor, WKNN)估計(jì)出目標(biāo)位置；然后利用DBSCAN對(duì)估計(jì)位置進(jìn)行聚類(lèi)以剔除異常點(diǎn)；最后結(jié)合概率模型確定最終估計(jì)位置。實(shí)驗(yàn)結(jié)果表明，基于DBSCAN的子空間匹配算法能有效剔除大誤差點(diǎn)，提高蜂窩網(wǎng)室內(nèi)定位系統(tǒng)的整體性能。Abstract: For the sake of reducing the indoor localization errors caused by dynamic signal fading in cellular network, this paper propose a novel Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based subspace matching algorithm for indoor localization, which can improve the localization accuracy by eliminating the location with large errors. Specifically, the signal space is firstly divided into several subspaces, where a position estimation can be obtained respectively using the Weighted K Nearest Neighbors (WKNN) approach. Then, DBSCAN is applied to the position coordinates obtained from each subspace which eliminates the outliers. Finally, the location is estimated based on probability analysis. Experimental results show that the proposed approach can improve the location accuracy by eliminating the location with large errors.
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