本文選題：室內(nèi)定位　切入點(diǎn)：蜂窩網(wǎng)　出處：《電子與信息學(xué)報(bào)》2017年05期 　論文類(lèi)型：期刊論文

【摘要】：針對(duì)無(wú)線信道動(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]:In order to solve the problem of large indoor location error caused by the dynamic fading characteristics of wireless channels, this paper proposes a density-based spatial clustering Based Clustering of Applications with Noisegne DBSCAN-based subspace matching algorithm, which can effectively eliminate large error points. Firstly, several subspaces are constructed by dividing the signal space, and then the weighted K Nearest neighbor matching algorithm is used to estimate the target position in the subspace, and then the estimated position is clustered by DBSCAN to eliminate the outliers. The experimental results show that the subspace matching algorithm based on DBSCAN can effectively eliminate large error points and improve the overall performance of indoor positioning system in cellular networks.
【作者單位】： 重慶郵電大學(xué)移動(dòng)通信重點(diǎn)實(shí)驗(yàn)室;
【基金】：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,cstc2015jcyj BX0065) 重慶郵電大學(xué)青年科學(xué)研究項(xiàng)目(A2013-31)~~
【分類(lèi)號(hào)】：TN929.53

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