面向NB-IoT终端的指纹匹配定位改进算法

doi:10.19678/j.issn.1000-3428.0053961

摘要/Abstract

摘要： 窄带物联网具有成本低、功耗小、连接量大和覆盖范围广等特性，但其超低的复杂度和较强的穿透衰落导致定位精度不高。基于信道状态信息（CSI）幅度和窄带参考信号接收功率（NRSRP），提出一种指纹匹配定位算法。利用CSI幅值和NRSRP离线构建指纹，并在线收集待定位终端的指纹信息，采用K近邻（KNN）算法得到最近的K个近邻点，充分利用待定位终端和K个近邻点的NRSRP信息并通过无线信道传播模型估计距离差。在此基础上，使用极大似然估计算法得到最终的估计位置。实验结果表明，与KNN、WKNN等算法相比，该算法能有效降低定位误差，提高定位精度。

关键词: 窄带物联网, 指纹匹配, K近邻, 极大似然估计, 信道状态信息

Abstract: Narrow Band Internet of Things(NB-IoT) is featured by low costs,low energy consumption,large amount of connection and wide coverage.However,its low complexity and strong penetration fading reduces the precision of localization.This paper proposes a localization algorithm using fingerprint matching based on the amplitude of Channel State Information(CSI) and Narrowband Reference Signal Received Power(NRSRP).The algorithm uses the CSI amplitude and NRSRP to construct fingerprints offline,and collects fingerprint information of the to-be-localized terminal online.The K-Nearest Neighbor(KNN) algorithm is used to obtain the nearest K neighboring point.The NRSRP information of the to-be-localized terminal and the K neighboring point is used to estimate the distance difference with the wireless channel transmission model.On this basis,the maximum likelihood estimation algorithm is used to obtain the final estimated position.Experimental results show that compared with KNN,WKNN and other algorithms,the proposed algorithm can effectively reduce the localization errors and improve the localization precision.

Key words: Narrow Band Internet of Things(NB-IoT), fingerprint matching, K-Nearest Neighbor(KNN), maximum likelihood estimation, Channel State Information(CSI)

中图分类号:

TP929.5

彭大芹, 李靖. 面向NB-IoT终端的指纹匹配定位改进算法[J]. 计算机工程, 2020, 46(3): 178-183,191.

PENG Daqing, LI Jing. Improved Localization Algorithm Using Fingerprint Matchingfor NB-IoT Terminals[J]. Computer Engineering, 2020, 46(3): 178-183,191.

https://www.ecice06.com/CN/Y2020/V46/I3/178

图/表 7

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