南京大学学报(自然科学版) ›› 2022, Vol. 58 ›› Issue (2): 328–335.doi: 10.13232/j.cnki.jnju.2022.02.016

• • 上一篇    

基于无线传播信道特征的非视距识别技术

刘鑫一, 谢景丽, 王威(), 徐志麟   

  1. 长安大学信息工程学院，西安，710064
  • 收稿日期:2021-10-20 出版日期:2022-03-30 发布日期:2022-04-02
  • 通讯作者: 王威 E-mail:wei.wang@chd.edu.cn
  • 作者简介:E⁃mail：wei.wang@chd.edu.cn
  • 基金资助:
    国家重点研发计划(2020YFB1807001);国家自然科学基金(61871059)

Identification of NLoS based on wireless propagation channel features

Xinyi Liu, Jingli Xie, Wei Wang(), Zhilin Xu   

  1. School of Information Engineering，Chang'an University，Xi'an，710064，China
  • Received:2021-10-20 Online:2022-03-30 Published:2022-04-02
  • Contact: Wei Wang E-mail:wei.wang@chd.edu.cn

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摘要：

复杂的室内环境中存在的各种无法躲避的障碍物会导致无线定位的测距精度较低，其中最主要的因素是存在非视距传播，因此识别信道状态是否为非视距对室内定位精度较为重要.提出一种基于信道信息的视距/非视距信道识别方法：首先对信号进行过滤，获取重要的信道抽头；然后提取过滤后信号的峰值，并计算其功率；最后通过计算得出该信道信号的峰均比，并联合假设检验对信道状态进行判决.仿真结果表明，峰均比特征在视距/非视距信道上有明显差异，可以作为识别视距/非视距信道的特征.该特征的视距识别正确率达到93.56%，非视距识别正确率达到87.23%，比使用峰度特征在视距场景下的识别正确率提高了2.65%，非视距正确率提高了0.71%.使用本算法在定位过程中进行验证，能够有效降低定位误差，提高定位精度，说明该算法的识别效果较好，具有一定的应用前景.

关键词: 无线通信, 信道测量, 室内定位, 非视距区分, 假设检验

Abstract:

Due to the existence of numerous obstacles，the ranging accuracy of wireless positioning is low in the complex indoor environment. The most important factor is the non?line?of?sight propagation. Therefore，it is necessary to identify the line?of?sight and non?line?of?sight condition to enhance the positioning accuracy. This paper proposes a line?of?sight/non?line?of?sight channel identification method based on channel state information. First，the received signal is processed in order to obtain discrete channel information，i.e.，parameters of channel taps. The next step is to extract the peak value of all taps based on which the power is calculated. Thereafter，the peak?to?average ratio of the channel taps is obtained and used to determine the channel status by hypothesis testing. The simulation results show that the peak?to?average ratio feature has a significant difference between line?of?sight and non?line?of?sight channel，which can be used as a feature to identify the line?of?sight and non?line?of?sight channel. The line?of?sight identification accuracy rate of this feature reaches 93.56% and the non?line?of?sight identification accuracy rate reaches 87.23%，which is 2.65% higher than that of kurtosis features in line?of?sight scenes. The non?line?of?sight accuracy rate is increased by 0.71%. Using this algorithm for verification in the positioning process can effectively improve the positioning accuracy. It is shown that the algorithm has a better performance and some application prospects.

Key words: wireless communication, channel measurement, indoor positioning, non?line?of?sight distinction, hypothesis test

中图分类号: 

  • TN925

图1

多路径传播"

图2

不同信道状态下的多路径功率"

图3

实验场景"

图4

测量的真实环境"

图5

LoS/NLoS条件下的CIR统计峰均比特征"

图6

LoS/NLoS条件下的CIR统计特征"

图7

基于LiFi对比模型下不同特征的识别算法"

表1

不同特征的性能分析"

识别方法PtPf
均方根延迟扩展89.77%73.76%
峰度90.91%86.52%
峰均比93.56%87.23%
LiFi模型下峰均比与均方根延迟扩展结合92.37%82.27%
LiFi模型下峰均比与峰度结合93.56%87.23%

图8

信号识别前后定位效果对比图"

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