| 英文标题 | Research on method of autocorrelation filtering and noise reduction for radio fingerprint samples of underground WLAN |
| 作者英文名 | Song Mingzhi, Qian Jiansheng |
| 机构英文名 | School of Information & Control Engineering,China University of Mining & Technology,Xuzhou Jiangsu 221000,China |
| 英文摘要 | In order to solve the influence of noise samples in the underground WLAN radio fingerprint database on the personnel positioning accuracy, this paper proposed a new autocorrelation filtering algorithm based on sampling interval <i>τ</i>. The research shows that although the received signal strength(RSS) sequences collected by the same mobile terminal are different at the same reference point and different time, there are significant differences of RSS between noise samples and other samples. Using this feature, this filtering algorithm based on sampling interval <i>τ</i> was used the mean of the samples as the balance reference of the fluctuation difference between two samples, which enlarged the anomalous characteristics of the noise samples, so that the possible noise samples could be filtered more accurately. The radio fingerprint database which filtered out the noise samples could better represent the RSS distribution at each reference point. With 90% confidence probability, the experimental results show that the location errors of static and dynamic personnel positioning using radio fingerprint samples filtered by autocorrelation filtering algorithm are 3 m and 3.5 m respectively, which are 1 m and 0.5 m less than those using original radio fingerprint samples. |
| 英文关键词 | underground personnel positioning; radio fingerprint; autocorrelation filtering; sample fluctuation difference |