Positioning Algorithm Based on the Fingerprint Database by Twice-Fuzzy Clustering in the High-Speed Railway Scenario

Abstract

When the wireless communication network is optimized for the high-speed railway scenario (HSRS), GPS connections are prone to frequent interruptions. The causes of this phenomenon are analyzed after collecting a large quantity of measured data. A positioning algorithm based on the fingerprint database by twice-fuzzy clustering is proposed to obtain the locations of the terminal inside the carriage of high-speed train (HST) in real time. After collecting more than 300,000 sampled data of both network characteristics and location information, the database of original fingerprints has been constructed. The identification and elimination of abnormal fingerprints are helpful to improve the quality of the fingerprint database. The longitude and latitude of the terminal, which is losing the signals of GPS, can be calculated by setting the fingerprint integral counter, threshold value and weighted measurement eigenvalues and by constructing a matrix of dissimilarity. The experimental results show that the proportion of similar fingerprints between 15 and 200 by once-fuzzy clustering is as high as 90.81%; Additionally, the number of over 95.57% of the similar fingerprints is between 1-20 by twice-fuzzy clustering. The proportion of samples with positioning accuracy less than 10 m is 63.33%, and those less than 5 m account for 41.67%. The average positioning accuracy of the proposed algorithm is 9.02 m, which is suitable for acquiring location information when the signals of GPS are losing in HSRS.

Keywords

• Clustering algorithms
• dissimilarity matrix
• fingerprint database
• fuzzy set theory
• location information