Triplet MatchNet Based Indoor Position Method Using CSI Fingerprint Similarity Comparison

IEEE Transactions on Vehicular Technology, vol. 72, pp. 16905–16910

Abstract

In this article, we propose a few-shot indoor position method based on Triplet Matchnet, which transforms coordinate positioning into channel state information (CSI) similarity matching problem. Triplet loss is designed to train and learn hidden correspondence between CSI features and physical space positions, with emphasis on minimizing distance or angle-based triplet loss. Then, according to pre-trained network with best similarity match, a similarity score map of CSI with unknown coordinates is constructed to predict position precisely. Experimental results show that angle-based triplet loss can obtain more accurate CSI fingerprint similarity matching accuracy. Compared with existing methods, experiment results confirm that our proposed method can achieve excellent positioning performance with few-shot datasets.

Authors 4

1. Hao Gu

   Southeast University

   Affiliation as printed

   National ASIC System Engineering Research Center, School of Electronic Science and Engineering, Southeast University, Nanjing, China

2. Jun Yang

   Southeast University

   Affiliation as printed

   National ASIC System Engineering Research Center, School of Electronic Science and Engineering, Southeast University, Nanjing, China

3. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

4. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany