Fingerprint Augment Based on Super-Resolution for WiFi Fingerprint Based Indoor Localization

Abstract

WiFi fingerprint based indoor localization has become a key research direction in the field of indoor localization due to its high positioning accuracy and low equipment deployment cost. Increasing the number of reference point collected offline can improve the positioning accuracy, however it yields excessive cost of offline collection. Fingerprint augment is an effective solution to reduce the cost while ensuring the positioning accuracy. In this paper, we are pioneering to propose a fingerprint augment framework based on super-resolution (FASR), which achieves the fusion of fingerprint augment and super-resolution based on mutual conversion between fingerprint data and fingerprint image. The processing framework of FASR is formulated and the implementation of three modules in FASR are given, including Fingerprint-To-Image Conversion module, Super-Resolution module and Image-To-Fingerprint Conversion module. Simulated and real data experiments reveal the feasibility and effectiveness of the FASR. In addition, we explore the impact of two key engineering parameters on the performance of the FASR method. Our work demonstrates the new application of super-resolution in image processing field in wireless indoor localization topics.