Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests

Xiansheng Guo, Nirwan Ansari, Lin Li, Huiyong Li

Research output: Contribution to journalArticlepeer-review

82 Scopus citations


Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.

Original languageEnglish (US)
Article number8304581
Pages (from-to)4686-4698
Number of pages13
JournalIEEE Internet of Things Journal
Issue number6
StatePublished - Dec 2018

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Group of fingerprints (GOOFs)
  • Multiple antennas
  • Random forests (RFs)
  • Sliding window aided mode-based (SWIM) fusion
  • Universal software radio peripheral (USRP)


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