Channel State Reconstruction Using Multilevel Discrete Wavelet Transform for Improved Fingerprinting-Based Indoor Localization

Shih Hau Fang, Wei Hsiang Chang, Yu Tsao, Huang Chia Shih, Chiapin Wang

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Recently, channel state information (CSI) has been adopted as an enhanced wireless channel measurement instead of received signal strength (RSS) for indoor WiFi positioning systems. However, although CSI contains richer location information, a challenging problem is the severe dynamic range and fluctuation among the high-dimensional channels, which may degrade accuracy and cause overfitting problems. This paper proposes a novel algorithm for improved fingerprinting-based indoor localization. The proposed algorithm decomposes the CSI sequence using the multilevel discrete wavelet transform (MDWT) and normalizes the wavelet coefficients by employing histogram equalization. The robust features were then extracted by reconstructing CSI through the inverse MDWT of the normalized coefficients. We demonstrate the effectiveness of the proposed algorithm through experiments. The results show that the proposed algorithm outperforms traditional RSS, CSI, and two CSI-based algorithms, FIFS and MIMO.

Original languageEnglish
Article number7552529
Pages (from-to)7784-7791
Number of pages8
JournalIEEE Sensors Journal
Volume16
Issue number21
DOIs
Publication statusPublished - 2016 Nov 1

Keywords

  • Channel state
  • indoor fingerprinting
  • mobile positioning
  • received signal strength
  • wavelet

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Channel State Reconstruction Using Multilevel Discrete Wavelet Transform for Improved Fingerprinting-Based Indoor Localization'. Together they form a unique fingerprint.

Cite this