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Indoor Localization Based on Sparse TDOA Fingerprints

Guanglie Ouyang,Tinghao Qi,Lixiao Wei,Bang Wang

Published 2022 in IEEE International Conference on Computational Science and Engineering

ABSTRACT

Fingerprint-based indoor localization methods usually use received signal strength (RSS) and channel status information (CSI) as the localization fingerprint, which suffers from time-consuming and labor-intensive site survey. In this paper, we propose an indoor localization method based on sparse time difference of arrival (TDOA) fingerprints. This method constructs the localization fingerprints by TDOA, which is calibrated by the straight line fitting method and the beacon estimation method. In order to get the dense fingerprint database, we propose a TDOA interpolation method based on distance relation. Experiments on field measurements validate the effectiveness of the proposed method. In the case of only sampling three reference points (RPs), the average localization error (ALE) of the proposed method reaches 0.824 m, which obtains a 48.8 % improvement compared with the traditional TDOA algorithm,

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    IEEE International Conference on Computational Science and Engineering

  • Publication date

    2022-12-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/CSE57773.2022.00010
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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