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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2022> 31(6): 570-583
Haoyu Yang, Yuanshuo Wang, Dongchen Li, Tiancheng Li. An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(6): 570-583. doi: 10.15918/j.jbit1004-0579.2022.060
Citation: Haoyu Yang, Yuanshuo Wang, Dongchen Li, Tiancheng Li. An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(6): 570-583.doi:10.15918/j.jbit1004-0579.2022.060
shu

An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion

doi:10.15918/j.jbit1004-0579.2022.060
  • 1.

    the Key Laboratory of Information Fusion Technology (Ministry of Education), School of Automation, Northwestern Polytechnical University, Xi’an 710072, China

  • 2.

    CSSC Systems Engineering Research Institute, Beijing 100094, China

Funds:This work was partially supported by the National Natural Science Foundation of China (No.62071389).
More Information
  • Author Bio:

    Haoyu Yangreceived the B.S. degree in Automation and Transportation Information from Chang’an University in 2016. He is currently a postgraduate student in Electronic Information Engineering with the Key Laboratory of Information Fusion Technology (Ministry of Education), the School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China. His research interests include indoor positioning, target tracking, information fusion

    Yuanshuo Wangreceived the B.S. degree in automation from Qingdao University in 2021. He is currently a postgraduate student in Electronic Information with the School of Automation, Northwestern Polytechnical University, Xi’an, China. His research interests include target tracking, neural processes and neural network

    Dongchen Liis working in CSSC Systems Engineering Research Institute. He was born in July 1988 and received his doctoral degree from Xidian University in 2016. His research interests include image processing, radar signal processing and UAV mission systems

    Tiancheng Lireceived two bachelor’s degrees from Harbin Engineering University, China, in 2008, the first Ph.D. degree from London South Bank University, U.K., in 2013 and the second Doctoral degree from Northwestern Polytechnical University (NPU), China, in 2015. He is currently a Professor with the School of Automation, NPU. Prior to this, he had been a Postdoctoral Researcher with the BISITE Group, University of Salamanca, Spain, from June 2014 to the fall of 2018, and a Visiting Scholar with the Vienna University of Technology, Austria, in the summer of 2017 and in the fall of 2018. He received the Excellent Doctoral Thesis Award of Shaanxi Province in 2017 and the Marie Sklodowska-Curie Individual Fellowship from European Commission in 2016-2018. His research is focused on multi-sensor information arithmetic average fusion, and data-driven targeting track algorithms for target detection and tracking

  • Corresponding author:t.c.li@nwpu.edu.cn
  • Received Date:2022-05-03
  • Rev Recd Date:2022-06-10
  • Accepted Date:2022-07-03
  • Publish Date:2022-12-25
    Abstract
  • In this paper, an effective target locating approach based on the fingerprint fusion positioning (FFP) method is proposed which integrates the time-difference of arrival (TDOA) and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios. The FFP method fuses the pedestrian dead reckoning (PDR) estimation to solve the moving target localization problem. We also introduce auxiliary parameters to estimate the target motion state. Subsequently, we can locate the static pedestrians and track the the moving target. For the case study, eight access stationary points are placed on a bookshelf and hypermarket; one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf. We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor, weighted k-nearest neighbor, pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter, unscented Kalman filter and particle filter (PF). The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors, especially in the 3D scenarios. Simulation results corroborate the effectiveness of our proposed approach.
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