RF signal shape reconstruction technology on the 2D space for indoor localization

Changsoo Yu, Beomju Shin, Chung G. Kang, Jung Ho Lee, Hankyeol Kyung, Teahun Kim, Taikjin Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Various technologies using smartphones are being studied to estimate the user's location in an indoor space where GNSS cannot be used. Typically, there is a fingerprinting method using RF signal strength. This method compares RSS information received as one-shot from the current user's location with a pre-built radio map to calculate the location. Therefore, the signal discriminant according to the position is poor and the position estimation accuracy is low. On the other hand Surface Correlation technology uses a RF signal shape accumulated along the user's trajectory. Therefore, there is an advantage in that signal discrimination according to location is improved and location estimation accuracy is high. In the case of pedestrians, we reconstruct a RF signal shape using PDR. The number of steps and the direction of pedestrians are estimated using the IMU in the smartphone. Using this, the path is estimated. However, in the case of real environment, there are a wide variety of behaviors. Because behavior other than walking is measured in IMU, it is changed from the true path and the distorted signal shape is reconstructed. Distorted signal shape cause many errors because they do not match radio maps. In this paper, we are consider pedestrian in real environment and propose an RF signal shape reconstruction technology. In order to detect the correct step in the motion change, we use the enhanced PDR. This is a technology that detects accurate steps through peak detection using the pitch of a smartphone. In addition, we propose adaptive step length estimation to estimate various step length. This algorithm uses only one parameter and can provide accurate distance information by changing linear equations according to intervals. Also, the pattern of the step is analyzed to detect only the step for the purpose of moving. We propose a pacing detection that detects the path, not the purpose of pedestrian moving, by utilizing variance of step length and variance of step detection time. Through this, the position error generated in the pacing path can be reduced. To verify the performance of the proposed technology, a scenario such as a pedestrian of real environment was set and tested. The experimental results show that the proposed technology calculates more accurate trajectory even if motion changes and various behaviors are performed compared to the conventional PDR. Through this, it was confirmed that the accurate RF signal shape was reconstructed and the position was calculated more stably from the calculated position results of Surface Correlation.

Original languageEnglish
Title of host publication2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665409346
DOIs
Publication statusPublished - 2022
Event2022 International Conference on Electronics, Information, and Communication, ICEIC 2022 - Jeju, Korea, Republic of
Duration: 2022 Feb 62022 Feb 9

Publication series

Name2022 International Conference on Electronics, Information, and Communication, ICEIC 2022

Conference

Conference2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Country/TerritoryKorea, Republic of
CityJeju
Period22/2/622/2/9

Bibliographical note

Funding Information:
IV. CONCLUSION In this paper, we propose a technology for reconstructing the RF signal shape even in the behavior of pedestrians considering the real environment. Unlike the existing fingerprinting method, SC calculates a location using a RF signal shape. The RF signal shape is reconstructed by accumulating received signals according to the user's trajectory. However, in the case of pedestrians, since there are various motions and behaviors, these distort the signal shape. Therefore, this paper proposes the RF signal shape reconstruction technology. We detect the steps in various motions using enhanced PDR. This estimates the correct steps through acceleration norm peak detection as well as pitch peak detection in motion such as swing. In addition, unlike the conventional step length estimation algorithm, we propose adaptive step length estimation that uses only one parameter and uses a different linear equation according to the section. Through this, it is possible to provide more accurate distance information by estimating various step length occurring in real environment. In addition, it detects only normal steps by judging the detected step pattern through pacing detection, and recognizes the pacing path by using the variance of the step length and step detection time. Through this, it was confirmed that more accurate PDR path generation was possible by correcting the PDR position error in the pacing section.Tests were performed considering various motions and behaviors, and improved PDR results were confirmed. In the case of the conventional technology, the error between the start and end positions was about 50m, and in the case of the proposed technology, the error was about 12m. Through this, an RF signal shape was reconstructed and the position was calculated through SC. As a result a position result similar to the true scenario was confirmed, and a more stable position result was confirmed unlike the conventional technology, In the future, we plan to apply the proposed technology to a more complex indoor environment ACKNOWLEDGMENT This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) [CRC-20-02-KIST]. It was partly supported by the Institute for Information and Communications Technology Program (IITP) Grant funded by the Korea Government (Ministry of Science and ICT, MSIT) under Grant 2019-0-01401 and by the Multi-source based 3D Emergency LOCalization using machine learning techniques (MELOC)..

Publisher Copyright:
© 2022 IEEE.

Keywords

  • PDR
  • Surface Correlation
  • indoor localization
  • signal shape

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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