Journal of Positioning, Navigation, and Timing

  • 제13권3호
  • /
  • Pages.289-299
  • /
  • 2024
  • /
  • 2288-8187(pISSN)
  • /
  • 2289-0866(eISSN)
항법시스템학회 (The Institute of Positioning, Navigation, and Timing)
권호 표지
DOI QR코드

DOI QR Code

Rényi Divergence 기반 이상치 검출을 통한 적응형 센서/이종 인프라 통합 보행자 항법 기술

Adaptive Sensor/Heterogeneous Infrastructure Integrated Pedestrian Navigation Technology using Rényi Divergence-based Outlier Detection

  • 권재욱 ;
  • 조성윤 ;
  • 유재준 ;
  • 서성훈
  • Jae Uk Kwon (Department of IT Engineering, Kyungil University) ;
  • Seong Yun Cho (Department of Mechanical Automotive Engineering, Kyungil University) ;
  • JaeJun Yoo (Mobility UX Section, Electronics and Telecommunications Research Institute) ;
  • SeongHun Seo (Mobility UX Section, Electronics and Telecommunications Research Institute)
  • 투고 : 2024.08.10
  • 심사 : 2024.08.29
  • 발행 : 2024.09.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In the Pedestrian Dead Reckoning (PDR)/Global Positioning System (GPS)/Wi-Fi-integrated navigation system for indoor/outdoor continuous positioning of pedestrians, the process of detecting outliers in measurements is very important. When accurate location information from measurements is used, reliable correction data can be generated during the fusion filtering process. However, abnormal measurements may occur in certain situations, such as indoor/outdoor transitions, which can degrade filter performance and lead to significant errors in the estimated position. To address this issue, this paper proposes a method for detecting outliers in measurements based on Rényi Divergence (RD). When the deviation of the RD value is large, the measurements are considered outliers, and positioning is performed using only pure PDR. Based on experiments conducted with real data, it was confirmed that outliers were effectively detected for abnormal measurements, leading to an improvement in the performance of pedestrian navigation.

키워드

과제정보

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00141819).

참고문헌

  1. Almeida, D., Pedrosa, E., & Curado, F. 2021, Magnetic Mapping for Robot Navigation in Indoor Environments, 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Lloret de Mar, Spain, 29 Nov - 02 Dec 2021, pp.103-108. https://doi.org/10.1109/IPIN51156.2021.9662528
  2. Chen, C. & Kia, S. S. 2021, A Renyi Divergence Based Approach to Fault Detection and Exclusion for Tightly Coupled GNSS/INS System, Proceedings of the 2021 International Technical Meeting of The Institute of Navigation, St. Louis, Missouri, 25-28 January 2021, pp.674-687. https://doi.org/10.33012/2021.17859
  3. Cho, S. Y. 2014, Biaxial Accelerometer-based Magnetic Compass Module Calibration and Analysis of Azimuth Computational Errors Caused by Accelerometer Errors, Journal of Institute of Control, Robotics and Systems, 20, 149-156. https://doi.org/10.5302/J.ICROS.2014.13.9008
  4. Cho, S. Y., Lee, J. H., & Park, C. G. 2020, Stable Zero-Velocity Detection Method Regardless of Walking Speed for Foot-Mounted PDR, Journal of Positioning, Navigation, and Timing, 9, 33-42. https://doi.org/10.11003/JPNT.2020.9.1.33
  5. Gil, M., Alajaji, F., & Linder, T. 2013, Renyi divergence measures for commonly used univariate continuous distributions, Information Sciences, 249, 124-131. https://doi.org/10.1016/j.ins.2013.06.018
  6. Hou, C., Xie, Y., & Zhang, Z. 2023, FCLoc: A Novel Indoor Wi-Fi Fingerprints Localization Approach to Enhance Robustness and Positioning Accuracy, IEEE Sensors Journal, 23, 7153-7167. https://doi.org/10.1109/JSEN.2022.3229476
  7. He, S. & Chan. S.-H. G. 2016, Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons, IEEE Communications Surveys & Tutorials, 18, 466-490. https://doi.org/10.1109/COMST.2015.2464084
  8. Kwon, J. U., Chae, M. S., Cho, E. Y., & Cho, S. Y. 2023, Fast Generation of Wi-Fi Positioning Fingerprint Database Using Reference Location Information Acquired Based on 1D-PDR, Proceedings of the Work-in-Progress Papers at the 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN-WiP 2023), Nuremberg, Germany, 25-28 September 2023.
  9. Kwon, J. U. & Cho, S. Y. 2021, DNN-based LTE Signal Propagation Modelling for Positioning Fingerprint DB Generation, Journal of Positioning, Navigation, and Timing, 10, 55-66. https://doi.org/10.11003/JPNT.2021.10.1.55
  10. Leitch, S. G., Ahmed, Q. Z., Abbas, W. B., Hafeez, M., Laziridis, P. I., et al. 2023, On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies, Sensors, 23, 1-25. https://doi.org/10.3390/s23208598
  11. Ma, Z. & Shi, K . 2023, Few-Shot Learning for WiFi Fingerprinting Indoor Positioning, MDPI Sensors, 23, 1-18. https://doi.org/10.3390/s23208458
  12. Makkawi, K., Ait-Tmazirte, N., Najjar, M. E. B. E., & Moubayed, N. 2021, Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Renyi Divergence Strategy for Vehicle Localization, Entropy, 23, 1-27. https://doi.org/10.3390/e23040463
  13. Thierrin, F. C., Alajaji, F., & Linder, T. 2022, Renyi Cross-Entropy Measures for Common Distributions and Processes with Memory, Entropy, 24, 1-9. https://doi.org/10.3390/e24101417
  14. Titterton, D. & Weston, J. 2004, Strapdown Inertial Navigation Technology, 2nd ed. (London: Peregrinus).
  15. van Erven, T. & Harremos, P. 2014, Renyi Divergence and Kullback-Leibler Divergence, IEEE Transactions on Information Theory, 60, 3797-3820. https://doi.org/10.1109/TIT.2014.2320500