DOI QR코드

DOI QR Code

Distance Estimation Using Convolutional Neural Network in UWB Systems

UWB 시스템에서 합성곱 신경망을 이용한 거리 추정

  • Nam, Gyeong-Mo (Department of Mobile Convergence and Engineering, Hanbat National University) ;
  • Jung, Tae-Yun (Department of Mobile Convergence and Engineering, Hanbat National University) ;
  • Jung, Sunghun (C4I R&D Center, LIG Nex1 Company) ;
  • Jeong, Eui-Rim (Department of Information and Commucation Engineering, Hanbat National University)
  • Received : 2019.07.11
  • Accepted : 2019.07.29
  • Published : 2019.10.31

Abstract

The paper proposes a distance estimation technique for ultra-wideband (UWB) systems using convolutional neural network (CNN). To estimate the distance from the transmitter and the receiver in the proposed method, 1 dimensional vector consisted of the magnitudes of the received samples is reshaped into a 2 dimensional matrix, and by using this matrix, the distance is estimated through the CNN regressor. The received signal for CNN training is generated by the UWB channel model in the IEEE 802.15.4a, and the CNN model is trained. Next, the received signal for CNN test is generated by filed experiments in indoor environments, and the distance estimation performance is verified. The proposed technique is also compared with the existing threshold based method. According to the results, the proposed CNN based technique is superior to the conventional method and specifically, the proposed method shows 0.6 m root mean square error (RMSE) at distance 10 m while the conventional technique shows much worse 1.6 m RMSE.

본 논문에서는 ultra-wideband(UWB) 시스템에서 합성곱 신경망(CNN)을 이용한 거리 추정 기법을 제안한다. 제안하는 기법은 UWB 신호를 이용하여 송신기와 수신기 사이의 거리를 추정하기 위하여 수신신호의 크기 샘플로 이루어진 1차원 벡터를 2차원 행렬로 재구성하며, 이 2차원 행렬로부터 합성곱 신경망 회귀를 이용하여 거리를 추정한다. IEEE 802.15.4a 표준의 UWB 실내 가시선 채널모델을 이용하여 수신신호를 생성하여 학습데이터를 만들며 합성곱 신경망 모델을 학습시킨다. 또한 실제 필드 시험을 통해 실내환경에서의 실험 데이터를 이용하여 거리추정 성능을 확인한다. 제안하는 기법은 기존의 문턱값 기반의 거리 추정 기법과의 성능비교도 수행하는데, 결과에 따르면 10m 거리에서 제안기법은 0.6m의 제곱근 평균 자승 에러를 보이는데 기존기법은 1.6m로 훨씬 큰 에러를 보인다.

Keywords

Acknowledgement

The authors gratefully acknowledge the financial support provided by Defense Acquisition Program Administration and Defense Industry Technology Center under the contract UD160005D

References

  1. C. P. Yoon, and C. G. Hwang, "Efficient indoor positioning systems for indoor location-based service provider," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 6, pp. 1368-1373, Jun. 2015. https://doi.org/10.6109/jkiice.2015.19.6.1368
  2. J. W. Choi, "Implementation of a car rearview camera system based on the binary-CDMA wireless personal area network technology," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 10, pp. 2292-2300, Oct. 2015. https://doi.org/10.6109/jkiice.2015.19.10.2292
  3. J. N. Lee, H. Y. Kang, Y. T. Shin, and J. B. Kim, "Indoor positioning algorithm combining bluetooth low energy plate with pedestrian dead reckoning," Journal of the Korea Institute of Information and Communication Engineering, vol. 22, no. 2, pp. 302-313, Feb. 2018. https://doi.org/10.6109/jkiice.2018.22.2.302
  4. H. B. Kil, H. Joo, C. Lee, and E. R. Jeong, "A Calibration Technique for Array antenna based GPS Receivers," Journal of the Korea Institute of Information and Communication Engineering, vol. 22, no. 4, pp. 683-690, Apr. 2018. https://doi.org/10.6109/JKIICE.2018.22.4.683
  5. S. W. Lee, and S. W. Kim, "Indoor location positioning technology trends and forecasts," The Journal of The Korean Institute of Communication Sciences, vol. 32, no. 2, pp. 81-88, Feb. 2015.
  6. K. Wu, J. Xiao, Y. Yi, D. Chen, X. Luo, and L. M. Ni, "CSI-based indoor localization," IEEE of the Korea Institute of Information and Communication Engineering, vol. 19, no. 6, pp. 1368-1373, Jun. 2015. https://doi.org/10.6109/jkiice.2015.19.6.1368
  7. M. S. Cheon, J. Y. Lee, and J. K. Choe, "A study on improvement of indoor position using BLE 5.0," The Journal of The Korean Institute of Communication Sciences, vol. 16, no. 4, pp. 43-49, Apr. 2018.
  8. J. W. Park, and Y. B. Ko, "UWB based maximum likehood estimation TDOA technology for high precision positioning in indoor environment," The Journal of The Korean Institute of Information Technology, vol. 16, no. 4, pp. 43-49, Apr. 2018. https://doi.org/10.14801/jkiit.2018.16.4.43
  9. C. E. Yi, and T. K. Sung, "UWB positioning technology introduction and technology trend," The Journal of The Korean Institute of Communication Sciences, vol. 34, no. 4, pp. 33-38, Apr. 2017.
  10. A. Alarifi, A. Al-Salman, M. alsaleh, A. Alnafessah, S. Al-Hadhrami, M. A. Al-Ammar, and H. S. Al-Khalifa, "Ultra wideband indoor positioning technologies: Analysis and recent advances," Sensors (Basel), vol. 16, no. 5, pp. 1-36, May. 2016.
  11. S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molish, H. V. Poor, and Z. Sahingle, "Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks," IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 70-84, Jul. 2005. https://doi.org/10.1109/MSP.2005.1458289
  12. J. H. Sohn, and G. H. Hwang, "Development of position awareness algorithm using improved trilateration measurement method," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 2, pp. 473-480, Feb. 2013. https://doi.org/10.6109/jkiice.2013.17.2.473
  13. H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning," IEEE Transaction on Medical Imaging, vol. 35, no. 5, pp. 1285-1298, May. 2016. https://doi.org/10.1109/TMI.2016.2528162
  14. X. Zhang, J. Zhao, and Y. LeCun, "Character-level convolutional networks for text classification," in Proceeding of the 29th Conference on Neural Information Processing Systems, pp. 649-657, 2015.