Browse > Article
http://dx.doi.org/10.6109/jkiice.2019.23.10.1290

Distance Estimation Using Convolutional Neural Network in UWB Systems  

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)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.10, 2019 , pp. 1290-1297 More about this Journal
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.
Keywords
Ultra-wideband systems; Distance Estimation; Convolutional Neural Network; Localization; ToA estimation;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 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.   DOI
2 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.   DOI
3 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.
4 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.   DOI
5 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.
6 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.   DOI
7 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.
8 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.
9 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.   DOI
10 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.   DOI
11 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.   DOI
12 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.
13 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.   DOI
14 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.   DOI