Journal of Positioning, Navigation, and Timing (J Position Navig Timing; JPNT)
Citation: Cho, S., Kim, J.-H., 2023, GNSS NLOS Signal Classifier with Successive Correlation Outputs using CNN, Journal of Positioning, Navigation, and Timing, 12, 1-9.
Journal of Positioning, Navigation, and Timing (J Position Navig Timing) 2023 March, Volume 12, Issue 1, pages 1-9. https://doi.org/10.11003/JPNT.2023.12.1.1
Received on 28 September 2022, Revised on 06 December 2022, Accepted on 11 January 2023, Published on 30 March 2023.
License: Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/bync/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sangjae Cho†, Jeong-Hoon Kim
Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon 34051, Korea
†Corresponding Author: E-mail, sanje@kaist.ac.kr; Tel: +82-42-350-1285 Fax: +82-42-350-1250
The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.
GNSS, multipath, NLOS, CNN
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Conceptualization, S.C.; methodology, S.C.; software, S.C.; validation, S.C.; formal analysis, S.C., J.K.; investigation, S.C.; resources, S.C.; data curation, S.C.; writing—original draft preparation, S.C. and J.K.; writing—review and editing, J.K.; visualization, S.J.; supervision, S.J.
The authors declare no conflict of interest.