AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (8.1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

School of Automation, Guangdong University of Technology, and also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou 510006, China
School of Automation, Guangdong University of Technology, and also with Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China
111 Center for Intelligent Batch Manufacturing Based on IoT Technology, and also with Key Laboratory of Intelligent Information Processing and System Integration of IoT, Ministry of Education, Guangzhou 510006, China
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technolog, Hong Kong 999077, China
Academician of Russian Engineering Academy, Moscow Moscow 125009, Russia
Show Author Information

Abstract

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System (BDS). However, most existing approaches to this issue involve supervised machine learning (ML) methods, and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal labeling. Inspired by an autoencoder with powerful unsupervised feature extraction, we propose a new deep learning (DL) model for BDS signal recognition that places a long short-term memory (LSTM) module in series with a convolutional sparse autoencoder to create a new autoencoder structure. First, we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time series. Second, we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data, which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series signals. Finally, we add an l1/2 regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition accuracy. We tested our proposed approach on a real urban canyon dataset, and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods (e.g., 11% better than a support vector machine) and two existing DL-based methods (e.g., 7.26% better than convolutional neural networks).

Keywords

convolutional sparse autoencoderBeiDou Navigation Satellite System (BDS)long short-term memory (LSTM)multipath classification

References

[1]

C. Ma, S. Pan, W. Gao, F. Ye, L. Liu, and H. Wang, Improving GNSS/INS tightly coupled positioning by using BDS-3 four-frequency observations in urban environments, Remote Sens., vol. 14, no. 3, p. 615, 2022.
Crossref Google Scholar
[2]

F. Li, R. Tu, J. Hong, S. Zhang, M. Liu, and X. Lu, Performance analysis of BDS–5G combined precise point positioning, Remote Sens., vol. 14, no. 13, p. 3006, 2022.
Crossref Google Scholar
[3]

Y. Xu, Y. Yang, and J. Li, Performance evaluation of BDS-3 PPP-B2b precise point positioning service, GPS Solut., vol. 25, no. 4, p. 142, 2021.
Crossref Google Scholar
[4]
H. Zhao, Z. Li, C. Chen, L. Wang, K. Xie, and S. Xie, Fusing vehicle trajectories and GNSS measurements to improve GNSS positioning correction based on actor-critic learning, in Proc. 2023 Int. Technical Meeting of The Institute of Navigation, Long Beach, CA, USA, 2023, pp. 82–94.
Crossref
[5]

W. Qiu, Q. Zeng, R. Xu, J. Liu, J. Shi, and Q. Meng, A multipath mitigation algorithm for GNSS signals based on the steepest descent approach, Satell. Navig., vol. 3, no. 1, p. 14, 2022.
Crossref Google Scholar
[6]

P. R. R. Strode and P. D. Groves, GNSS multipath detection using three-frequency signal-to-noise measurements, GPS Solut., vol. 20, no. 3, pp. 399–412, 2016.
Crossref Google Scholar
[7]
Groves, P. D, Jiang, Z, Rudi, M, and Strode, P, A portfolio approach to NLOS and multipath mitigation in dense urban areas, in Proc. the 26th Int. Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, TN, USA, 2013, pp. 3231–3247.
[8]
L. T. Hsu, GNSS multipath detection using a machine learning approach, in Proc. IEEE 20th Int. Conf. Intelligent Transportation Systems (ITSC), Yokohama, Japan, 2017, pp. 1–6.
Crossref
[9]
Z. Li, K. Zeng, L. Wang, X. Kan, R. Yuan, and S. Xie, NLOS/LOS classification by constructing indirect environment interaction from GNSS receiver measurements using a transformer-based deep learning model, in Proc. 2023 Int. Technical Meeting of The Institute of Navigation, Long Beach, CA, USA, 2023, pp. 859–870.
Crossref
[10]

I. Smolyakov, M. Rezaee, and R. B. Langley, Resilient multipath prediction and detection architecture for low-cost navigation in challenging urban areas, NAVIGATION, vol. 67, no. 2, pp. 397–409, 2020.
Crossref Google Scholar
[11]

Z. Lyu and Y. Gao, An SVM based weight scheme for improving kinematic GNSS positioning accuracy with low-cost GNSS receiver in urban environments, Sensors, vol. 20, no. 24, p. 7265, 2020.
Crossref Google Scholar
[12]

J. Zidan, E. I. Adegoke, E. Kampert, S. A. Birrell, C. R. Ford, and M. D. Higgins, GNSS vulnerabilities and existing solutions: A review of the literature, IEEE Access, vol. 9, pp. 153960–153976, 2021.
Crossref Google Scholar
[13]

J. Chen, H. Shi, Z. Fang, C. Yuan and Y. Xu, Performance analysis of the GNSS instantaneous ambiguity resolution method using three collinear antennas, IEEE Sens. J., pp. 11936–11945, 2023.
Crossref Google Scholar
[14]

G. Chang, C. Chen, Y. Yang, and T. Xu, Tikhonov regularization based modeling and sidereal filtering mitigation of GNSS multipath errors, Remote. Sens., vol. 10, no. 11, p. 1801, 2018.
Crossref Google Scholar
[15]

R. Lu, W. Chen, D. Dong, Z. Wang, C. Zhang, Y. Peng, and C. Yu, Multipath mitigation in GNSS precise point positioning based on trend-surface analysis and multipath hemispherical map, GPS Solut., vol. 25, no. 3, p. 119, 2021.
Crossref Google Scholar
[16]

A. El-Mowafy, B. Xu, and L. T. Hsu, Integrity monitoring using multi-GNSS pseudorange observations in the urban environment combining ARAIM and 3D city models, J. Spatial Sci., vol. 67, no. 1, pp. 91–110, 2022.
Crossref Google Scholar
[17]

R. Sun, G. Wang, W. Zhang, L. T. Hsu, and W. Y. Ochieng, A gradient boosting decision tree based GPS signal reception classification algorithm, Appl. Soft Comput., vol. 86, p. 105942, 2020.
Crossref Google Scholar
[18]
Y. Qin, Z. Li, S. Xie, R. Yuan, and J. Xie, BDS multipath signal classification using support vector machine, in Proc. 4th Int. Conf. Industrial Artificial Intelligence (IAI), Shenyang, China, 2022, pp. 1–6.
Crossref
[19]
G. Zhang, B. Xu, and L. T. Hsu, GNSS shadow matching based on intelligent LOS/NLOS classifier, in Proc. 16th IAIN World Congr., Chiba, Japan, pp. 1–7, 2018.
[20]

T. Suzuki and Y. Amano, NLOS multipath classification of GNSS signal correlation output using machine learning, Sensors, vol. 21, no. 7, p. 2503, 2021.
Crossref Google Scholar
[21]
R. Zawislak, M. Greiff, K. J. Kim, K. Berntorp, S. Di Cairano, M. Konishi, K. Parsons, P. V. Orlik, and Y. Sato, GNSS multipath detection aided by unsupervised domain adaptation, in Proc. the 35th Int. Technical Meeting of the Satellite Division of The Institute of Navigation, Denver, CO, USA, pp. 2127–2137, 2022.
Crossref
[22]

H. Wang, S. Pan, W. Gao, Y. Xia, and C. Ma, Multipath/NLOS detection based on K-means clustering for GNSS/INS tightly coupled system in urban areas, Micromachines, vol. 13, no. 7, p. 1128, 2022.
Crossref Google Scholar
[23]

H. Xu, L. T. Hsu, D. Lu, and B. Cai, Sky visibility estimation based on GNSS satellite visibility: An approach of GNSS-based context awareness, GPS Solut., vol. 24, no. 2, p. 59, 2020.
Crossref Google Scholar
[24]

H. F. Ng, G. Zhang, and L. T. Hsu, Robust GNSS shadow matching for smartphones in urban canyons, IEEE Sens. J., vol. 21, no. 16, pp. 18307–18317, 2021.
Crossref Google Scholar
[25]
S. Zhang, S. Lo, Y. H. Chen, T. Walter, and P. Enge, GNSS multipath detection in urban environment using 3D building model, in Proc. IEEE/ION Position, Location and Navigation Symp. (PLANS), Monterey, CA, USA, 2018, pp. 1053–1058.
Crossref
[26]

S. Miura, L. T. Hsu, F. Chen, and S. Kamijo, GPS error correction with pseudorange evaluation using three-dimensional maps, IEEE Trans. Intell. Transport. Syst., vol. 16, no. 6, pp. 3104–3115, 2015.
Crossref Google Scholar
[27]

G. Zhang, P. Xu, H. Xu, and L. T. Hsu, Prediction on the urban GNSS measurement uncertainty based on deep learning networks with long short-term memory, IEEE Sens. J., vol. 21, no. 18, pp. 20563–20577, 2021.
Crossref Google Scholar
[28]

H. Su, B. Wu, and X. Mao, Non-line-of-sight multipath detection method for BDS/GPS fusion system based on deep learning, J. Shanghai Jiaotong Univ. Sci., vol. 27, no. 6, pp. 844–854, 2022.
Crossref Google Scholar
[29]

Y. Quan, L. Lau, G. W. Roberts, X. Meng, and C. Zhang, Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning, Remote. Sens., vol. 10, no. 12, p. 2052, 2018.
Crossref Google Scholar
[30]

A. Blais, N. Couellan, and E. Munin, A novel image representation of GNSS correlation for deep learning multipath detection, Array, vol. 14, p. 100167, 2022.
Crossref Google Scholar
[31]

A. V. Kanhere, S. Gupta, A. Shetty, and G. Gao, Improving gnss positioning using neural-network-based corrections, NAVIGATION-US, vol. 69, no. 4, p. 548, 2022.
Crossref Google Scholar
[32]

Z. He, H. Shao, Z. Ding, H. Jiang, and J. Cheng, Modified deep autoencoder driven by multisource parameters for fault transfer prognosis of aeroengine, IEEE Trans. Ind. Electron., vol. 69, no. 1, pp. 845–855, 2022.
Crossref Google Scholar
[33]

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, LSTM: A search space odyssey, IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 10, pp. 2222–2232, 2017.
Crossref Google Scholar
[34]

Z. Xu, X. Chang, F. Xu, and H. Zhang, L1/2 regularization: A thresholding representation theory and a fast solver, IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 7, pp. 1013–1027, 2012.
Crossref Google Scholar
[35]

Z. Li, S. Ding, W. Chen, Z. Yang, and S. Xie, Proximal alternating minimization for analysis dictionary learning and convergence analysis, IEEE Trans. Emerg. Top. Comput. Intell., vol. 2, no. 6, pp. 439–449, 2018.
Crossref Google Scholar
[36]

H. Zhao, J. Wu, Z. Li, W. Chen, and Z. Zheng, Double sparse deep reinforcement learning via multilayer sparse coding and nonconvex regularized pruning, IEEE Trans. Cybern., vol. 53, no. 2, pp. 765–778, 2023.
Crossref Google Scholar
[37]

Z. Li, H. Zhao, Y. Guo, Z. Yang, and S. Xie, Accelerated Log-regularized convolutional transform learning and its convergence guarantee, IEEE Trans. Cybern., vol. 52, no. 10, pp. 10785–10799, 2022.
Crossref Google Scholar
[38]

Z. Li, Z. Yang, H. Zhao, and S. Xie, Direct-optimization-based DC dictionary learning with the MCP regularizer, IEEE Trans. Neural Netw. Learning Syst., vol. 34, no. 7, pp. 3568–3579, 2023.
Crossref Google Scholar
[39]

P. O. Hoyer, Nonnegative matrix factorization with sparseness constraints, J. Mach. Learn. Res., vol. 5, pp. 1457–1469, 2004.
Google Scholar
Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 68-86
DOI: 10.26599/TST.2024.9010004
Cite this article:
Qin Y, Li Z, Xie S, et al. Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM. Tsinghua Science and Technology, 2025, 30(1): 68-86. https://doi.org/10.26599/TST.2024.9010004

213

Views

42

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 25 October 2023
Revised: 08 December 2023
Accepted: 26 December 2023
Published: 26 March 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return