Volume 31 Issue 5
Sep.  2022
Turn off MathJax
Article Contents
CHENG Lan, ZHANG Jing, NI Zihang, et al., “Multipath Suppressing Method Based on Pseudorange Model Using Modified Teaching-Learning Based Optimization Algorithm,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 821-831, 2022, doi: 10.1049/cje.2020.00.168
Citation: CHENG Lan, ZHANG Jing, NI Zihang, et al., “Multipath Suppressing Method Based on Pseudorange Model Using Modified Teaching-Learning Based Optimization Algorithm,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 821-831, 2022, doi: 10.1049/cje.2020.00.168

Multipath Suppressing Method Based on Pseudorange Model Using Modified Teaching-Learning Based Optimization Algorithm

doi: 10.1049/cje.2020.00.168
Funds:  This work was supported by the National Natural Science Foundation of China (62073232, 61973226)
More Information
  • Author Bio:

    (corresponding author) was born in Henan Province, China. She received the M.S. degree in control theory and control engineering from Taiyuan University of Technology in 2008 and the Ph.D. degree in control science and engineering from Beijing Institute of Technology in 2012. She is currently an Assistant Professor with the College of Electrical and Power Engineering, Taiyuan University of Technology. Her research interests include multipath mitigation for GNSS and simultaneous localization and mapping for robots. (Email: taolan_1983@126.com)

    received the M.S. degree in control theory and control engineering from Taiyuan University of Technology in 2019. She is currently with Xi’an Branch of China Telecom Co. Ltd. Her research interest is multipath mitigation for GNSS. (Email: 1428149002@qq.com)

    is currently a graduate student in Taiyuan University of Technology. His research interests include differential evolution algorithm and its application in multipath mitigation. (Email: ni.zihang@foxmail.com)

    received the M.S. degree in control theory and control engineering, in 2003 and the Ph.D. degree in circuits and systems, in 2007, both from Taiyuan University of Technology. He is currently a Professor with the College of Electrical and Power Engineering, Taiyuan University of Technology. His research interests include intelligent control theory and application, evolutionary computation, knowledge discovery, and intelligent information processing. (Email: yangaowei@tyut.edu.cn)

  • Received Date: 2020-06-12
  • Accepted Date: 2021-10-21
  • Available Online: 2022-01-06
  • Publish Date: 2022-09-05
  • Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable, which also leads to increasing demand for high-positioning accuracy. In some complex environments (such as dense urban, valley), multipath interference is one of the main error sources deteriorating positioning accuracy, and it is difficult to eliminate via differential techniques due to its uncertainty of occurrence and irrelevance in different instants. To address this problem, we propose a positioning method for global navigation satellite systems (GNSS) by adopting a modified teaching-learning based optimization (TLBO) algorithm after the positioning problem is formulated as an optimization problem. Experiments are conducted by using actual satellite data. The results show that the proposed positioning algorithm outperforms other algorithms, such as particle swarm optimization based positioning algorithm, differential evolution based positioning algorithm, variable projection method, and TLBO algorithm, in terms of accuracy and stability.
  • loading
  • [1]
    B. S. Ge, H. Zhan,g and Y.Q. Jin, “Redundant measurement based method for online mitigation of GNSS multipath errors,” Systems Engineering and Electronics, vol.41, no.11, pp.2581–2587, 2019.
    [2]
    B. Ba, W. Cui, D. Wang, et al., “Maximum likelihood time delay estimation based on Monte Carlo importance sampling in multipath environment,” International Journal of Antennas and Propagation, vol.2017, article no.4215293, 2017.
    [3]
    L. Cheng, Z. Y. Wang, G. Xie, et al., “A sliding average extended Kalman filter and its application in multipath estimation,” in Proceedings of the 12th IEEE International Conference on Control and Automation, Kathmandu, Nepal, pp.944–946, 2016.
    [4]
    L. Cheng, Z. Wang, J. Chen, et al., “An improved multipath estimation algorithm using particle filter and sliding average extended Kalman filter,” Journal of Electronics & Information Technology, vol.39, no.3, pp.709–716, 2017.
    [5]
    L. Cheng, H. Yue, Y. Xing, et al., “Multipath estimation based on modified-constrained rank-based differential evolution with minimum error entropy,” IEEE Access, vol.6, pp.61569–61582, 2018. doi: 10.1109/ACCESS.2018.2875020
    [6]
    Q. Liu, M. Xu, and T. LI, “Joint blind estimation of PN codes and channels with maximum likelihood for DSSS signals in multipath channels,” Acta Electonica Sinica, vol.49, no.8, pp.1480–1488, 2021.
    [7]
    H. Yang, X. Yang, B. Sun, et al., “Global navigation satellite system multipath mitigation using a wave-absorbing shield,” Sensors, vol.16, no.8, article no.1332, 2016. doi: 10.3390/s16081332
    [8]
    M. T. Ho, H. A. Krichene, G. F. Ricciardi, et al., “Multipath mitigation in calibration range estimation algorithm,” in Proceedings of 2017 IEEE Radar Conference, Seattle, WA, USA, pp.1256–1261, 2017.
    [9]
    V. A. Veitsel, A. V. Zhdanov, and M. I. Zhodzishsky, “The mitigation of multipath errors by strobe correlators in GPS/GLONASS receivers,” GPS Solutions, vol.2, no.2, pp.38–45, 1998. doi: 10.1007/PL00000035
    [10]
    K. Jia and L. Hao, “Modeling of multipath channel and performance analysis of MIMO-DCO-OFDM system in visible light communications,” Chinese Journal of Electronics, vol.28, no.3, pp.630–639, 2019. doi: 10.1049/cje.2019.03.010
    [11]
    L. Cheng, K. Wang, M. Ren, et al., “Adaptive filter approach for GPS multipath estimation under correntropy criterion in dynamic multipath environment,” IEEE Transactions on Signal Processing, vol.67, no.22, pp.5798–5810, 2019. doi: 10.1109/TSP.2019.2946028
    [12]
    Q. Jia, R. Wu, W. Wang, et al., “Multipath interference mitigation in GNSS via WRELAX,” GPS Solutions, vol.21, no.2, pp.487–498, 2017. doi: 10.1007/s10291-016-0538-9
    [13]
    L. Cheng, Y. Xing, M. Ren, et al., “Multipath estimation algorithm using ε constrained rank-based differential evolution,” Acta Electronica Sinica, vol.46, no.1, pp.167–174, 2018.
    [14]
    D. J. Jwo and C. S. Hsu, “Multipath parameter estimation using the unscented particle filter based code tracking loop for non-Gaussian noises,” Applied Mechanics and Materials, vol.764–765, pp.565–569, 2015. doi: 10.4028/www.scientific.net/AMM.764-765.565
    [15]
    K. Breivik, B. Forssell, C. Kee, et al., “Estimation of multipath error in GPS pseudorange measurements,” Navigation, vol.44, no.1, pp.43–52, 1997. doi: 10.1002/j.2161-4296.1997.tb01938.x
    [16]
    N. Viandier, D. F. Nahimana, J. Marais, et al., “Gnss performance enhancement in urban environment based on pseudo-range error model,” in Proceedings of 2008 IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, USA, pp.377–382, 2008.
    [17]
    A. Angrisano, A. Maratea, and S. Gaglione, “A resampling strategy based on bootstrap to reduce the effect of large blunders in GPS absolute positioning,” Journal of Geodesy, vol.92, no.1, pp.81–92, 2018. doi: 10.1007/s00190-017-1046-6
    [18]
    J. Lesouple, T. Robert, M. Sahmoudi, et al., “Multipath mitigation for GNSS positioning in an urban environment using sparse estimation,” IEEE Transactions on Intelligent Transportation Systems, vol.20, no.4, pp.1316–1328, 2019. doi: 10.1109/TITS.2018.2848461
    [19]
    G. Chen, M. Gan, C. L. P. Chen, et al., “A two-stage estimation algorithm based on variable projection method for GPS positioning,” IEEE Transactions on Instrumentation and Measurement, vol.67, no.11, pp.2518–2525, 2018. doi: 10.1109/TIM.2018.2826798
    [20]
    R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol.43, no.3, pp.303–315, 2011. doi: 10.1016/j.cad.2010.12.015
    [21]
    D. Li, C. Zhang, X. Shao, et al., “A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints,” Journal of Intelligent Manufacturing, vol.27, no.4, pp.725–739, 2016. doi: 10.1007/s10845-014-0919-2
    [22]
    B. Wang, H. Li, and Y. Feng, “An improved teaching-learning-based optimization for constrained evolutionary optimization,” Information Sciences, vol.456, pp.131–144, 2018. doi: 10.1016/j.ins.2018.04.083
    [23]
    V. Patel and V. Savsani, “Multi-objective optimization of a Stirling heat engine using TS-TLBO (tutorial training and self learning inspired teaching-learning based optimization) algorithm,” Energy, vol.95, pp.528–541, 2016. doi: 10.1016/j.energy.2015.12.030
    [24]
    L. Cheng, Y. Guo, and G. Xie, “Two improved signal synchronizing methods for GPS software receiver,” in Proceedings of the International Symposium on Computational Intelligence and Industrial Applications, Beijing, China, pp.1–6, 2016.
    [25]
    C. M. Zhang, J. Chen, and B. Xin, “Differential evolution with adaptive population size combining lifetime and extinction mechanisms,” in Proceedings of the 8th Asian Control Conference, Kaohsiung, China, 1221–1226, 2011.
    [26]
    J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of International Conference on Neural Networks, Perth, WA, Australia, pp.1942–1948, 1995.
    [27]
    R. Storn and K. Price, “Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol.11, no.4, pp.341–359, 1997. doi: 10.1023/A:1008202821328
    [28]
    P. R. R. Strode and P. D. Groves, “GNSS multipath detection using three-frequency signal-to-noise measurements,” GPS Solutions, vol.20, no.3, pp.399–412, 2016. doi: 10.1007/s10291-015-0449-1
    [29]
    K. Borre, D. Akos, N. Bertelsen, et al., A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach, Springer Science & Business Media, New York, 2007.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(4)

    Article Metrics

    Article views (474) PDF downloads(72) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return