Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-25T16:45:53.286Z Has data issue: false hasContentIssue false
  • English
  • Français

GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

Published online by Cambridge University Press:  06 December 2018

Rui Sun Open the ORCID record for Rui Sun [Opens in a new window] ,
Li-Ta Hsu ,
Dabin Xue ,
Guohao Zhang  and
Washington Yotto Ochieng
Rui Sun
Affiliation:
(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China) (State Key Laboratory of Geo-Information Engineering, Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China)
Li-Ta Hsu*
Affiliation:
(Interdisciplinary Division of Aeronautical and Aviation Engineering, the Hong Kong Polytechnic University, Hong Kong)
Dabin Xue
Affiliation:
(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
Guohao Zhang
Affiliation:
(Interdisciplinary Division of Aeronautical and Aviation Engineering, the Hong Kong Polytechnic University, Hong Kong)
Washington Yotto Ochieng
Affiliation:
(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China) (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

Keywords

NLOSMultipathUrban CanyonANFIS
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 685 - 701
Copyright
Copyright © The Royal Institute of Navigation 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adjrad, M. and Groves, P. D. (2016). Intelligent Urban Positioning using Shadow Matching and GNSS Ranging Aided by 3D Mapping, ION GNSS + 2016, Portland, Oregon, September, 534–553.Google Scholar
Betaille, D., Peyret, F., Ortiz, M., Miquel, S. and Fontenay, L. (2013). A new modeling based on urban trenches to improve GNSS positioning quality of service in cities. IEEE Intelligent transportation systems magazine, 5(3), 59–70.Google Scholar
Bhattacharyya, S. and Gebre-Egziabher, D. (2014). Vector loop RAIM in nominal and GNSS-stressed environments. IEEE Transactions on Aerospace and Electronic Systems, 50(2), 1249–1268.Google Scholar
Blasch, E. P., Salerno, J. J. and Tadda, G. P. (2011). Measuring the worthiness of situation assessment. IEEE Aerospace & Electronics Conference, 87–94.Google Scholar
Braasch, M. S. (1996) Multipath effects. In: Global Positioning System: Theory and Applications. 1: 547–568.Google Scholar
Chiu, S. (1994). Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent & Fuzzy Systems, 2(3).Google Scholar
Cox, D. B. (1978). Integration of GPS with Inertial Navigation Systems. Navigation, 25(2), 236–245.Google Scholar
Dierendonck, V. A., Fenton, P. and Ford, T. (1992). Theory and performance of narrow correlator spacing in a GPS receiver. Navigation, 39(3), 265–283.Google Scholar
Euler, H. J. and Goad, C.C. (1991). On optimal filtering of GPS dual frequency observations without using orbit information. Journal of Geodesy, 65(2), 130–143.Google Scholar
Fessler, J. A. and Hero, A. O. (1994). Space-alternating generalized expectation-maximization algorithm. IEEE Transactions on Signal Processing, 42(10), 2664–2677.Google Scholar
Groves, P. D. (2011). Shadow matching: A new GNSS positioning technique for urban canyons. The Journal of Navigation, 64(3), 417–430.Google Scholar
Groves, P. D., Jiang, Z., Rudi, M. and Strode, P. (2013) A Portfolio Approach to NLOS and Multipath Mitigation in Dense Urban Areas. In: Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2013), 3231–3247. Manassas, US.Google Scholar
Groves, P. D. and Jiang, Z. (2013). Height Aiding, C/N 0 Weighting and Consistency Checking for GNSS NLOS and Multipath Mitigation in Urban Areas. The Journal of Navigation, 66(5), 653–669.Google Scholar
Gurtner, W. (1994). Innovation: Rinex–The Receiver Independent Exchange Format. GPS World, 5(7), 48–53.Google Scholar
Hartinger, H. and Brunner, F. K. (1999). Variances of GPS phase observations: the SIGMA-? model. GPS solutions, 2(4), 35–43.Google Scholar
Hsu, L. T., Jan, S. S., Groves, P. D. and Kubo, N. (2015). Multipath mitigation and NLOS detection using vector tracking in urban environments. GPS Solutions, 19(2), 249–262.Google Scholar
Hsu, L. T., Gu, Y. and Kamijo, S. (2016). 3D building model-based pedestrian positioning method using GPS/ GLONASS/QZSS and its reliability calculation. GPS Solutions, 20(3), 413–428.Google Scholar
Hsu, L.T. (2017). GNSS multipath detection using a machine learning approach. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 1–6.Google Scholar
Hsu, L-T, Tokura H, Kubo N, Gu Y, Kamijo S (2017). Multiple Faulty GNSS Measurement Exclusion based on Consistency Check in Urban Canyons. IEEE Sensors Journal, 17(6), 1909–1917Google Scholar
Izadpanah, A., O'Driscoll, C. and Lachapelle, G. (2008, September). GPS multipath parameterization using the extended kalman filter and a dual LHCP/RHCP antenna. In Proceedings of the 21st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2008), Savannah, GA, USA, 16–19.Google Scholar
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.Google Scholar
Jiang, Z. and Groves, P. D. (2014). NLOS GPS signal detection using a dual-polarisation antenna. GPS Solutions, 18(1), 15–26.Google Scholar
Jilani, A., Murawwat, S. and Jilani, S. O. (2015). Controlling Speed of DC Motor with Fuzzy Controller in Comparison with ANFIS Controller. Intelligent Control and Automation, 06(01), 64–74.Google Scholar
Kubo, N., Kobayashi, K., Hsu, L. T. and Amai, O. (2017). Multipath Mitigation Technique under Strong Multipath Environment Using Multiple Antennas. Journal of Aeronautics, Astronautics and Aviation, 49(1), 75–82.Google Scholar
Kuusniemi, H., (2005). User-Level Reliability and Quality Monitoring in Satellite-Based Personal Navigation. PhD thesis, Tampere University of Technology, Finland, 2005. Publication 544Google Scholar
Lau, L. and Cross, P. (2007). Development and testing of a new ray-tracing approach to GNSS carrier-phase multipath modelling. Journal of Geodesy, 81(11), 713.Google Scholar
Palamartchouk, K., Clarke, P. J., Edwards, S. J. and Tiwari, R. (2015). Dual-polarization GNSS observations for multipath mitigation and better high precision positioning. In Proceedings of the 28th International Technical Meeting of the ION Satellite Division, ION GNSS + 2015, Tampa, Florida (pp. 2772–2779).Google Scholar
Peyraud, S., Bétaille, D., Renault, S., Ortiz, M., Mougel, F., Meizel, D. and Peyret, F. (2013). About non-line-of-sight satellite detection and exclusion in a 3D map-aided localization algorithm. Sensors, 13(1), 829–847.Google Scholar
Phan, Q., Tan, S., McLoughlin, I. V. and Vu, D. V. (2013). A unified framework for GPS code and carrier-phase multipath mitigation using support vector regression. Advances in Artificial Neural Systems, 2013, Article ID 240564, 1–14.Google Scholar
Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51(2), 350–365.Google Scholar
Sahmoudi, M. and Amin, M. G. (2008). Fast iterative maximum-likelihood algorithm (FIMLA) for multipath mitigation in the next generation of GNSS receivers. IEEE Transactions on Wireless Communications, 7(11).Google Scholar
Smith, L. I. (2002). A tutorial on principal components analysis. Cornell University, USA, 51(52), 65.Google Scholar
Sokhandan, N., Ziedan, N., Broumandan, A., & Lachapelle, G. (2017). Context-aware adaptive multipath compensation based on channel pattern recognition for GNSS receivers. The Journal of Navigation, 70, 944–962.Google Scholar
Suzuki, T. (2016). Integration of GNSS Positioning and 3D Map using Particle Filter, Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS + 2016), Portland, Oregon, September, 1296–1304.Google Scholar
Takagi, T. and Sugeno, M. (1983). Derivation of fuzzy control rules from human operator's control actions. Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, 55–60.Google Scholar
Tranquilla, J. M., Carr, J. P. and Al-Rizzo, H. M. (1994). Analysis of a choke ring groundplane for multipath control in global positioning system (GPS) applications. IEEE Transactions on Antennas and Propagation, 42(7), 905–911.Google Scholar
Ubeyli, E. D. (2009). Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer. Journal of Medical Systems, 33(5), 353–358.Google Scholar
Van Nee, R.D.J, Siereveld, J., Fenton, P.C. and Townsend, B.R. (1994). The multipath estimating delay lock loop: approaching theoretical accuracy limits, in Position Location and Navigation Symposium, IEEE, (246–251).Google Scholar
Wang, H. S., Kao, C. Y. and Chen, J. F. (2013a). Sequential quadratic method for GPS NLOS positioning in urban canyon environments. International Journal of Automation & Smart Technology, 3(1), 37–46.Google Scholar
Wang, L., Groves, P. D. and Ziebart, M. K. (2015). Smartphone shadow matching for better cross-street GNSS positioning in urban environments. The Journal of Navigation, 68(3), 411–433.Google Scholar
Wei, L. (2016). A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting, Applied Soft Computing, 42, 368–376.Google Scholar
Yang, C., Shi, W. and Chen, W. (2018). Adaptive unscented Kalman filtering based on correlated inference with application in GNSS/IMU integrated navigation. GPS Solutions, 22, 100, https://doi.org/10.1007/s10291-018-0766-2.Google Scholar
Yozevitch, R., Moshe, B. B. and Weissman, A. (2016). A robust GNSS los/nlos signal classifier. Navigation, 63(4), 429–442.Google Scholar
Ziedan, N. I. (2012). Multipath Channel Estimation and Pattern Recognition for Environment-Based Adaptive Tracking Conference: ION GNSS 2012, 25th International Technical Meeting of the Satellite Division of the Institute of Navigation, Nashville, Tennessee.Google Scholar
Ziedan, N. I. (2017). Urban Positioning Accuracy Enhancement Utilizing 3D Buildings Model and Accelerated Ray Tracing Algorithm. Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS + 2017), Portland, Oregon, September, 3253–3268.Google Scholar