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Year 2020, , 43 - 48, 22.03.2020

Ziya Gökalp Ersan , Metin Zontul İlkay Yelmen

https://doi.org/10.17776/csj.634940

Abstract

References

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  • [6] G. S., Raoa, Siripurapu D., Bagadib L.; Elevation and Position Uncertainty based KF Model for Position Accuracy Improvement, ScienceDirect, 2018
  • [7] Çayıroğlu İ., Kalman Filter and Programming, Science and Technology Information Sharing, (2012) 1.
  • [8] Dai P., Li Z., Research on Map-matching Algorithm Using Kaman Filter to Improve Localization Accuracy from Baidu Map Based on Android, 2016
  • [9] Laveti, G. S., Rao, G. S., and Bidikar, B., Modified Kalman Filter for GPS Position Estimation over the Indian Sub Continent, Procedia Computer Science, 87 (2016) 198-203.
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  • [17] Orderud, F., Comparison of Kalman Filter Estimation Approaches for State Space Models with Nonlinear Measurements, (2005) ss. 7-9
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  • [23] https://www.u-blox.com Retrieved October 2, 2019.
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  • [27] https://bookdown.org/content/2096/korelasyon-ve-regresyon.html, Correlation, Retrieved October 2, 2019.

Map matching with kalman filter and location estimation

Year 2020, , 43 - 48, 22.03.2020

Ziya Gökalp Ersan , Metin Zontul İlkay Yelmen

https://doi.org/10.17776/csj.634940

Abstract

Known as Global Navigation Satellite Systems, GNSS is a geolocation service. GNSS systems used in the world are known as GPS in America, GLONASS in Russia, GALILEO in Europe, BEIDOU in China and IRNSS in India. However, GPS is the only one that works decisively today. GNSS systems are used effectively in the navigation of all types of land, sea and air vehicles such as search and rescue, target finding, and landing and take-off of airplanes with or without limited visibility. However, when environmental and weather conditions are unfavorable, the accuracy of the GPS systems in the GNSS may vary. This study is presented as a solution to the map matching problem by minimizing the error deviation rates of GPS data from NOVATEL and UBLOX based vehicle tracking devices with the help of Kalman Filter Algorithm. In addition, the deviation rate between the GPS data from the vehicle tracking system and the estimated point coordinates is provided in meters.

Keywords

Map Matching Kalman Filter Location Estimation GPS

References

  • [1] Munoz, D., Lara, F. B., Vargas C., and Enriquez-Caldera R., Position Location Techniques and Applications, Academic Press, (2019).
  • [2] Nikolić, M, and Jadranka, J., Implementation of generic algorithm in map-matching model, Expert Systems with Applications 72 (2017) 283-292.
  • [3] Sylvie, L. P., Nicolas, G., Mehdi, B., A HMM map-matching approach enhancing indoor positioning performances of an inertial measurement system, In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, (2015) 1–4.
  • [4] G. S., Rao, Global Navigation Satellite Systems, McGraw Hill Education Private limited, ISBN (13):978-0-07-070029-1, 2010.
  • [5] Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J., Global positioning system: theory and practice, Springer Science & Business Media (2012)
  • [6] G. S., Raoa, Siripurapu D., Bagadib L.; Elevation and Position Uncertainty based KF Model for Position Accuracy Improvement, ScienceDirect, 2018
  • [7] Çayıroğlu İ., Kalman Filter and Programming, Science and Technology Information Sharing, (2012) 1.
  • [8] Dai P., Li Z., Research on Map-matching Algorithm Using Kaman Filter to Improve Localization Accuracy from Baidu Map Based on Android, 2016
  • [9] Laveti, G. S., Rao, G. S., and Bidikar, B., Modified Kalman Filter for GPS Position Estimation over the Indian Sub Continent, Procedia Computer Science, 87 (2016) 198-203.
  • [10] Li L., Quddus M., Zhao L., High accuracy tightly-coupled integrity monitoring algorithm for map-matching, (2013) 13-26.
  • [11] Quddus, M., and Washington, S., Shortest path and vehicle trajectory aided map-matching for low frequency GPS data, Transportation Research Part C: Emerging Technologies, 55 (2015) 328-339.
  • [12] Greenfeld, J. S., Matching GPS observations to locations on a digital map, In 81st annual meeting of the transportation research board 1 (3) (2002) 164-173.
  • [13] Velaga, N. R., Quddus, M. A., and Bristow, A. L., Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems, Transportation Research Part C: Emerging Technologies, 17(6) (2009) 672-683.
  • [14] Kim, W., Jee, G., Lee, J., Efficient use of digital road map in various positioning for ITS. In: IEEE Symposium on Position Location and Navigation, SanDeigo, CA. (2000)
  • [15] Ganesh L., Vijaya Kumar B., Indoor Wireless Localization using Haversine Formula, International Advanced Research Journal in Science, Engineering and Technology, 2(7) (2015) 2393-8021.
  • [16] https://www.movabletype.co.uk/scripts/latlong.html, Haversine formula, Retrieved October 2, 2019.
  • [17] Orderud, F., Comparison of Kalman Filter Estimation Approaches for State Space Models with Nonlinear Measurements, (2005) ss. 7-9
  • [18] He, J., and Yao, X., From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 6(5) (2002) 495-511.
  • [19] Brown, R. G., and Hwang, P. Y, Introduction to random signals and applied Kalman filtering 3 (2012)
  • [20] Wang, H., Wang, W., Sun, H., and Rahnamayan, S., Firefly algorithm with random attraction, International Journal of Bio-Inspired Computation, 8(1) (2016) 33-41.
  • [21] Zhang, L., Liu, L., Yang, X. S., and Dai, Y., A novel hybrid firefly algorithm for global optimization, PloS one, 11(9) (2016).
  • [22] Mosavi, M. R., Azad, M. S., and EmamGholipour, I., Position estimation in single-frequency GPS receivers using Kalman filter with pseudo-range and carrier phase measurements, Wireless personal communications, 72(4) (2013) 2563-2576.
  • [23] https://www.u-blox.com Retrieved October 2, 2019.
  • [24] Schafer, J. B., Konstan, J., and Riedl, J., Recommender systems in e-commerce, In Proceedings of the 1st ACM conference on Electronic commerce, (1999) 158-166.
  • [25] Kırbaş İ., Short-term multi-step wind speed estimation using statistical methods and artificial neural networks, Sakarya University Journal of Institute of Science and Technology, 22 (1), 24-38 , 2018
  • [26] Zhang, F., Gong, T., Lee, V. E., Zhao, G., Rong, C., and Qu, G., Fast algorithms to evaluate collaborative filtering recommender systems, Knowledge-Based Systems, 96 (2016) 96-103.
  • [27] https://bookdown.org/content/2096/korelasyon-ve-regresyon.html, Correlation, Retrieved October 2, 2019.
There are 27 citations in total.

Details

Primary Language English
Journal Section Natural Sciences
Authors

Ziya Gökalp Ersan 0000-0002-2575-0735

Metin Zontul 0000-0002-7557-2981

İlkay Yelmen 0000-0002-1684-9717

Publication Date March 22, 2020
Submission Date October 19, 2019
Acceptance Date February 11, 2020
Published in Issue Year 2020

Cite

APA Ersan, Z. G., Zontul, M., & Yelmen, İ. (2020). Map matching with kalman filter and location estimation. Cumhuriyet Science Journal, 41(1), 43-48. https://doi.org/10.17776/csj.634940