Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System

Mamudu Hamidu

doi:doi:10.11648/j.com.20170502.11

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Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System

Mamudu Hamidu

Published in Communications (Volume 5, Issue 2)

Received: 4 September 2016 Accepted: 30 March 2017 Published: 12 June 2017

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Abstract

This research is about modelling and simulation of the RFID based Transport Highway Integrated System (THIS) using the iterative motion of the vehicle plying the highway and the application of Kalman filter to aid in effective positioning after signal transmit. This approach will help to identify drivers, vehicles and track location appropriately. The design when able to be implemented with the use of Kalman filter to filter out the noise there will be much accuracy in the vehicle position prediction on the high-way. According to the graphs obtained through Kalman algorithm it was realized that: The noise level was appreciative as compared with the actual signal from the vehicle. If the vehicle model is created based on true situation our estimated state will be close to the true value. Even when measurements are very noisy that is a 20% error will only produce a 5% inaccuracy. The position prediction of a vehicle on the high-way is better as the Gaussian white noise is eliminated, tracking to know the exact location via GPS coordinate will reduce the error margin. If you have a badly deﬁned model, you will not get a good estimate. But you can relax your model by increasing your estimated error. This will let the Kalman ﬁlter rely more on the measurement values, but still allow some noise removal.

Published in	Communications (Volume 5, Issue 2)
DOI	10.11648/j.com.20170502.11
Page(s)	8-18
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Modelling, Simulation Kalman Filter, Gaussian White Noise, Algorithm, Iterative, Positioning

References

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[2]	Obolensky, N. (2002). Kalman filtering methods for moving vehicle tracking (Doctoral dissertation, University of Florida).
[3]	Sharma, R. (2014). ‘Vehicle Tracking in Extreme Noisy Channel Through Kalman Filter’. International Journal of Innovative Science and Modern Engineering (IJISME). [Online] 2(12). Available from < http://www.ijisme.org/attachments/File/v2i12/L07421121214.pdf> [10 January 2014].
[4]	Bajaj, D., & Gupta, N. (2012). GPS based automatic vehicle tracking using RFID. International Journal of Engineering and Innovative Technology (IJEIT), 1(1), 31-35.
[5]	Kodavati, B., Raju, V. K., Rao, S. S., Prabu, A. V., Rao, T. A., & Narayana, D. Y. (2011). GSM and GPS based vehicle location and tracking system. International Journal of Engineering Research and Applications (IJERA) ISSN, 2248-9622.
[6]	Jog, S., Sutaone, M. S., & Badawe, V. V. (2011). Performance Improvement of GPS based Vehicle Tracking System using DGPS and Mobile Wi-Max. International Journal on Computer Science Engineering and Technology (IJCSET), 1(8), 491-495.
[7]	Lee, S., Tewolde, G., & Kwon, J. (2014, March). Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application. In Internet of Things (WF-IoT), 2014 IEEE World Forum on (pp. 353-358). IEEE.
[8]	Sheet, D., Kumar, A., Dutta, A., Dasgupta, S., Datta, T., & Sarkar, S. K. (2007). Realization and simulation of the hardware for RFID system and its performance study. [9] Min, Z. et al. (2007). ‘A RFID-based Material Tracking Information System’. Proceedings of the IEEE International Conference on Automation and Logistics, Jinan, China, pp. 2922 – 2926.
[9]	Kamel, M. (2015). Real-Time GPS/GPRS Based Vehicle Tracking System. International Journal of Engineering And Computer Science, 4(8), 648-652.
[10]	Wood, L., Clerget, A., Andrikopoulos, I., Pavlou, G., & Dabbous, W. (2001). IP routing issues in satellite constellation networks. International Journal of Satellite Communications, 19(1), 69-92.
[11]	D'Roza, T., & Bilchev, G. (2003). An overview of location-based services. BT Technology Journal, 21(1), 20-27.
[12]	Patel, S. P., & Deshmukh, S. S. (2013). Geo-Location Big Data Based Data Mining Architecture Using MongoDB For Collaborative E-Initiative Based Crowd-sourced Traffic Management System. International Journal of Advanced Research in Computer Science, 4(4).
[13]	Behrendt, K., & Fodero, K. (2006, October). The perfect time: An examination of time-synchronization techniques. In Proc. 33rd Ann. West. Prot. Rel. Conf., Spokane, WA, USA (pp. 17-19).
[14]	Kamarudin, N., & Amin, Z. M. (2004). Multipath error detection using different GPS receiver’s antenna. In 3rd FIG (International Federation of Surveyors) Regional Conference, Jakarta (pp. 1-11).
[15]	Smyth, A. W., Masri, S. F., Kosmatopoulos, E. B., Chassiakos, A. G., & Caughey, T. K. (2002). Development of adaptive modeling techniques for non-linear hysteretic systems. International journal of non-linear mechanics, 37(8), 1435-1451.
[16]	Mamudu H. (2016). Use of RFID Technology as a Reporting Mechanism in Vehicle Tracking System. Advances in Wireless Communications and Networks. Vol. 2, No. 1, 2016, pp. 1-10. doi: 10.11648/j.awcn.20160201.11.

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Mamudu Hamidu. (2017). Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System. Communications, 5(2), 8-18. https://doi.org/10.11648/j.com.20170502.11

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ACS Style

Mamudu Hamidu. Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System. Communications. 2017, 5(2), 8-18. doi: 10.11648/j.com.20170502.11

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AMA Style

Mamudu Hamidu. Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System. Communications. 2017;5(2):8-18. doi: 10.11648/j.com.20170502.11

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@article{10.11648/j.com.20170502.11,
  author = {Mamudu Hamidu},
  title = {Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System},
  journal = {Communications},
  volume = {5},
  number = {2},
  pages = {8-18},
  doi = {10.11648/j.com.20170502.11},
  url = {https://doi.org/10.11648/j.com.20170502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20170502.11},
  abstract = {This research is about modelling and simulation of the RFID based Transport Highway Integrated System (THIS) using the iterative motion of the vehicle plying the highway and the application of Kalman filter to aid in effective positioning after signal transmit. This approach will help to identify drivers, vehicles and track location appropriately. The design when able to be implemented with the use of Kalman filter to filter out the noise there will be much accuracy in the vehicle position prediction on the high-way. According to the graphs obtained through Kalman algorithm it was realized that: The noise level was appreciative as compared with the actual signal from the vehicle. If the vehicle model is created based on true situation our estimated state will be close to the true value. Even when measurements are very noisy that is a 20% error will only produce a 5% inaccuracy. The position prediction of a vehicle on the high-way is better as the Gaussian white noise is eliminated, tracking to know the exact location via GPS coordinate will reduce the error margin. If you have a badly deﬁned model, you will not get a good estimate. But you can relax your model by increasing your estimated error. This will let the Kalman ﬁlter rely more on the measurement values, but still allow some noise removal.},
 year = {2017}
}

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TY - JOUR
T1 - Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System
AU - Mamudu Hamidu
Y1 - 2017/06/12
PY - 2017
N1 - https://doi.org/10.11648/j.com.20170502.11
DO - 10.11648/j.com.20170502.11
T2 - Communications
JF - Communications
JO - Communications
SP - 8
EP - 18
PB - Science Publishing Group
SN - 2328-5923
UR - https://doi.org/10.11648/j.com.20170502.11
AB - This research is about modelling and simulation of the RFID based Transport Highway Integrated System (THIS) using the iterative motion of the vehicle plying the highway and the application of Kalman filter to aid in effective positioning after signal transmit. This approach will help to identify drivers, vehicles and track location appropriately. The design when able to be implemented with the use of Kalman filter to filter out the noise there will be much accuracy in the vehicle position prediction on the high-way. According to the graphs obtained through Kalman algorithm it was realized that: The noise level was appreciative as compared with the actual signal from the vehicle. If the vehicle model is created based on true situation our estimated state will be close to the true value. Even when measurements are very noisy that is a 20% error will only produce a 5% inaccuracy. The position prediction of a vehicle on the high-way is better as the Gaussian white noise is eliminated, tracking to know the exact location via GPS coordinate will reduce the error margin. If you have a badly deﬁned model, you will not get a good estimate. But you can relax your model by increasing your estimated error. This will let the Kalman ﬁlter rely more on the measurement values, but still allow some noise removal.
VL - 5
IS - 2
ER -

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Mamudu Hamidu

Electrical/Electronic Engineering Department, Faculty of Engineering & Technology, Kumasi Technical University, Kumasi, Ghana

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APA Style

Mamudu Hamidu. (2017). Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System. Communications, 5(2), 8-18. https://doi.org/10.11648/j.com.20170502.11

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ACS Style

Mamudu Hamidu. Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System. Communications. 2017, 5(2), 8-18. doi: 10.11648/j.com.20170502.11

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AMA Style

Mamudu Hamidu. Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System. Communications. 2017;5(2):8-18. doi: 10.11648/j.com.20170502.11

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@article{10.11648/j.com.20170502.11,
  author = {Mamudu Hamidu},
  title = {Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System},
  journal = {Communications},
  volume = {5},
  number = {2},
  pages = {8-18},
  doi = {10.11648/j.com.20170502.11},
  url = {https://doi.org/10.11648/j.com.20170502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20170502.11},
  abstract = {This research is about modelling and simulation of the RFID based Transport Highway Integrated System (THIS) using the iterative motion of the vehicle plying the highway and the application of Kalman filter to aid in effective positioning after signal transmit. This approach will help to identify drivers, vehicles and track location appropriately. The design when able to be implemented with the use of Kalman filter to filter out the noise there will be much accuracy in the vehicle position prediction on the high-way. According to the graphs obtained through Kalman algorithm it was realized that: The noise level was appreciative as compared with the actual signal from the vehicle. If the vehicle model is created based on true situation our estimated state will be close to the true value. Even when measurements are very noisy that is a 20% error will only produce a 5% inaccuracy. The position prediction of a vehicle on the high-way is better as the Gaussian white noise is eliminated, tracking to know the exact location via GPS coordinate will reduce the error margin. If you have a badly deﬁned model, you will not get a good estimate. But you can relax your model by increasing your estimated error. This will let the Kalman ﬁlter rely more on the measurement values, but still allow some noise removal.},
 year = {2017}
}

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