Volume 5, Issue 2, March 2017, Page: 8-18
Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System
Mamudu Hamidu, Electrical/Electronic Engineering Department, Faculty of Engineering & Technology, Kumasi Technical University, Kumasi, Ghana
Received: Sep. 4, 2016;       Accepted: Mar. 30, 2017;       Published: Jun. 12, 2017
DOI: 10.11648/j.com.20170502.11      View  2009      Downloads  84
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.
Keywords
Modelling, Simulation Kalman Filter, Gaussian White Noise, Algorithm, Iterative, Positioning
To cite this article
Mamudu Hamidu, Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System, Communications. Vol. 5, No. 2, 2017, pp. 8-18. doi: 10.11648/j.com.20170502.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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