Modeling and Simulation of GPS Positioning and Iterative Vehicle Motion Using Kalman Filter in Vehicle Tracking System
Issue:
Volume 5, Issue 2, March 2017
Pages:
8-18
Received:
4 September 2016
Accepted:
30 March 2017
Published:
12 June 2017
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.
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. ...
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