| Title | An Improved Robust Low Cost Approach for Real Time Vehicle Positioning in a Smart City |
|---|---|
| ID_Doc | 41999 |
| Authors | Belhajem, I; Ben Maissa, Y; Tamtaoui, A |
| Title | An Improved Robust Low Cost Approach for Real Time Vehicle Positioning in a Smart City |
| Year | 2017 |
| Published | |
| Abstract | The Global Positioning System (GPS) aided low cost Dead Reckoning (DR) system can provide without interruption the vehicle position for efficient fleet management solutions in smart cities. The Extended Kalman Filter (EKF) is generally applied for data fusion using the sensor's measures and the GPS position as a helper. However, the EKF depends on the vehicle dynamic variations and may quickly diverge during periods of GPS signal loss. In this paper, we present a robust low cost approach using EKF and neural networks (NN) with Particle Swarm Optimization (PSO) to reliably estimate the real time vehicle position. While GPS signals are available, we train the NN with PSO on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 94% over the simple EKF predictions in case of GPS failures. |
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