Robust Multi-Sensor Data Fusion for Mobility Tracking

Mobility tracking technology plays a crucial role in state-of-the-art fields such as autonomous driving and smart transportation. In the inevitable trend of the Internet of Vehicles (IoV), utilizing measurement data from multiple sensors for mobility tracking presents a promising solution. However, ensuring that each data source maintains consistent measurement performance during data fusion can be challenging. Instability in a single data source can severely diminish tracking accuracy. To address this issue, this paper proposes a robust multi-sensor data fusion algorithm based on Kalman filtering. The proposed algorithm employs an elliptical gating mechanism to refine sensor measurement data and eliminate outliers. This approach mitigates the impact of erroneous measurements on tracking results, thereby enhancing the algorithm’s adaptability and robustness. Monte Carlo simulations are conducted to calculate the cumulative distribution function (CDF) of the estimation error. Simulation results demonstrate that the proposed algorithm can significantly improve estimation accuracy, achieving at least an order of magnitude improvement over existing methods.