Accurate detection of vehicle position plays an important role in many intelligent transportation systems, especially vehicle-to-vehicle applications. In this paper, we propose an Extended Kalman Filter (EKF) based method to detect Global Positioning System (GPS) errors for such vehicle-based applications. A machine learning methodology is presented for Kalman filter parameter tuning with application to GPS error correction in vehicle positioning. We also present a model free neural network that is trained on past vehicle GPS trajectories to predict the current vehicle position. Experimental results on real-world data show that the proposed system is effective for detecting and reducing GPS errors. The machine learning algorithm for EKF parameter tuning can be implemented through in-vehicle learning, and the proposed GPS error detection method can be implemented for in-vehicle applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Accurate vehicle position estimation using a Kalman filter and neural network-based approach


    Contributors:
    Baek, Stanley (author) / Liu, Chang (author) / Watta, Paul (author) / Murphey, Yi Lu (author)


    Publication date :

    2017-11-01


    Size :

    1057261 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    MISSILE POSITION ESTIMATION USING UNSCENTED KALMAN FILTER

    Teguh Herlambang / Subchan Subchan | DOAJ | 2022

    Free access

    Vehicle parameter estimation method based on BP neural network and improved unscented Kalman filter

    SHI FENG / LUAN ZHONGKAI / LIU YANFU et al. | European Patent Office | 2024

    Free access


    GPS Based Attitude Estimation of Aircraft Using Neural Network Aided Kalman Filter

    Katoch, R. / Mahapatra, P.R. | British Library Conference Proceedings | 2006