Global navigation satellite system (GNSS) plays a crucial role in providing the globally referenced positioning for self-driving systems. Unfortunately, the numerous multipath or non-line-of-sight (NLOS) receptions (known as outlier observations) caused by the signal reflections from buildings reduce the positioning accuracy of GNSS in dense urban environments. The recently investigated factor graph- based GNSS positioning formulation simultaneously considers the historical information, which significantly increases the measurement redundancy of state estimation. Taking this advantage, this paper proposes an outlier mitigation method where the bias involved in the outliers is estimated simultaneously with the position of the receiver. Specifically, the outliers are firstly detected using a pre-trained deep learning network. Secondly, an unknown variable associated with the bias is assigned to each identified outlier measurement. Then the position of the GNSS receiver, together with the bias of outlier measurements, is estimated simultaneously via the factor graph optimization (FGO) based on the pseudorange measurements and Doppler frequency shift. Finally, the effectiveness of the proposed method is validated using a dataset collected in the urban canyon by a low-cost automobile- level GNSS receiver.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    GNSS Outliers Mitigation in Urban Areas Using Sparse Estimation Based on Factor Graph Optimization


    Contributors:
    Bai, Xiwei (author) / Wen, Weisong (author) / Zhang, Guohao (author) / Ng, Hoi-Fung (author) / Hsu, Li-Ta (author)


    Publication date :

    2022-10-08


    Size :

    929228 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multipath Mitigation for GNSS Positioning in an Urban Environment Using Sparse Estimation

    Lesouple, Julien / Robert, Thierry / Sahmoudi, Mohamed et al. | IEEE | 2019


    Robust State Estimation with Sparse Outliers

    Graham, Matthew C. / How, Jonathan P / Gustafson, Donald E. | DSpace@MIT | 2015

    Free access

    Robust State Estimation with Sparse Outliers

    Graham, Matthew C. / How, Jonathan P. / Gustafson, Donald E. | AIAA | 2015


    Robust State Estimation with Sparse Outliers

    Matthew C Graham | Online Contents | 2015