Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required. Conventional methods for GNSS positioning involves a two-step process, where intermediate measurements, such as Doppler shift and time delay of received GNSS signals, are computed and then used to solve for the receiver's position. Alternatively, direct position estimation (DPE) was proposed to infer the position directly from the sampled signal without intermediate variables, yielding to superior levels of sensitivity and operation under challenging environments. However, the positioning resilience of DPE method is still under the threat of various interferences. robust interference mitigation (RIM) processing has been studied and proved to be efficient against various interference in conventional two-step positioning (2SP) methods, and therefore worthy to be explored regarding its potential to enhance DPE. This article extends DPE methodology by incorporating RIM strategies that address the increasing need to protect GNSS receivers against intentional or unintentional interferences, such as jamming signals, which can deny GNSS-based positioning. RIM, which leverages robust statistics, was shown to provide competitive results in two-step approaches and is here employed in a high-sensitivity DPE framework with successful results. This article also provides a quantification of the loss of efficiency of using RIM when no interference is present and validates the proposed methodology on relevant interference cases, while the approach can be used to mitigate other common interference signals.


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    Titel :

    Robust Interference Mitigation Techniques for Direct Position Estimation


    Beteiligte:
    Li, Haoqing (Autor:in) / Tang, Shuo (Autor:in) / Wu, Peng (Autor:in) / Closas, Pau (Autor:in)


    Erscheinungsdatum :

    01.12.2023


    Format / Umfang :

    1003578 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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