Estimation methods for passive near-field localization have been studied to an appreciable extent in signal processing research. Such localization methods find use in various applications, for instance in medical imaging. However, methods based on the standard near-field signal model can be inaccurate in real-world applications, due to deficiencies of the model itself and hardware imperfections. It is expected that deep neural network (DNN) based estimation methods trained on the nonideal sensor array signals could outperform the model-driven alternatives. In this work, a DNN based estimator is trained and validated on a set of real world measured data. The series of measurements was conducted with an inexpensive custom built multichannel software-defined radio (SDR) receiver, which makes the nonidealities more prominent. The results show that a DNN based localization estimator clearly outperforms the compared model-driven method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Near-field localization using machine learning: an empirical study


    Contributors:


    Publication date :

    2021-04-01


    Size :

    1990749 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Method and system for near-field localization

    KORNEK DANIEL / THOMPSON ANDREW | European Patent Office | 2020

    Free access

    Field Experiment of Localization Based on Machine Learning in LTE Network

    Kanazawa, Noboru / Nagate, Atsushi / Yamamoto, Atsushi | IEEE | 2018


    Machine Learning Empowered Large RIS-assisted Near-field Communications

    Zhong, Ruikang / Mu, Xidong / Liu, Yuanwei | IEEE | 2023



    3-D Near-Field Source Localization Using a Spatially Spread Acoustic Vector Sensor

    Shu, Ting / He, Jin / Dakulagi, Veerendra | IEEE | 2022