An alternative approach to high-resolution direction-of-arrival estimation in the context of automotive FMCW signal processing is shown by training a neural network with simulation as well as experimental data to estimate the mean and distance of the azimuth angles from two targets. Testing results are post-processed to obtain the estimated azimuth angles which can be validated afterwards. The performance of the proposed neural network is then compared with a reference implementation of a maximum likelihood estimator. Final evaluations show super-resolution like performance with significantly reduced computation time, which is expected to have an impact on future multi-dimensional high-resolution DoA estimation.


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    Titel :

    Single-Snapshot Direction-of-Arrival Estimation of Multiple Targets using a Multi-Layer Perceptron


    Beteiligte:
    Fuchs, Jonas (Autor:in) / Weigel, Robert (Autor:in) / Gardill, Markus (Autor:in)


    Erscheinungsdatum :

    2019-04-01


    Format / Umfang :

    803369 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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