In this paper we present two novel approaches to unambiguously estimate the direction of arrival (DOA) of an RF source by an array of receive antennas whose positions can take-on any arbitrary geometry. The first approach employs a simple constrained integer optimization, while the second approach employs deep learning. In both approaches the impact of imperfect array calibration on the performance of DOA estimation is quantified. We demonstrate in Monte Carlo simulations that both approaches are capable of achieving super-resolution performance under imperfect array calibration conditions. It was found that the constrained integer optimization outperforms deep learning when one has an accurate physics model of the receiver imperfections, however deep learning was more robust to significant calibration errors.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Ambiguity Resolution in Direction of Arrival Estimation using Mixed Integer Optimization and Deep Learning


    Contributors:


    Publication date :

    2019-07-01


    Size :

    1205803 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    APPLIED INTERVAL BASED INTEGER AMBIGUITY RESOLUTION

    van Kampen, E. / de Weerdt, E. / Chu, Q.P. et al. | British Library Online Contents | 2009



    APPLIED INTERVAL BASED INTEGER AMBIGUITY RESOLUTION

    van Kampen, E. | Online Contents | 2009


    ON THE RELIABILITY OF INTEGER AMBIGUITY RESOLUTION

    Verhagen, S. | Online Contents | 2005