We consider two-dimensional (2-D) nonparametric complex spectral estimation (with its 1-D counterpart as a special case) of data matrices with missing samples occurring in arbitrary patterns. Previously, the missing amplitude and phase estimation-expectation maximization (MAPES-EM) algorithms were developed for the general 1-D missing-data problem and shown to have excellent spectral estimation performance. In this correspondence, we present 2-D extensions of MAPES-EM and develop another 2-D MAPES algorithm, referred to as MAPES-CM, which solves a maximum likelihood problem iteratively via cyclic maximization (CM). Compared with MAPES-EM, MAPES-CM has similar spectral estimation performance but is computationally much more efficient, which is especially important for long data sequences and 2-D applications such as synthetic aperture radar (SAR) imaging.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Two-dimensional nonparametric spectral analysis in missing data case


    Contributors:
    Yanwei Wang (author) / Stoica, P. (author) / Jian Li (author)


    Publication date :

    2007-10-01


    Size :

    2561241 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Nonparametric Reliability Analysis of Spacecraft Failure Data

    Saleh, Joseph Homer / Castet, Jean‐François | Wiley | 2011


    Some examples and problems of application of nonparametric correlation and spectral analysis

    Schmidt,H. / Daimler-Benz,Stuttgart,DE | Automotive engineering | 1985


    Recovery of missing data via wavelets followed by high-dimensional modeling

    Gürvіt, Ercan / Baykara, N. A. | American Institute of Physics | 2017


    Unmanned aerial vehicles trajectory analysis considering missing data

    Bo Wang / Volodymyr Kharchenko / Alexander Kukush et al. | DOAJ | 2019

    Free access