Array processing is widely used in sensing applications for estimating the locations and waveforms of the sources in a given field. In the absence of a large number of snapshots, which is the case in numerous practical applications, such as underwater array processing, it becomes challenging to estimate the source parameters accurately. This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing. IAA-APES can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA-APES is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX) to refine the IAA-APES&BIC estimates if desired. IAA-APES can also be applied to active sensing applications, including single-input single-output (SISO) radar/sonar range-Doppler imaging and multi-input single-output (MISO) channel estimation for communications. Simulation results are presented to evaluate the performance of IAA-APES for all of these applications, and IAA-APES is shown to outperform a number of existing approaches.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares


    Beteiligte:
    Yardibi, T. (Autor:in) / Jian Li, (Autor:in) / Stoica, P. (Autor:in) / Ming Xue, (Autor:in) / Baggeroer, A.B. (Autor:in)


    Erscheinungsdatum :

    01.01.2010


    Format / Umfang :

    3167017 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Source localization and sensing: a nonparametric iterative adaptive approach based on weighted least squares

    Baggeroer, Arthur B. / Xue, Ming / Stoica, Petre et al. | DSpace@MIT | 2010

    Freier Zugriff


    Fast Weighted Least Squares Based Pansharpening

    Kaplan, Nur Huseyin / Erer, Isin | IEEE | 2019


    A Weighted Least Squares Algorithm for Passive Localization in Multipath Scenarios

    El Gemayel, Noha / Jakel, Holger / Jondral, Friedrich K. | IEEE | 2015


    A New Weighted Least-Squares Method for Hybrid TDOA-AOA Localization

    Almalki, Abdulellah / Liu, Huaping / Zou, Yanbin | IEEE | 2024