• DocumentCode
    1790553
  • Title

    Contiguously clustered linear arrays through Bayesian compressive sensing

  • Author

    Bekele, Ephrem T. ; Oliveri, G. ; Massa, A.

  • Author_Institution
    ELEDIA Res. Center @ DISI, Univ. of Trento, Trento, Italy
  • fYear
    2014
  • fDate
    16-19 Nov. 2014
  • Firstpage
    1
  • Lastpage
    3
  • Abstract
    Bayesian Compressive Sensing (BCS) is applied for the synthesis of contiguously clustered linear arrays. The standard sub-array problem is formulated as a probabilistic BCS synthesis problem and the Relevance Vector Machine (RVM) is used to obtain a sparse contiguous non-overlapping subarray configuration which has maximal far-field pattern match with a given reference pattern. Selected numerical experiment results are reported to validate the synthesis technique.
  • Keywords
    Bayes methods; antenna radiation patterns; compressed sensing; linear antenna arrays; pattern matching; support vector machines; telecommunication computing; Bayesian compressive sensing; RVM; contiguously clustered linear antenna array synthesis; maximal far-field pattern matching; probabilistic BCS synthesis problem; relevance vector machine; sparse contiguous nonoverlapping subarray configuration; Bayes methods; Compressed sensing; Pattern matching; Phased arrays; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Antenna Measurements & Applications (CAMA), 2014 IEEE Conference on
  • Conference_Location
    Antibes Juan-les-Pins
  • Type

    conf

  • DOI
    10.1109/CAMA.2014.7003311
  • Filename
    7003311
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1790553