• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      A complexity-reduced ML parametric signal reconstruction method

      Thumbnail
      View / Download
      715.4 Kb
      Author
      Deprem, Z.
      Leblebicioglu, K.
      Arkan O.
      Çetin, A.E.
      Date
      2011
      Source Title
      Eurasip Journal on Advances in Signal Processing
      Print ISSN
      16876172
      Volume
      2011
      Language
      English
      Type
      Article
      Item Usage Stats
      121
      views
      79
      downloads
      Abstract
      The problem of component estimation from a multicomponent signal in additive white Gaussian noise is considered. A parametric ML approach, where all components are represented as a multiplication of a polynomial amplitude and polynomial phase term, is used. The formulated optimization problem is solved via nonlinear iterative techniques and the amplitude and phase parameters for all components are reconstructed. The initial amplitude and the phase parameters are obtained via time-frequency techniques. An alternative method, which iterates amplitude and phase parameters separately, is proposed. The proposed method reduces the computational complexity and convergence time significantly. Furthermore, by using the proposed method together with Expectation Maximization (EM) approach, better reconstruction error level is obtained at low SNR. Though the proposed method reduces the computations significantly, it does not guarantee global optimum. As is known, these types of non-linear optimization algorithms converge to local minimum and do not guarantee global optimum. The global optimum is initialization dependent. © 2011 Z. Deprem et al.
      Keywords
      Additive White Gaussian noise
      Alternative methods
      Component estimation
      Convergence time
      Expectation-maximization approaches
      Global optimum
      Iterative technique
      Local minimums
      Low SNR
      Multicomponent signals
      Non-linear optimization algorithms
      Optimization problems
      Phase parameters
      Polynomial phase
      Reconstruction error
      Time-frequency techniques
      Gaussian noise (electronic)
      Iterative methods
      Optimization
      White noise
      Computational complexity
      Permalink
      http://hdl.handle.net/11693/21882
      Published Version (Please cite this version)
      http://dx.doi.org/10.1155/2011/875132
      Collections
      • Department of Electrical and Electronics Engineering 3636
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy